Intelligent black hole detection in mobile AdHoc networks

Security is a critical and challenging issue in MANET due to its open-nature characteristics such as: mobility, wireless communications, self-organizing and dynamic topology. MANETs are commonly the target of black hole attacks. These are launched by malicious nodes that join the network to sabotage and drain it of its resources. Black hole nodes intercept exchanged data packets and simply drop them. The black hole node uses vulnerabilities in the routing protocol of MANETS to declare itself as the closest relay node to any destination. This work proposed two detection protocols based on the collected dataset, namely: the BDD-AODV and Hybrid protocols. Both protocols were built on top of the original AODV. The BDD-AODV protocol depends on the features collected for the prevention and detection of black hole attack techniques. On the other hand, the Hybrid protocol is a combination of both the MI-AODV and the proposed BDD-AODV protocols. Extensive simulation experiments were conducted to evaluate the performance of the proposed algorithms. Simulation results show that the proposed protocols improved the detection and prevention of black hole nodes, and hence, the network achieved a higher packet delivery ratio, lower dropped packets ratio, and lower overhead. However, this improvement led to a slight increase in the end-to-end delay

Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles

Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput

FEATURE SELECTION FOR INTRUSION DETECTION SYSTEM IN A CLUSTER-BASED HETEROGENEOUS WIRELESS SENSOR NETWORK

Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment.  Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period

Feature Selection for Black Hole Attacks

The security issue is essential and more challenging in Mobile Ad-Hoc Network (MANET) due to its characteristics such as, node mobility, self-organizing capability and dynamic topology. MANET is vulnerable to different types of attacks. One of possible attacks is black hole attack. Black hole attack occurs when a malicious node joins the network with the aim of intercepting data packets which are exchanged across the network and dropping them which affects the performance of the network and its connectivity. This paper proposes a new dataset (BDD dataset) for black hole intrusion detection systems which contributes to detect the black hole nodes in MANET. The proposed dataset contains a set of essential features to build an efficient learning model where these features are selected carefully using one of the feature selection techniques which is information gain technique J48 decision tree, Naïve Bayes (NB) and Sequential Minimal Optimization (SMO) classifiers are learned using training data of BDD dataset and the performance of these classifiers is evaluated using a learning machine tool Weka 3.7.11. The obtained performance results indicate that using the proposed dataset features succeeded in build an efficient learning model to train the previous classifiers to detect the black hole attack