Wireless Sensor Network Security

In an era marked by hyperconnectivity and digital dependence, malicious websites have emerged as covert yet formidable threats to user privacy and data security. This paper critically examines the evolving architecture of spyware-laden websites, offering a structured typology of fifty distinct categories based on their operational logic, attack vectors, and user deception techniques. By integrating cybersecurity theory with real-time simulation, the study reveals how browser-level JavaScript functions are manipulated to implement surveillance tactics such as keylogging, clipboard hijacking, fingerprinting, and credential harvesting often without user awareness. Moving beyond surface-level classification, the paper explores the psychological dimensions of user behavior, including motivation, cognitive bias, and security fatigue, which often facilitate successful attacks. A live JavaScript demonstration is presented to illustrate the subtle but dangerous mechanisms employed by threat actors, grounding abstract concepts in practical reality. The paper further examines the role of emerging technologies such as artificial intelligence and machine learning in detecting and mitigating these threats. It also advocates for a proactive cybersecurity paradigm that emphasizes user empowerment, community engagement, and policy responsiveness. Through an interdisciplinary lens, the study argues that combating web-based spyware requires not only technical innovation but also behavioral insight and systemic reform. By exposing the invisible battlefield embedded within everyday browsing, this paper seeks to elevate digital literacy, inform security protocols, and inspire a new generation of defense strategies in the face of an increasingly deceptive online landscape.

Packet sniffers are designed to intercept network traffic in shared communication channels. This is accomplished by re-configuring network interface cards to permit device drivers to process all network traffic, including packets that are not addressed to the host computer. Packet sniffing is primarily used in intrusion detection, network management, wiretapping and hacking. This paper describes a novel application of packet sniffing to monitor chat room conversations for criminal activity. Current manual monitoring techniques must scrutinize massive amounts of conversations for potential criminal activity. The packet sniffer described in this paper permits the automated monitoring and filtering of chat room conversations. Moreover, it records and preserves packet-based evidence, enabling the complete reconstruction of illicit chat room activity for purposes of prosecution.

In various cloud computing models, the data need to be protected and to access these data in secure manner is important. The cryptographic key which is used to secure these data using both in the encryption as well as in decryption it is mandatory to manage these keys to secure these keys by disclosing in public networks such as any wireless and cloud environment. Utilizing Ciphertext Policy Attribute-based Encryption (CP-ABE), which provides effective data governance and key management, for cloud data encryption. The work based on the combination of Cipher Text-Policy Attribute based Encryption and Proxy Re-Encryption is elaborated in the article (CP-ABE-PRE). The encrypted data should ideally be transformed such that it may be unlocked with new keys, without an intermediate decryption step that would allow the cloud provider to read the plaintext this process is known as data re-encryption. The computational and communication burden on users connecting to the cloud from resource constrained devices can be reduced using the proposed technique. The experimental results show for Cipher Text-Policy Attribute-Based Encryption are compared to the current algorithm (CP-ABE) demonstrate good results in encryption and decryption times. Additionally, the CP-ABE offers crucial distribution and administration options for cloud data. CP-ABE with Proxy Re-Encryption does appear to be highly efficient which proves verifiability and fairness for cloud data users to which also address revocation problem as well as collusion resistant model.

only propenies of the received signal, without using the decisions of the receiver to detennine the timing" These Bit synchronization techniques for the coherent de- circuits are generally simpler than data-aided bit synchro- modulation of QPSK in the presence of co-channel inter- nizers, but are known to perform worse than data-aided ference are investigated using time-domain digital simu- circuits in additive white Gaussian noise (A WGN) chan- lation. The effects of Nyquist filter excess bandwidth, the nels (3). The data-aided approach to bit synchronization number of interferers, and the symbol timing alignment uses infonnation about the decisions that the receiver is of the interference with the signal are studied" The results making to assist it in detennining the optimum sampling indicate that the filter excess bandwidth has an important instant influence on perfonnance, with larger excess bandwidth Earll di h . ed f han I . " er stu es ave examm aspects 0 co-c ...

Due to their complexity and large attack surface, 5G networks pose threats and potential for cybersecurity. This paper examines these issues. The study seeks to uncover 5G network design cybersecurity challenges and assess new solutions. The paper uses secondary data from research, industry sources, and expert evaluations to discuss the expanding threat environment, including virtualization, edge computing, and IoT integration risks. Significant findings show that 5G systems need Zero-Trust Architecture, AI-powered threat detection, and Post-Quantum Cryptography to safeguard against sophisticated assaults. The report emphasizes worldwide collaboration in achieving harmonized cybersecurity standards and regulatory frameworks. Governments and companies must prioritize secure IoT devices, engage in quantum-resistant technology research, and expand public-private collaborations for threat intelligence sharing. The report concludes that 5G network security requires a multilayered strategy incorporating innovative technology and coordinated efforts to provide a durable and trustworthy foundation for the connected future.

Secure communication mechanisms in Wireless Sensor Networks (WSNs) have been widely deployed to ensure confidentiality, authenticity and integrity of the nodes and data. Recently many WSNs applications rely on trusted communication to ensure large user acceptance. Indeed, the trusted relationship thus far can only be achieved through Trust Management System (TMS) or by adding external security chip on the WSN platform. In this study an alternative mechanism is proposed to accomplish trusted communication between sensors based on the principles defined by Trusted Computing Group (TCG). The results of other related study have also been analyzed to validate and support our findings. Finally the proposed trusted mechanism is evaluated for the potential application on resource constraint devices by quantifying their power consumption on selected major processes. The result proved the proposed scheme can establish trust in WSN with less computation and communication and most importantly eliminating the need for neighboring evaluation for TMS or relying on external security chip.

In recent years, a wireless sensor network is gaining much more importance due to its immense contribution in numerous applications. Deployment of sensor nodes that would reduce computation, minimize cost and gaining high degree of network connectivity is an challenging task. Random deployment of sensor nodes causes the wireless sensor networks to face topological weaknesses such as communication bottlenecks, network partitions and sensing holes. These problems lead to uneven energy utilization, reduction in reliability of network and reduction in network lifetime. Bottleneck detection algorithm is proposed to identify bottleneck and minimal bottleneck zones in network. Additional sensor node deployment strategy is used in bottleneck detection algorithm to extend network lifetime. Random additional sensor node deployment and Targeted additional sensor node deployment are proposed to enhance network lifetime. Deployment strategies are compared with respect to network parameters such as throughput, packet delivery fraction and network lifetime.

Kenya's rapid digitalization of government and educational services has increased their exposure to cyberattacks. There has been an increase in phishing, ransomware, and Distributed Denial of Service (DDoS) attacks, which are now escalating toward potential impacts on essential digital infrastructure. This study highlights significant incidents such as the 2023 Anonymous Sudan attack on the eCitizen platform and cyberattacks targeting Kenyan universities. The existing defenses against cyber-attacks are based on traditional measures such as firewalls and antivirus. These have proven to be inadequate given the sophisticated nature of evolving cyber threats. This study assesses the role of AI in threat detection systems offering real-time capabilities for a quick autonomous response. AI and blockchain are emerging as critical solutions to supplicate the existing gaps. AI performs a dual function; it fortifies defenses while offering offense, as AI-driven assaults introduce contemporary defenses with a new set of challenges. As cloud adoption grows, these systems could easily become the prominent targets of attacks, thus reiterating the importance of precise cybersecurity measures. AI-driven threat detection substantially reduce response times, while blockchain technology offers secure, immutable methods for storing sensitive data such as academic records and digital identities. This study proposes the application of AI for fast detection of threats, the use of blockchain for secure storage, increase in staff training, and provision for a common cybersecurity protocol. Public-private partnerships can mitigate high implementation costs, making it feasible for these recommendations. These measures are vital for reducing cyber threats and safe guarding Kenya digital infrastructure. This will support Kenya digital transformation goals.

An essential component of the educational system is e-learning. this study delves into the potential risks and threats that e-learning systems face from unauthorized access by third parties and ways to protect data from unauthorized use, alteration, and reuse in a variety of e-learning-related circumstances, this work presents a systematic literature review on phishing techniques. it also takes mitigation techniques for phishing into account. as a result, the component and the threat posed by the information security component are presented in this study. in addition, important information security techniques for safeguarding e-learning systems are suggested at the conclusion of this paper. today, cybercrime remains a continuous danger. this paper gives an overview of the different types of phishing attempts and how they work. we come across new forms of cybercrime every day, along with its dire repercussions. as a result, there are numerous ways for hackers to pilfer sensitive and important data in addition to money. we also provide ideas and tactics that should be taken into account while creating mitigation plans. mitigation strategies primarily rely on human-centric approaches, secure e-learning systems, machine learning and neural networks, deep learning, and cryptography. as new phishing attacks emerge, new strategies will continue to develop to counter them.

Wireless Sensor Networks (WSN) are networks of typically small, battery-powered, wireless devices, equipped with on-board processing, communication, and sensing capabilities. Especially wireless sensor network suffers from excessive packet loss, over hearing, retransmission of the packets due to node mobility and constant energy dissipation. A current technique for routing and data transmission does not take into account of optimizing the transmission through Energy Balancing. There are several power and energy aware algorithms that claim to compensate for the energy losses. The main fundamental of most of the techniques is to route the packets through the highest energy nodes which lead to quick battery drainage of those node. Therefore the network lifetime decreases. In this project we have proposed a unique protocol for Network lifetime improvement by modifying the Leach protocol. The fundamental of the protocol is to develop a cluster based routing where cluster heads should be selected based on maximum coverage and should have sufficient energy to prolong the communication. Clusters are dynamically formed and changed with transmission. The technique is compared with conventional LEACH. Result shows that the proposed system achieves high data delivery with extended lifetime.

Metering Infrastructure (AMI) networks. However, few works have studied the efficient use of public key cryptography certificates in such a network and most of them focus on certificates' revocation. In this paper, we extensively investigate the performance our previous proposal on an efficient certificate renewal scheme that we proposed for AMI networks. First, quantitative analysis is carried out to compare our scheme against signature-based certificate renewal schemes. Then, all schemes are implemented in a realistic network model using NS-3 to evaluate their performance. Simulation results demonstrate the improved performance of our scheme in computational cost, communication overhead, end-to-end delay, packet delivery ratio, and required bandwidth compared with the signature-based certificate renewal scheme.

The great qualities as well as fast data transfer through one location to the other using wireless technologies for communication is causing a rapid progress in this field. Security constitutes one of the biggest challenges in Wireless Sensor Networks [WSNs], relying mostly on applications deployed. There are many different security measures that have been suggested to protect the privacy of information in WSNs. With those strategies come disadvantages like security flaws. In this work, a security-related survey is undertaken. Because of their physical restrictions in terms of both size and power, WSN influence the adoption of security greatly. We also present the pro's and con's of several existing models with their implementation in WSN. The importance of QoS was also a very important one to know the performance of the various models in terms of security.

Wireless Mesh Networking is an emerging technology in order to provide a possibility to build a network that can grow in terms of coverage to offer service access (i.e. internet access) for a large number of people with different needs. Wireless mesh networks are more vulnerable to wormhole attack (one out of many kind of attacks). In a typical wormhole attack, two or more malicious nodes plan together by establishing a tunnel using an efficient communication medium. The aim of this paper is to describe a wormhole detection algorithm for wireless mesh networks which detect the wormholes by calculating neighbour list and directional neighbour list of the source node. The main goal of the algorithm is that it can provide approximate location of nodes and effect of wormhole attack on all nodes which is useful in implementing countermeasures. The performance evaluation is done by varying no. of wormholes in the network. Keywords— Wireless Mesh Networks, Wormhole attack, Wormhole Detecti...

Healthcare institutions require advanced technology to collect patients’ data accurately and continuously. The tradition technologies still suffer from two problems: performance and security efficiency. The existing research has serious drawbacks when using public-key mechanisms such as digital signature algorithms. In this paper, we propose Reliable and Efficient Integrity Scheme for Data Collection in HWSN (REISCH) to alleviate these problems by using secure and lightweight signature algorithms. The results of the performance analysis indicate that our scheme provides high efficiency in data integration between sensors and server (saves more than 24% of alive sensors compared to traditional algorithms). Additionally, we use Automated Validation of Internet Security Protocols and Applications (AVISPA) to validate the security procedures in our scheme. Security analysis results confirm that REISCH is safe against some well-known attacks.

Redundant data retransmission problem in wireless sensor networks (WSNs) can be eliminated using the data aggregation process which combines similar data to reduce the resource-consumption and consequently, saves energy during data transmission. In the recent days, many researchers have focused on securing this paradigm despite the constraints it imposes such as the limited resources. Most of the solutions proposed to secure the data aggregation process in WSNs are essentially based on the use of encryption keys to protect data during their transmission in the network. Indeed, the key generation and distribution mechanisms involve additional computation costs and consume more of energy. Considering this, in this paper, we propose a new security mechanism to secure data aggregation in WSNs called SDAW (secure data aggregation watermarking-based B Djallel Eddine Boubiche

In this work, we consider the general problem of distributed detection in the presence of Byzantines using wireless sensor networks. Instead of attempting to mitigate Byzantine attacks as a system designer, we investigate the issue from the perspective of a Byzantine attacker. The probability for each individual sensor to be compromised (compromising probability) required to blind the system operation is adopted as the attack measure. Under the system setting that the fusion center (FC) declares the most likely hypothesis to be true based on the M -ary data from N local sensors, a Byzantine attack policy that can blind the FC with the minimum compromising probability for each individual sensor is derived under the assumption that the Byzantine attacker knows the statistics of the local outputs. The closed-form expression for a blind-achieving Byzantine transition probability that is used to alter the statistics of the local outputs of compromised sensors is also established. Our results indicate that the statistics of the local outputs is essential for the minimization of an attacker's effort.

In wireless sensor networks, sensor nodes are spread randomly over the coverage area to collect information of interest. Data fusion is used to process these collected information before they are sent to the base station, the observer of the sensor network. We study the security of the data fusion process in this work. In particular, we propose a witness-based solution to assure the validation of the data sent from data fusion nodes to the base station. We also present the theoretical analysis for the overhead associated with the mechanism, which indicates that even in an extremely harsh environment the overhead is low for the proposed mechanism.

Wireless sensor networks place sensors into an area to collect data and send them back to a base station. Data fusion, which fuses the collected data before they are sent to the base station, is usually implemented over the network. Since the sensor is typically placed in locations accessible to malicious attackers, information assurance of the data fusion process is very important. A witness-based approach has been proposed to validate the fusion data. In this approach, the base station receives the fusion data and "votes" on the data from a randomly chosen sensor node. The vote comes from other sensor nodes, called "witnesses," to verify the correctness of the fusion data. Because the base station obtains the vote through the chosen node, the chosen node could forge the vote if it is compromised. Thus, the witness node must encrypt the vote to prevent this forgery. Compared with the vote, the encryption requires more bits, increasing transmission burden from the chosen node to the base station. The chosen node consumes more power. This work improves the witness-based approach using direct voting mechanism such that the proposed scheme has better performance in terms of assurance, overhead, and delay. The witness node transmits the vote directly to the base station. Forgery is not a problem in this scheme. Moreover, fewer bits are necessary to represent the vote, significantly reducing the power consumption. Performance analysis and simulation results indicate that the proposed approach can achieve a 40 times better overhead than the witness-based approach.

The purpose of a wireless sensor network (WSN) is to provide the users with access to the information of interest from data gathered by spatially distributed sensors. Generally the users require only certain aggregate functions of this distributed data. Computation of this aggregate data under the end-to-end information flow paradigm by communicating all the relevant data to a central collector node is a highly inefficient solution for this purpose. An alternative proposition is to perform in-network computation. This, however, raises questions such as: what is the optimal way to compute an aggregate function from a set of statistically correlated values stored in different nodes; what is the security of such aggregation as the results sent by a compromised or faulty node in the network can adversely affect the accuracy of the computed result. In this paper, we have presented an energyefficient aggregation algorithm for WSNs that is secure and robust against malicious insider attack by any compromised or faulty node in the network. In contrast to the traditional snapshot aggregation approach in WSNs, a node in the proposed algorithm instead of unicasting its sensed information to its parent node, broadcasts its estimate to all its neighbors. This makes the system more fault-tolerant and increase the information availability in the network. The simulations conducted on the proposed algorithm have produced results that demonstrate its effectiveness.

Wireless sensor networks (WSNs) have attracted a lot of interest over the last decade in wireless and mobile computing research community. Applications of WSNs are numerous and growing, which range from indoor deployment scenarios in the home and office to outdoor deployment in adversary's territory in a tactical battleground. However, due to distributed nature and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their performance. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Design and implementation of secure WSNs is, therefore, a particularly challenging task. This chapter provides a comprehensive discussion on the state of the art in security technologies for WSNs. It identifies various possible attacks at different layers of the communication protocol stack in a typical WSN and presents their possible countermeasures. A brief discussion on the future direction of research in WSN security is also included.

As the Internet of Things (IoT) gradually emerges as the next step in the evolution of the Internet, it is important to identify the various potential domains of IoT applications and the research agenda associated with these applications. Become. From smart cities to healthcare, smart agriculture, logistics, retail, smart homes and smart environments, the IoT is expected to permeate almost every aspect of everyday life. Today's IoT technology has improved significantly over the last few years, but there are still many issues that need attention. As the concept of IoT emerges from heterogeneous technologies, many research challenges inevitably arise. The fact that the IoT is so widespread that it affects almost every area of our lives has become a significant research topic for research in various related areas such as information technology and computer science. Therefore, the IoT opens the way to a new dimension in research. This white paper describes recent developments in IoT technology and addresses future application and research challenges.

This case study explores the experience of Tools and Solutions (T&S), a small business that encountered a devastating ransomware attack and successfully restored its operations through strategic resilience. Initially exposed due to inadequate cybersecurity measures, T&S became a target for attackers who encrypted critical data and disrupted business continuity. Through prompt crisis management and longterm reforms, including cloud-based backup solutions, the implementation of the Odoo ERP system, employee cybersecurity training, and the application of the NIST Cybersecurity Framework, T&S enhanced its cybersecurity stance. This case underscores the vital preventive strategies necessary to mitigate ransomware threats, particularly for small and medium-sized enterprises, by integrating technology, policy development, and employee education. T&S's transition from vulnerability to resilience serves as a beneficial model for organisations aiming to strengthen defences against increasingly sophisticated cyber threats. The findings highlight that cybersecurity is not just a technological issue but an organisation-wide discipline that requires ongoing investment and diligence.

In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are se...

Cortina is a distributed Real-Time Location System (RTLS) designed to track assets or people moving indoors. Our solution leverages a low-cost, low-power Wireless Sensor Network (WSN) based on the IEEE 802.15.4 radio standard. The network, which consists of wall-plugged sensor nodes, is designed to be self-configuring, self-healing and self-calibrating, thus reducing deployment and maintenance costs. Assets and people are tracked using small battery operated wireless tags that collect Received Signal Strength (RSS) measurements from nearby sensor nodes. The tags also include an accelerometer for activity recognition, and a barometric pressure sensor to accurately detect the floor plan. We have conducted experiments over a 2000 m 2 area instrumented with eighteen sensor nodes. Our initial results show that the system can track people in real-time with an average error of 2.8 m.

Advancements in information technology, and transfer of data online leading to the vulnerabilities and giving an invitation to cyber threats. Security and privacy are important issues in cloud storage and networking fields. Cloud computing created an environment that facilitates the internet of things. The cloud computing technology consisted of servers, applications, and other storage devices, relying on the virtual connection. The stored information in clouds can be manipulated without the need for extensive physical activities. The internet of things devices provide information and services through information and communication technology, these end-node IoT devices are not very intelligent and resource-constrained thus they are vulnerable to cyber threats. It posed an issue of insecurity as malicious individuals learned ways of intruding these systems and causing intellectual and physical losses. The study proposes numerous solutions for the challenges of privacy and security in the cloud and IoT. The paper provides an in-depth examination of security challenges in cloud computing and IoT. The methodology uses the interpretive technique. The results depict the importance of the machine and deep learning in cyber-attack and mitigation. Analysis of variance (ANOVA) test statistics is used to test the reliability of the study.

Radio frequency (RF) jamming is a denial of service attack targeted at wireless networks. In resource-hungry scenarios with constant traffic demand, jamming can create connectivity problems and seriously affect communication. Therefore, the vulnerabilities of wireless networks must be studied. In this study, we investigate a particular type of RF jamming that exploits the semantics of physical (PHY) and medium access control (MAC) layer protocols. This can be extended to any wireless communication network whose protocol characteristics and operating frequencies are known to the attacker. We propose two efficient jamming techniques: A low-data-rate random jamming and a shot-noise based protocol-aware RF jamming. Both techniques use shot-noise pulses to disrupt ongoing transmission ensuring they are energy efficient, and they significantly reduce the detection probability of the jammer. Further, we derived the tight upper bound on the duration and the number of shot-noise pulses for Wi-Fi, GSM, and WiMax networks. The proposed model takes consider the channel access mechanism employed at the MAC layer, data transmission rate, PHY/MAC layer modulation and channel coding schemes. Moreover, we analyze the effect of different packet sizes on the proposed jamming methodologies. The proposed jamming attack models have been experimentally evaluated for 802.11b networks on an actual testbed environment by transmitting data packets of varying sizes. The achieved results clearly demonstrate a considerable increase in the overall jamming efficiency of the proposed protocolaware jammer in terms of packet delivery ratio, energy expenditure and detection probabilities over contemporary jamming methods provided in the literature.

Mission critical wireless sensor networks require an efficient, lightweight and flexible intrusion detection methodology to identify abnormal node or malicious attackers. The proposed idea in this paper is to develop a new approach of abnormal node detection in wireless sensor network using IDS security routing methodology by dividing the network into number of pairs. Every node of each pair is responsible to identify abnormality of other node in that particular pair considering different attributes of nodes. The abnormal node detection algorithm uses both signatures and knowledge based routing methodology to achieve most accurate and reliable result.

Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%.

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the security issues related to wireless sensor networks and suggests some techniques for achieving system security. This paper also discusses a protocol that can be adopted for increasing the security of the transmitted data.

In this paper, we present a monitoring assisted robust routing scheme for wireless mesh networks which exploits the broadcast nature of wireless transmissions at special routers with added monitoring functionalities. These routers passively listen to the transmissions in their neighborhood and compare the routing behavior against the routing state collectively maintained by them. If any inconsistency is found, as a result of software/hardware malfunction, these routers try to determine the node causing it and recover from it in a timely fashion. The scheme is developed for wireless mesh networks where the communication overhead is a critical issue. The performance evaluation of our scheme shows considerable improvement in reliability (i.e., delivery ratio achieved by standard routing protocols) with minimal overhead under situations of malfunctions. Regular paper submission to 2 nd round of review of: ACM/Springer Mobile Networks and Applications (MONET) Special Issue on "Advances in Wireless Mesh Networks".

With enterprise cloud-native environments growing, a single cluster of Kubernetes is rarely sufficient. Compliance mandates, latency management, regional availability, or service segregation all compel the use of multi-cluster Kubernetes management. Managing multiple clusters, however, brings complexity into networking, security, monitoring, and deployment. In this blog, we discuss the best tools, pitfalls, and practices for successfully operating multi-cluster Kubernetes environments at scale.

This study explores the role of Information and Communication Technology (ICT) in enhancing accessibility for special needs students at Kebbi State School for Special Needs. It investigates the current integration of educational technologies, their effectiveness in addressing diverse learning challenges, and the barriers impeding their adoption. Using a mixed-methods approach, data were collected through surveys, interviews, and observations involving students, teachers, and administrators. Quantitative data were analyzed using descriptive statistics, while qualitative data were subjected to thematic analysis.
Findings reveal that tools such as screen readers, text-to-speech software, and interactive boards significantly improve learning outcomes for visually and hearing-impaired students. However, challenges such as inadequate funding, lack of teacher training, and device maintenance hinder widespread ICT adoption. The study also highlights the need for government and non-governmental organizations to prioritize resource allocation and teacher development programs. Diagrams and tables illustrate key insights, including the comparative analysis of student performance before and after ICT integration.
The research underscores the transformative potential of ICT in fostering inclusive education. It recommends targeted interventions to bridge the gap between policy and practice, ensuring all students, regardless of ability, can access quality education. By addressing the unique needs of special education in Kebbi State, this study contributes to the broader discourse on equity and accessibility in education.

Ethical hacking and penetration testing are essential for identifying and mitigating security vulnerabilities in an era of increasing cyber threats. Traditional penetration testing methods, while effective, face challenges such as high costs, time-consuming assessments, and limited adaptability to evolving attack patterns. To address these limitations, this study proposes an AI-Driven Breach and Attack Simulation framework that integrates machine learning algorithms and automation techniques to simulate real-world attacks, analyze system weaknesses, and provide actionable security insights in real time. By leveraging Graph Neural Networks (GNN) for attack path prediction, BERT-based models for vulnerability detection, and Security Orchestration, Automation, and Response for risk assessment, the AI-driven Breach and Attack Simulation (BAS) system ensures continuous security evaluation while reducing false positives and response time. Experimental results demonstrate the superiority of AI-driven BAS over conventional penetration testing methods, achieving improved detection accuracy, automated remediation, and enhanced cyber resilience. This research highlights the importance of AIpowered cybersecurity solutions in proactively defending against modern cyber threats, suggesting that AI-driven BAS can revolutionize penetration testing by providing scalable, real-time, and adaptive security measures across various industries.

The study explored the extent to which cybercrime incidents affected the performance of a commercial bank in Uganda. The Specific objectives were: To determine the extent to which cybercrime activities affect bank profitability and to establish the extent to which information security awareness impacts on bank performance. An Explanatory research design was employed targeting a tier 1 bank in Uganda. The data for the research was collected via an interview guide, document reviews and a questionnaire that was purposely created for this study. A Sample of 52 employees was purposively and randomly selected from a population of 60 Employees. Spearman's correlation coefficient was calculated to show the relationship between study variables and test hypothesis. Findings revealed cyber security activities have a negative impact on the bank's performance, information security awareness weakens the negative impact of

Wireless networks have emerged as a fundamental component of modern communication systems, facilitating ubiquitous connectivity and data exchange. However, this convenience comes with an inherent vulnerability to security threats, ranging from unauthorized access to eavesdropping and data breaches. This MSc final year project seeks to address this critical issue by comprehensively enhancing the security of wireless networks. The project's primary objective is to identify and mitigate security threats, develop advanced security measures, and foster a security-conscious culture within the wireless network environment. The methodology integrates threat analysis, policy development, access control, encryption, intrusion detection, user behaviour analysis, regulatory compliance, and awareness training. The expected outcomes include improved security measures, enhanced user awareness, and recommendations for securing emerging wireless technologies, ultimately fortifying the trust and reliability of wireless communication.

In the modern digital era, organizations are increasingly dependent on information systems and technology to operate effectively. This reliance, however, comes with an inherent risk: cyberattacks. These attacks are growing both in frequency and sophistication, leaving organizations vulnerable to data breaches, financial losses, and damage to their reputation. As a result, cybersecurity measures must evolve to address these emerging threats. One such proactive approach to improving cybersecurity is penetration testing, often referred to as "ethical hacking." This article explores the concept of penetration testing, its significance in modern cybersecurity, and how it contributes to fortifying the security posture of organizations [2]. What is Penetration Testing? Penetration testing (pen testing) is a simulated cyberattack performed by cybersecurity experts, commonly referred to as ethical hackers. The primary aim is to identify, exploit, and address vulnerabilities in a system before malicious hackers can take advantage of them. Pen testers use the same tools and techniques as cybercriminals but are authorized and operate within the bounds of an organization's security policies. The process is meant to uncover weaknesses in an organization's infrastructure, software, or human operations, providing valuable insights into how a potential attacker could exploit these vulnerabilities. Penetration tests are not a one-size-fits-all approach[1]. They can vary in scope and method depending on the organization's needs. Some of the most common types of penetration tests include: External Testing: This focuses on vulnerabilities that are accessible from the outside, such as external-facing web servers, email systems, and firewalls. External testing simulates the perspective of an external attacker trying to breach the system remotely. Internal Testing: In contrast, internal testing assumes that an attacker has already gained access to the network. This could involve simulating an attack by a disgruntled employee or a compromised user account. Internal tests are critical for identifying vulnerabilities in internal networks, databases, and critical business systems. Web Application Testing: Given the increasing reliance on web applications, these are often the target of attacks. Penetration testers focus on identifying vulnerabilities within the code, logic, and configuration of web applications, which can

An important reason to protect provider and medical care patient information and data is digital terrorism. Patient health records and healthcare provider information are sensitive issues that can affect patients' health or even national security. Many frequent attacks have carried out breaches on health databases/data sets continuously and on remote servers and local servers with wireless sensor networks (WSNs) in different places. Various modern approaches addressed this issue and were designed to prevent these attacks with the aim of providing the security of patient information and data transmitted and collected by WSNs. However, there are still several signs of weakness in these approaches, which are described in the literature review. This research proposes a new trustworthy approach that supports the provision of data security and information collected by WSN and stored on basestation databases. This approach takes advantage of the integration of several security mechanisms including symmetric cryptography for encryption, asymmetric cryptography for signature and access control, as well as the implementation of the Lesamnta-LW function in the signature mechanism. Solid methodology and careful use of these mechanisms protect and hide users' information from being exposed to attackers. Security analyses, investigative comparisons and rigorous findings indicate that the proposed approach is superior to previous approaches.

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor's still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneous WSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor's energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.

Healthcare institutions require advanced technology to collect patients' data accurately and continuously. The tradition technologies still suffer from two problems: performance and security efficiency. The existing research has serious drawbacks when using public-key mechanisms such as digital signature algorithms. In this paper, we propose Reliable and Efficient Integrity Scheme for Data Collection in HWSN (REISCH) to alleviate these problems by using secure and lightweight signature algorithms. The results of the performance analysis indicate that our scheme provides high efficiency in data integration between sensors and server (saves more than 24% of alive sensors compared to traditional algorithms). Additionally, we use Automated Validation of Internet Security Protocols and Applications (AVISPA) to validate the security procedures in our scheme. Security analysis results confirm that REISCH is safe against some well-known attacks.

This paper explores the methodologies employed in the study of mobile and social media phishing, aiming to enhance the understanding of these evolving threats and develop robust countermeasures. By synthesizing existing research, we identify key approaches, including surveys, controlled experiments, data mining, and machine learning, to gather and analyze data on phishing tactics. These methods enable us to uncover patterns in attacker behavior, pinpoint vulnerabilities in mobile and social platforms, and evaluate the effectiveness of current detection and prevention strategies. Our findings highlight the growing sophistication of phishing techniques, such as social engineering and deceptive messaging, which exploit the trust and habits of users on these platforms. Through this investigation, we aim to contribute actionable insights for improving anti-phishing technologies, user awareness programs, and policy frameworks. This research is critical in addressing the escalating risks posed by phishing in the digital age, where mobile devices and social media are integral to daily life. By advancing the field of phishing forensics, we strive to protect users, safeguard sensitive information, and mitigate the widespread impact of these cyber threats.

Wireless Sensor Networks (WSN) is an emerging technology for attraction of researchers with its research
challenges and various application domains. Today, WSN applications can be used in environmental
detection, Monitoring system, medical system, military and industrial monitoring for ability to transform
human life in various aspects. Depending on applications used for WSNs, security is the biggest challenges
in WSNs and security aspect is essential for WSNs before designing WSNs. The routing protocols for WSNs
need security services for transmission exact and secure data to the users through the network. LEACH
(Low Energy Adaptive Clustering Hierarchy) is a routing protocol used in WSNs by arranging sensor
nodes into clusters. Every sensor cluster is managed by a Cluster Head (CH) during the network operation
such as routing and data aggregation from Cluster Member (CM). Therefore, security and authentication
is necessary between CH and CM. However, LEACH is lack of security. This paper presents integration of
security and authentication between CH and CM on LEACH routing protocol. For the implementation of
this integration, NS-2 simulation software is used and it is necessary to combine security agent into NS-2
tool for WSN. But currently, NS-2 does not support these features. Therefore, the main aim of this paper is
to develop security and authentication agent into NS-2 and LEACH protocol for WSNs with the simulation
results.

Wireless Sensor Networks (WSNs) are becoming widespread and pervasive, even in context where dependability and security of the deployed network could be crucial to critical and life-saving tasks. Due to the evolution rush experienced in past few years, several security aspects need to be further investigated. In this paper, we present a survey of the main vulnerabilities of WSNs and propose a specific taxonomy. This is a first step towards the definition of a formal security evaluation framework for WSNs, as we introduce in the end of this paper.

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

In today's increasingly digital and decentralized work environment, securing endpoints has become a critical concern for organizations aiming to protect sensitive data and maintain operational integrity. Distributed workforces, which rely heavily on remote devices such as laptops, smartphones, and tablets, present a unique challenge in terms of securing the vast array of endpoints that connect to corporate networks. These endpoints, often vulnerable to cyberattacks, serve as prime attack vectors for malicious actors seeking unauthorized access to organizational systems. This paper examines the role of endpoint security in reducing attack vectors in distributed work environments. By reviewing existing literature and analyzing recent trends in cybersecurity, we highlight the primary threats and vulnerabilities that affect endpoint devices in remote work settings. Furthermore, we explore the technologies and strategies currently used to mitigate these risks, such as endpoint detection and response (EDR) tools, multi-factor authentication (MFA), and virtual private networks (VPNs), while also evaluating their limitations. Additionally, the paper discusses emerging solutions, such as artificial intelligence (AI)-driven security systems and zero-trust architectures, that offer the potential for more robust, adaptive protection in dynamic, dispersed work environments. The importance of a holistic approach to security, one that includes not only technical measures but also employee training and awareness, is emphasized. Ultimately, this research underscores the need for organizations to strengthen endpoint security frameworks in order to reduce exposure to cyberattacks. By addressing the unique vulnerabilities of remote work setups, companies can protect their data, enhance productivity, and reduce the risks associated with distributed work models. This paper concludes with a discussion on the future directions of endpoint security, offering insights into how organizations can stay ahead of evolving cyber threats.

Cybersecurity is not an exception of the various fields that have been influenced by the advancement of artificial intelligence hence its evolution. Nevertheless, AI is also used by hackers for the increasing number, scale, and efficacy of their attacks. This paper therefore defines AI Cybercrime as automated phishing, AI malware, social engineering and cyber espionage, which are new form of threats that affect persons, organizations, and governments. This paper focuses on investigating AI-assisted cyber threats, knowledge and tools of an attacker, as well as defenses against threats and attacks. Applying threat detection systems with the help of artificial intelligence, ways and means of machine learning-based anomaly detection, and interaction between a human and an AI are discussed as some possible countermeasures. These small examples of AI applications in phishing, ransomware, and espionage have raised a call for robust and evolving cybersecurity measures. The results also state the fact that AI acts as a potential threat as well as having the capability to deal with threats effectively, thus requiring constant R&D in AI security.

As sensor networks are deployed in adversarial environments and used for critical applications such as battlefield surveillance and medical monitoring, security weaknesses become a big concern. The severe resource constraints of WSNs give rise to the need for resource bound security solutions. In this paper we present SIGF (Secure Implicit Geographic Forwarding), a configurable secure routing protocol family for wireless sensor networks that provides "good enough" security and high performance. By avoiding or limiting shared state, the protocols prevent many common attacks against routing, and contain others to the local neighborhood. SIGF makes explicit the tradeoff between security provided and state which must be stored and maintained. It comprises three protocols, each forming a basis for the next: SIGF-0 keeps no state, but provides probabilistic defenses; SIGF-1 uses local history and reputation to avoid attackers; and SIGF-2 uses neighborhood-shared state to provide stronger security guarantees. Our performance evaluation shows that SIGF achieves high packet delivery ratios with low overhead and endto-end delay. We evaluate the security of SIGF protocols under various security attacks and show that it effectively contains the damage from compromised nodes and defends against black hole, selective forwarding, Sybil, and some denial of service attacks.

The difficulty with safety in Wireless Sensor Networks (WSNs) requires comprehensive, holistic security techniques that can offer a long-term solution. Quantum security is anticipated to assist in the overall security strategy for WSN systems given the broad adoption of WSN around the world, notably the Internet of Things (IoTs). With the use of WSN and these quantum safeguards, data security can be offered whenever and anywhere. This study examines the security limitations of WSNs and offers a comprehensive approach through all network layers that will function as an Equal security mechanism solution and a solution to shield nodes from threats such as attacks on sensitive data, eavesdropping, disruption, destruction, and alteration. WSNs are used in a range of missions, especially the armed forces, ecological, and health ones, for evaluation, tracking, and regulating purposes. However, problems arise from being aware of their resource limitations. Data and node security are difficu...