Raja Azlina Raja Mahmood

A survey on detecting black hole attack in AODV-based mobile ad hoc networks

This paper presents a survey of current methods of detecting black hole attack against ad hoc on-demand distance vector routing protocol in mobile ad hoc networks. In a black hole attack, a malicious node answers each route request with a fake reply claiming to have the shortest and freshest route to the destination. However, when the data packets arrive, the malicious node discards them. Seven detection methods are described in this paper, and their strengths and weaknesses discussed.

A Distributed Hierarchical Graph Neuron-Based Classifier: An Efficient, Low-Computational Classifier

Many of the widely used classifiers are time consuming and resource intensive, and hence not practical to be used in the emerging wireless networks. We present an efficient classifier, termed distributed hierarchical graph neuron (DHGN)-based classifier. Our proposed solution uses a new form of neural network, which consists of a hierarchical graph-based representation of input patterns, and adopts a one-cycle learning process. We compare the effectiveness and computational complexity of our proposed classifier with the well known self-organizing map (SOM) classifier in a supervised environment. The results show that the DHGN-based classifier offers lower computational complexity than SOM while guaranteeing satisfactory classification accuracy.

Impact of the energy-based and location-based LEACH secondary cluster aggregation on WSN lifetime

The improvement of sensor networks’ lifetime has been a major research challenge in recent years. This is because sensor nodes are battery powered and may be difficult to replace when deployed. Low energy adaptive clustering hierarchical (LEACH) routing protocol was proposed to prolong sensor nodes lifetime by dividing the network into clusters. In each cluster, a cluster head (CH) node receives and aggregates data from other nodes. However, CH nodes in LEACH are randomly elected which leads to a rapid loss of network energy. This energy loss occurs when the CH has a low energy level or when it is far from the BS. LEACH with two level cluster head (LEACH-TLCH) protocol deploys a secondary cluster head (2CH) to relieve the cluster head burden in these circumstances. However, in LEACH-TLCH the optimal distance of CH to base station (BS), and the choicest CH energy level for the 2CH to be deployed for achieving an optimal network lifetime was not considered. After a survey of related literature, we improved on LEACH-TLCH by investigating the conditions set to deploy the 2CH for an optimal network lifetime. Experiments were conducted to indicate how the 2CH impacts on the network at different CH energy levels and (or) CH distance to BS. This, is referred to as factor-based LEACH (FLEACH). Investigations in FLEACH show that as CHs gets farther from the BS, the use of a 2CH extends the network lifetime. Similarly, an increased lifetime also results as the CH energy decreases when the 2CH is deployed. We further propose FLEACH-E which uses a deterministic CH selection with the deployment of 2CH from the outset of network operation. Results show an improved performance over existing state-of-the-art homogeneous routing protocols.

Performance evaluation of time-based black hole attack detection in mobile ad hoc networks

Efficient and quick attack detection is critical in any networks, especially if the attack is harmful and can bring down the whole network within a short period of time. A black hole or packet drop attack is one example of a harmful attack in mobile ad hoc networks. In this study, we implement a series of time-based black hole attack detection of different time intervals and compare the results. We study the performances of the networks, the packet delivery ratio percentage, with detection interval time of 900, 450 and 300 seconds with a total of 900 seconds of simulation time. The results suggest that appropriate time interval is critical in providing reliable detection results in timely manner. In general, the 450 seconds detection interval time has provided more reliable results, with lower false positive percentage in comparison to those of the 300 seconds detection interval time. The best explanation to the high false positive rate in the shorter detection interval time is due to the insufficient time given to the packets to arrive to the destinations during the detection process. Meanwhile, implementing the attack detection only after 900 seconds may be considered too late and thus, may have a devastating impact to the networks.

Lightweight and distributed attack detection scheme in mobile ad hoc networks

Many of the widely used intrusion detection schemes, such as Self Organizing Map and Artificial Immune System, require heavy computational power in order to provide highly accurate results. These schemes have been successfully deployed in wired networks, which have high computational and bandwidth capabilities. Mobile ad hoc networks, however have limited resources and hence deploying such schemes are impractical. We propose a lightweight, low-computation, distributed intrusion detection scheme for mobile ad hoc networks termed the Distributed Hierarchical Graph Neuron (DHGN). The DHGN-based network is a new form of neural network, which consists of a hierarchical graph-based representation of input patterns. This pattern recognition scheme adopts a divide-and-distribute approach that divides an input pattern into a number of subpatterns, which are then concurrently processed for recognition. The first section of this paper provides an in-depth study of mobile ad hoc networks and current intrusion detection implementation in these networks. The second section of the paper provides an overview of the proposed two-stage cooperative intrusion detection system architecture and compares the proposed algorithm with Self Organizing Map classifier. The experiments show that our low computational scheme produced similar classification accuracy results to Self Organizing Map algorithm.

Impact of node inter-domain movement on MANETs performance

Packet delivery ratio (PDR) percentage is one of the important network performance indicators in MANETs. In general, the PDR value degrades as speed of the node increases and coupled with high mobility or constant movement. As more nodes move at high speed, more broken path or link breakage occur and thus, more packets will be dropped. Interestingly, PDR rate has also been used to detect packet drop or black hole attack in the network. Thus, the packet drop activity may due to either the broken path process itself or deliberate drop by malicious nodes. Validating the packet drop action itself is imperative in reducing the false positive rate during the attack detection. This paper studies the movements of nodes in the networks that have caused high packet drop percentage. In particular, we investigate the inter-domain movement since it has substantial effect on the packet drop percentage. To the best of our knowledge, this is the first work that studies such relationship. The results on the overall network show that the high number of inter-domain movement may not necessarily contribute significantly to the packet drop percentage. However, when focus is on the inter-domain movement of the critical nodes, we yield consistent results. The proposed monitoring approach is also energy efficient as it reduces the need to monitor other large number of nodes insignificant movements.

A Lightweight Graph-Based Pattern Recognition Scheme in Mobile Ad Hoc Networks

A lightweight, low-computation, distributed intrusion detection scheme termed the distributed hierarchical graph neuron (DHGN) was proposed to be incorporated into a cooperative intrusion detection system (IDS) in mobile ad hoc networks (MANETs). Its onecycle learning and divide-and-distribute recognition task approach allows DHGN to detect similar patterns in short of time. An IDS of such properties is essential in the resource constrained MANETs environment. MANETs are distributed and self-configuring networks, with limited resources and dynamic nodes.

A Wheel Graph Structured Associative Memory for Single-Cycle Pattern Recognition within P2P Networks

A novel and efficient associative-memory-based pattern recognition scheme within P2P networks is proposed and implemented. The proposed scheme, known as the multi-wheel Graph Neuron, is adapted from Graph Neuron-based algorithms which are single-cycle, light-weight, and scalable associative-memory-based pattern recognition algorithms for wireless sensor networks, and has been implemented over a structured P2P Chord overlay network. The proposed approach promotes collaboration among peers during the detection process within the P2P networks. Since the scheme only required single cycle learning, the communication cost amongst peers is minimized. The preliminary results show that the proposed single-cycle recognition scheme guarantees high detection accuracy.

Multi-wheel graph neuron: a distributed associative memory for structured P2P networks

The significant growing amount of shared files in distributed and serverless P2P networks has impacted heavily on content management and retrieval field. In this paper, we propose an associative memory network with intention to provide efficient pattern recognition within P2P networks. An efficient pattern recognition algorithm which works within distributed and dynamic nature of P2P network would be beneficial for better content handling and distribution. Our approach, which is called multi-wheel Graph Neuron (mWGN), is a variant of Graph Neuron (GN)-based algorithm built on top of Chord overlay network. GN algorithm is an associative memory designed for wireless sensor environment. mWGN is a restructured design from Hierarchical Graph Neuron (HGN), preserving its single cycle learning, lightweight and accuracy feature while requiring fewer number of nodes. Result from the experiment shows the proposed approach is highly accurate and the fault tolerance of Chord protocol provides stability for the proposed approach.

A distributed event detection scheme for wireless sensor networks

Many of the existing event detection schemes in wireless sensor networks that employ classification or clustering approaches suffer from high communication and computational overheads. We propose a low-computation, distributed, and lightweight event detection scheme in wireless sensor networks, which is adopted from the pattern recognition scheme known as Distributed Hierarchical Graph Neuron. The experimental results show that the proposed scheme guarantees satisfactory classification accuracy, in comparison to Support Vector Machine and Self-Organizing Map algorithms.