A Media Access Control Protocol for Wireless Adhoc Networks with Misbehaviour Avoidance

The most common wireless Medium Access Control (MAC) protocol is IEEE 802.11. Currently IEEE 802.11 standard protocol is not resilient for many identified MAC layer attacks, because the protocol is designed without intention for providing security and with the assumption that all the nodes in the wireless network adhere to the protocol. However, nodes may purposefully show misbehaviours at the MAC layer in order to obtain extra bandwidth con-serve resources and degrade or disrupt the network performance. This research proposes a secure MAC protocol for MAC layer which has integrated with a novel misbehaviour detection and avoidance mechanism for Mobile Ad Hoc Networks (MANETs). The proposed secure MAC protocol the sender and receiver work collaboratively together to handshakes prior to deciding the back-off values. Common neighbours of the sender and receiver contributes effectively to misbehaviours detection and avoidance process at MAC layer. In addition the proposed solution introduces a new trust distribution model in the network by assuming none of the wireless nodes need to trust each other. The secure MAC protocol also assumes that misbehaving nodes have significant levels of intelligence to avoid the detectio

Analysis of DoS Attacks at MAC Layer in Mobile Adhoc Networks

—Wireless network security has received tremendous attention due to the vulnerabilities exposed in the open communication medium. The most common wireless Medium Access Control (MAC) protocol is IEEE 802.11, which assumes all the nodes in the network are cooperative. However, nodes may purposefully misbehave in order to disrupt network performance, obtain extra bandwidth and conserve resources. These MAC layer misbehaviours can lead to Denial of Service (DoS) attacks which can disrupt the network operation. There is a lack of comprehensive analysis of MAC layer misbehaviour driven DoS attacks for the IEEE 802.11 protocol. This research studied possible MAC layer DoS attack strategies that are driven by the MAC layer malicious/selfish nodes and investigates the performance of the IEEE 802.11 protocol. Such DoS attacks caused by malicious and selfish nodes violating backoff timers associated with the protocol. The experimental and analytical approach evaluates several practical MAC layer backoff value manipulation and the impact of such attacks on the network performance and stability in MANETs. The simulation results show that introducing DoS attacks at MAC layer could significantly affect the network throughput and data packet collision rate. This paper concludes that DoS attacks with selfish/malicious intend can obtain a larger throughput by denying well-behaved nodes to obtain deserved throughput, also DoS attacks with the intend of complete destruction of the network can succee

Resilient Misbehaviour Detection MAC Protocol (MD-MAC) for Distributed Wireless Networks

Chaminda Alocious, Hannan Xiao, B. Christianson, 'Resilient Misbehaviour Detection MAC Protocol (MD-MAC) for Distributed Wireless Networks' paper presented at the 2016 IEEE Wireless Communications and Networking Conference (IEEE WCNC). Doha, Qatar. 3-6 April 2016Wireless network security requirements are becoming more important and critical. The modern network security architectures require more attention to provide security in each network layer. This will require understanding of protocol vulnerabilities in existing protocol architectures. However, providing security requirements are not just limited to confidentiality and integrity, also availability and fairness are important security elements. IEEE 802.11 MAC protocol is one of the most common standard in modern day networks and has been designed without a consideration for providing security protection at MAC layer. IEEE 802.11 assumes all the nodes in the network are cooperative. However, nodes may purposefully misbehave in order to obtain extra bandwidth, conserve resources and disrupt network performance. This research proposes a Misbehaviour Detection MAC protocol (MD-MAC) to address the problematic scenarios of MAC layer misbehaviours, which takes a novel approach to detect misbehaviours in Mobile Adhoc Networks (MANETs). The MD-MAC modifies the CSMA/CA protocol message exchange and uses verifiable backoff value generation mechanism with an incorporated trust model which is suitable for distributed networks. The MD-MAC protocol has been implemented and evaluated in ns2, simulation results suggest that the protocol is able to detect misbehaving wireless nodes in a distributed network environment

Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi