A Study on Intrusion Detection System in Wireless Sensor Networks

The technology of Wireless Sensor Networks (WSNs) has become most significant in present day. WSNs are extensively used in applications like military, industry, health, smart homes and smart cities. All the applications of WSN require secure communication between the sensor nodes and the base station. Adversary compromises at the sensor nodes to introduce different attacks into WSN. Hence, suitable Intrusion Detection System (IDS) is essential in WSN to defend against the security attack. IDS approaches for WSN are classified based on the mechanism used to detect the attacks. In this paper, we present the taxonomy of security attacks, different IDS mechanisms for detecting attacks and performance metrics used to assess the IDS algorithm for WSNs. Future research directions on IDS in WSN are also discussed

An Efficient Intrusion Detection Framework in Cluster-Based Wireless Sensor Networks

International audienceIn the last few years, the technological evolution in the field of wireless sensor networks was impressive, which made them extremely useful in various applications (military, commercial, etc.). In such applications, it is essential to protect the network from malicious attacks. This presents a demand for providing security mechanisms in these vulnerable networks. In this paper, we design a new framework for intrusion detection in cluster-based wireless sensor networks. Our detection framework is composed of different protocols that run at different levels. The first protocol is a specification-based detection protocol that runs at intrusion detection system (IDS) agents (low level). The second one is a binary classification detection protocol that runs at cluster head (CH) node (medium level). In addition, a reputation protocol is used at each CH to evaluate the trustworthiness level of its IDSs agents. Each CH monitors its CH neighbors on the basis of a specification detection protocol with the help of a vote mechanism applied at the base station (high level). We evaluated the performances of our framework in the presence of four well-known attacks: hello flood, selective forwarding, black hole, and wormhole attacks. We evaluated specifically the detection rate, false positive rate, energy consumption, and efficiency. Simulation results show that our detection framework exhibits high detection rate (almost 100%), low number of false positives, less time to detect the attack, and less energy consumption. Our intrusion detection framework outperforms other schemes proposed in the literature in terms of detection, false positive rate, and energy consumption

An efficient intrusion detection framework in cluster-based wireless sensor networks

International audienc

An intelligent intrusion detection system for external communications in autonomous vehicles

Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification