FUZZY BASED MALICIOUS NODES DETECTION IN MOBILE AD-HOC NETWORKS

Proceedings of National Conference on Recent Trends in Parallel Computing (RTPC - 2014)

The mobile ad-hoc network is more exposed to being attacked, their some fundamental Properties, such as dynamic topology, limited power and limited bandwidth, it is very hard to achieve absolute security in the mobile ad hoc network. Attack avoidance method like encryption and authentication are not enough for reducing the possibilities of attacks. However, these methods are designed to prevent for a set of possible known attacks. These methods are not able to prevent newer attacks that are originated in the existing security measures. So, the mechanism is needed to detect and response for these newer attacks. This paper explores and classifies current techniques of Intrusion Detection System (IDS) on MANET and we have study various intrusion detection techniques in MANET.

A mobile ad hoc network (MANET) is made up of various mobile nodes defined as free to move anywhere in the network and they communicate with each other wirelessly. MANET is also known as self organized network, which is a collection of mobile nodes responsible for every operation performed by them and responsible for its maintenance. Trust and cooperation among mobile ad hoc network (MANET) nodes affects the performance and lifetime of the network. Due to the dynamic nature of MANET nodes, they are exposed to various security attacks. The attack of creating and forwarding route request packets can lead to hogging of network resources and cause denial of service to legitimate nodes. This type of DoS attack is difficult to detect because malicious nodes mimic legitimate nodes in all aspects except flooding the network with request packets. A fuzzy based intrusion detection system has been proposed to mitigate such situation and reduce the loss of throughput. The proposed mechanism could prevent this specific kind of DoS attack and does not use any additional network bandwidth.

2014

The mobile ad-hoc networks (MANET) is a new wireless technology, having features like dynamic topology and self-configuring ability of nodes. The self -configuring ability of nods in MANET made it popular among the critical mission such as military use and emergency recovery. But due to the open medium and wide distribution of nodes make MANET vulnerable to malicious attackers. So to protect MANET from various attacks, it is crucial to develop an efficient secure intrusion-detection system for MANET. In this paper, we focus on various intrusion-detection system in MANETs. Keywords— Intrusion detection system,Malicious nodes, Misbehaviour report, Acknowledement.

Circuits and Systems, 2016

Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of thousands of nodes that are mobile as well as autonomous and they do not requires any existing network infrastructure. The autonomous nodes can freely and randomly move within the network which can create temporary dynamic network and these networks can change their topology frequently. The security is the primary issue in MANET which degrades the network performance significantly. In this paper, cluster based malicious node detection methodology is proposed to detect and remove the malicious nodes. Each node within the cluster gets the cluster key from the cluster head and this key is used for the data transaction between cluster head and node. The cluster head checks this key for every data transaction from node and match with their cluster table. If match is valid, and then only it will recognize that this node is belongs to this cluster, otherwise it is decided as malicious node. This paper also discusses the detection of link failure due to the presence of malicious node by determining the gain of each link in the network. The performance of the proposed method is analyzed using packet delivery ratio, network life time, and throughput and energy consumption. The proposed malicious node detection system is compared with the conventional techniques as OEERP (Optimized energy efficient routing protocol), LEACH (Low energy adaptive clustering hierarchy), DRINA (Data routing for In-network aggregation) and BCDCP (Base station controlled dynamic clustering protocol).

International Journal of Computer Applications, 2015

Wireless communication is widely adopted and application oriented technology There are a huge literature about Mobile Ad-hoc network is available. In these studies, the ad hoc network has two major issues security and performance. In this paper a feasible and adoptable solution is introduced for enhancing security in MANET. The presented work utilizes the network characteristics and their behavioral difference during attack. Using the attack and normal network behavior a machine learning algorithm is trained and the malicious patterns are distinguished according to the new network samples. The proposed machine learning based ad hoc network security is implemented using NS2 simulator and the performance of the system is evaluated in terms of metrics viz. throughput, packet delivery ratio, end to end delay and energy consumption. According to the obtained results the performance of the proposed secure network is optimum and adoptable.

The MANETs is one type of Ad Hoc network that is used for mobile communication. The Mobile Ad Hoc NETworks (MANETS) are used when the user is moving. Because MANET does not depend on fixed infrastructure. In MANETs Wireless networks are used to connect with different networks. surrounded by all the contemporary wireless networks, Mobile Ad hoc NETwork (MANET) is one of the most important and unique applications. The self-configuring ability of nodes in MANET absolutes it admired among essential mission applications like military use or emergency recovery. The mobility and scalability carried by wireless network ended it possible in many applications. Surrounded by all the contemporary wireless networks Mobile Ad hoc NETwork (MANET) is one of the most significant and exclusive applications. In this review paper a qualified learns of Secure Intrusion-Detection Systems for determining malicious nodes and attacks on MANETs are presented. One of the main advantages of wireless networks i...

Security in mobile ad hoc environment is the most concerned research issue which is focused in the proposed research methodology by introducing authenticated routing based attack injection and detection framework using genetic fuzzy rule based system (AR-AIDF-GFRS). This assures both the successful detections of attacks present in the environment and secured routing by using trusted nodes. Here, initially, jellyfish attack is injected into the MANET environment. This attack is detected by genetic fuzzy based rule system which would generate rules based on which attack would be identified. And then to ensure the secured routing, trust evaluation of nodes is done by ant colony based trust evaluation method (ACTEM). This method selects the optimal nodes which are trusted in nature for establishing the route path. The overall evaluation of the proposed research method is done in NS2 simulation environment which proves that AR-AIDF-GFRS can outperform the existing research method by accurately identifying attacker nodes.

Each node in Mobile Ad Hoc Network (MANETs) communicates with each other to transfer the packet to destination node. Any anomalous behaviour of a node can confine it from executing this operation and even can disturb the whole network process. Therefore, the need of monitoring the nodes arises to keep a check on the behaviour of a node. In this paper, an algorithm is proposed to monitor the nodes & to check if a node is under attack or not. Moreover, a second layer of security is added which is furnished by a testbed to monitor the nodes.

The mobile ad hoc networks (MANET) have been used in latest years, in several applications. They are more susceptible to malicious attack. It's more problematical to deliver security in mobile ad hoc network entirely. It's based on some exclusive characteristics. In addition the inhibition methods need to detect and yield essential activities to deliver the security to these types of networks. For this purpose many intrusion detection systems (IDSs) are used. An Intrusion Detection System (IDS) perceives malicious and selfish nodes in a network. Designing efficient IDS for wireless ad-hoc networks that would not disturb the performance of the network significantly is indeed a challenging task. Detecting Misbehavior (such as transmission of false information) in Mobile Adhoc Network (MANET) is an important problem with wide range of security applications. Authentication and confidentiality is an important aspect of mobile adhoc networks. It enables entities to cope with insecurity and uncontrollability caused by the free will of others. Security management is highly challenging issues in MANETs due to computational complexity constraints, and the independent movement of component nodes. The proposed technique used is Enhanced Adaptive Acknowledgment for intrusion detection system specially designed for MANETs. A Hybrid cryptographic Algorithm is used for obtaining a Authentication of message and recovering the original message without any conflict. The concept of implementing a hybrid scheme in AACK greatly decreases the network overhead.

International Journal of Computer Network and Information Security

Mobile ad hoc networks (MANETs) are faced with various security challenges emanating from malicious attacks. Their dynamic nature make nodes more vulnerable to attacks from either malicious nodes or intruders since there is no fixed infrastructure resulting in each node acting as router to transmit data. Currently, several solutions have been proposed and implemented in different ways aimed at eliminating or reducing these malicious attacks. However, the attacks still persist. Therefore, this paper proposes an efficient security mechanism based on machine learning as a solution that detects and identifies malicious attacks in real-time basis by classifying packets data as either normal or abnormal. To achieve this, we conducted experiments using logistic regression (LR) and a support vector machine (SVM) to choose the best predictive model utilizing the Iris data set. The results obtained show that LR performed better than SVM with an accuracy of 100% detection rate. Thus, LR is better suited for the identification of malicious attacks in MANETs. Furthermore, we proposed and designed a framework to detect malicious attacks in real-time in MANETs based on packet behavior using the LR model and the components were presented. We believe that, if this framework is implemented in MANETs, it could go a long way to reduce the rate of attacks in the infrastructure less network.