A Dynamic Anomaly Detection Scheme for AODV-Based Mobile Ad Hoc Networks

Nakayama, Hidehisa; Kurosawa, Satoshi; Jamalipour, Abbas; Nemoto, Y.; Kato, Nei

doi:10.1109/tvt.2008.2010049

Cited by 103 publications

(59 citation statements)

References 32 publications

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“…Nakayama et al [11] propose an adaptive method to detect attacks on the AODV routing protocol. The method is based on the calculation of projection distances using multidimensional statistics and the Principal Component Analysis (PCA) to determine the axis that expresses the most relevant data correlations.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Mitigation for Disaster Area Networks

Cucurull

Asplund

Nadjm‐Tehrani

2010

Lecture Notes in Computer Science

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Abstract. One of the most challenging applications of wireless networking are in disaster area networks where lack of infrastructure, limited energy resources, need for common operational picture and thereby reliable dissemination are prevalent. In this paper we address anomaly detection in intermittently connected mobile ad hoc networks in which there is little or no knowledge about the actors on the scene, and opportunistic contacts together with a store-and-forward mechanism are used to overcome temporary partitions. The approach uses a statistical method for detecting anomalies when running a manycast protocol for dissemination of important messages to k receivers. Simulation of the random walk gossip (RWG) protocol combined with detection and mitigation mechanisms is used to illustrate that resilience can be built into a network in a fully distributed and attack-agnostic manner, at a modest cost in terms of drop in delivery ratio and additional transmissions. The approach is evaluated with attacks by adversaries that behave in a similar manner to fair nodes when invoking protocol actions.

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Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Mitigation for Disaster Area Networks

Cucurull

Asplund

Nadjm‐Tehrani

2010

Lecture Notes in Computer Science

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“…Kabiri et al [8] have proposed an anomaly-based engine that focuses on detecting denial of service attacks (DoS). The proposed engine shares a number of similarities with [3] analyzed in sect. 2.1.…”

Section: Anomaly Detection Engine With Optimal Featuresmentioning

confidence: 99%

“…Nakayama et al [3] have proposed an anomaly-based engine for detecting malicious actions that target the Ad-hoc On-demand Distance Vector (AODV) [14] routing protocol. The proposed engine utilizes machine learning in order to generate and maintain a normal profile and relies on principal component analysis (PCA) [4] for resolving malicious behaviors.…”

Section: A Dynamic Anomaly Detection Scheme For Aodv-based Manetsmentioning

confidence: 99%

“…SVM is a non-probabilistic binary linear classifier, which, given a training sample, builds a model that decides whether a new example falls within the same category as the training sample or not. According to the authors, the training process of the SVM has a computational complexity of O(N 3 ), where N represents the number of training samples [5]. During the training phase, which takes place offline at a system with abundant resources [5], data are collected from the physical, medium access control (MAC) and network layers.…”

Section: Cross-layer Detection Of Sinkhole Attacks In Manetsmentioning

confidence: 99%

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An Evaluation of Anomaly-Based Intrusion Detection Engines for Mobile Ad Hoc Networks

Panos

Xenakis

Stavrakakis

2011

Trust, Privacy and Security in Digital Business

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Abstract. Mobile Ad Hoc Networks are susceptible to a variety of attacks that threaten their operation and the provided services. Intrusion Detection Systems may act as defensive mechanisms, since they monitor network activities in order to detect malicious actions performed by intruders. Anomaly-based detection engines are a topic of ongoing interest in the research community, due to their advantage in detecting unknown attacks. However, this advantage is offset by a number of limitations such as high rates of false alarms, imposition of processing overhead, lack of adaptability under dynamic network conditions etc. This paper presents a comprehensive evaluation and comparison of the most recent literature in the area of anomaly detection for MANETs. The provided weaknesses and limitations, which are thoroughly examined in this paper, constitute open issues in the area of MANET security and will drive future research steps.

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“…Attacks are even smart and act differently in different situations. So, above table describes the traditional methods for solvation of problems in WSN [12][13][14][15][16][17][18][19][20]. …”

Section: Literature Reviewmentioning

confidence: 99%

A survey on wireless sensor network (wsn) security using ai methods

2016

IJLTET

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A Dynamic Anomaly Detection Scheme for AODV-Based Mobile Ad Hoc Networks

Cited by 103 publications

References 32 publications

Anomaly Detection and Mitigation for Disaster Area Networks

Anomaly Detection and Mitigation for Disaster Area Networks

An Evaluation of Anomaly-Based Intrusion Detection Engines for Mobile Ad Hoc Networks

A survey on wireless sensor network (wsn) security using ai methods

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