Soft Computing-Based Information Security

IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200), 1998

This paper describes the structure and the theoretical principles of the security software that we developed. This sofware is an industrial application based on the neural networks theory that we describe hereafter. Its aim is to recognize somebody's face and thus to add one more protecting level to the Windows NT and Windows 95 security access. We implemented the face learning phase by using the projection onto eigenvectors matrix and the backpropagation algorithm. We stored in a database, the identification of the faces which have been learned and we added a security protection when opening the personal session of the operating system Windows NT and we created a new level of protection for Windows 95. We tested our algorithms on images of other types than faces and the results allow to use the software in industrial control.

International Journal of Artificial Intelligence & Applications, 2010

Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam). Our tested data and experiments results shows promising results, and spam's are detected out at least 98.17 % with 0.12% false positive.

IASET, 2021

The World Wide Web is used by hackers to send malicious attack in form of phishing, e-mail spoofing and malware infection to people. With the speed of cyber activity and high volume of data used, the protection of cyber space cannot be handled by any physical device or by human intervention alone. It needs considerable automation to detect threats and to make intelligent real-time decisions. It is difficult to develop software with conventional algorithms to effectively protect against the dynamically evolving attacks. It can be tackled by applying bio inspired computing methods of artificial intelligence to the software. The purpose of this study is to explore the possibilities of artificial intelligence based algorithms in addressing the cybercrime issues. The algorithms include Logistic Regression (LR), Support Vector Machine (SVM) and Counter Propagation Neural network (CPNN) and their ensemble. 700 dataset were gotten from a renowned database. The dataset were subjected to features extraction and transformation. The outputs of the experimentation showed that sensitivity produced

Confidentiality, Integrity, and Availability of Military information is a crucial and critical factor for a country's national security. The security of military information systems (MIS) and Networks (MNET) is a subject of continuous research and design, due to the fact that they manage, store, manipulate, and distribute the information. This study presents a bio-inspired hybrid artificial intelligence framework for cyber security (bioHAIFCS). This framework combines timely and bio-inspired Machine Learning methods suitable for the protection of critical network applications, namely military information systems, applications and networks. More specifically, it combines (a) the hybrid evolving spiking anomaly detection model (HESADM), which is used in order to prevent in time and accurately , cyber-attacks, which cannot be avoided by using passive security measures, namely: Firewalls, (b) the evolving computational intelligence system for malware detection (ECISMD) that spots and isolates malwares located in packed executables untraceable by antivirus, and (c) the evolutionary prevention system from SQL injection (ePSSQLI) attacks, which early and smartly forecasts the attacks using SQL Injections methods.

As the popularity and usage of Internet increases all over the world, security issues have also become very essential that have to be considered. In literature, different techniques from various disciplines have been utilized to develop efficient security methods but those techniques always have some shortcomings. In this chapter the author discuss soft computing techniques that have been applied in Intrusion Detection System (IDS). The scope of this chapter will encompass core techniques of soft computing including artificial neural networks, Fuzzy Logic Genetic algorithms and hybridization of these approaches. In this chapter the research contribution of each of above mentioned techniques will be systematically summarized and compared that will allows us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this chapter should provide useful insights into the current IDS literature and be a good source for anyone who is...

Nowadays,securing the transmitted data is the most important challenging areas of development and research in modern communication. Users are able to communicate over an insecure channel using cryptography,so an attacker cannot decrypt and understand the original message. Public key cryptography requires large computational power, huge time consumption and complexity. An Artificial Neural Network (ANN) is used in order to overcome these problems. The connection between cryptography and ANN provides a great help for the security concerns. This paper presents a review for the contribution of ANN in the field of network security

Soft Computing

Computer communications, 2007

An intrusion detection system's main goal is to classify activities of a system into two major categories: normal and suspicious (intrusive) activities. Intrusion detection systems usually specify the type of attack or classify activities in some specific groups. The objective of this paper is to incorporate several soft computing techniques into the classifying system to detect and classify intrusions from normal behaviors based on the attack type in a computer network. Among the several soft computing paradigms, neuro-fuzzy networks, fuzzy inference approach and genetic algorithms are investigated in this work. A set of parallel neuro-fuzzy classifiers are used to do an initial classification. The fuzzy inference system would then be based on the outputs of neuro-fuzzy classifiers, making final decision of whether the current activity is normal or intrusive. Finally, in order to attain the best result, genetic algorithm optimizes the structure of our fuzzy decision engine. The experiments and evaluations of the proposed method were performed with the KDD Cup 99 intrusion detection dataset.

Journal of Information Security

In today's world, computer network is evolving very rapidly. Most public or/and private companies set up their own local networks system for the purpose of promoting communication and data sharing within the companies. Unfortunately, their data and local networks system are under risks. With the advanced computer networks, the unauthorized users attempt to access their local networks system so as to compromise the integrity, confidentiality and availability of resources. Multiple methods and approaches have to be applied to protect their data and local networks system against malicious attacks. The main aim of our paper is to provide an intrusion detection system based on soft computing algorithms such as Self Organizing Feature Map Artificial Neural Network and Genetic Algorithm to network intrusion detection system. KDD Cup 99 and 1998 DARPA dataset were employed for training and testing the intrusion detection rules. However, GA's traditional Fitness Function was improved in order to evaluate the efficiency and effectiveness of the algorithm in classifying network attacks from KDD Cup 99 and 1998 DARPA dataset. SOFM ANN and GA training parameters were discussed and implemented for performance evaluation. The experimental results demonstrated that SOFM ANN achieved better performance than GA, where in SOFM ANN high attack detection rate is 99.

Advances in Intelligent Systems and Computing, 2020

In today's globalized world, the computer-based information system is one of the most significant domains that have played a major role in human life. On the other hand, data has been the superlative valuable stuff of an organization. Organizations are thus more reliant on the information as more data would be collected and distributed on a network-based system. Contravention of network security is one of the severe forms of cyber-attacks, as attackers generally seek complete access to all information and devices. Cybersecurity serves a tremendous amount of security as it is intended to protect the information. Safeguard mechanisms are used to enhance information security. With the development of soft computing, intrusion detection effectively monitors the dubious operation of system behavior and monitoring station that has become an integral part of security execution. Detection of intrusion is a mechanism of intelligently detecting events that occur in the operation of suspicious and malicious device activity and device resources within a network environment. It focuses on identifying possible attacks, capturing data and attempts to investigate. If IDS is implemented with alerts, then a device will be placed under risk. It is an enormous synchronized challenge on the availability of system assets resources that is conducted indirectly through a significant number of unreliable internet computer operators that disrupt services of valid users by consuming the server assets like CPU, hard drives, etc. This academic article addresses several facets of combating DDOS attacks by using soft computing methods for identification, security and prevention, strategies to trace back, relevant queries and research issues using artificial neural network (ANN).