Geethapriya Thamilarasu

A cross-layer based intrusion detection approach for wireless ad hoc networks

Wireless ad-hoc networks are vulnerable to various kinds of security threats and attacks due to relative ease of access to wireless medium and lack of a centralized infrastructure. In this paper, we seek to detect and mitigate the denial of service (DoS) attacks that prevent authorized users from gaining access to the networks. These attacks affect the service availability and connectivity of the wireless networks and hence reduce the network performance. To this end, we propose a novel cross-layer based intrusion detection system (CIDS) to identify the malicious node(s). Exploiting the information available across different layers of the protocol stack by triggering multiple levels of detection, enhances the accuracy of detection. We validate our design through simulations and also demonstrate lower occurrence of false positives

A Cross-layer Approach to Detect Jamming Attacks in Wireless Ad hoc Networks

Building an efficient intrusion detection system (IDS) is a challenging task in wireless ad hoc networks due to the resource constraints and lack of a centralized control. In this work, we present a decentralized monitor-based IDS for detecting jamming type denial of service (DoS) attacks at the lower layers of the protocol stack. The varying channel and network dynamics in ad hoc networks can impair service similar to a jamming scenario, resulting in false positives on intrusion detections. To this end, we incorporate a cross-layer design in our IDS to differentiate the malicious jamming behavior from genuine network failures. We validate our design through simulation, and establish the effectiveness of the model. From the simulation results, we observe a significant improvement in the accuracy of detection and lower false positives

Intrusion detection in RFID systems

In recent years, advances in radio frequency identification (RFID) technology has led to their widespread adoption in diverse applications such as object identification, access authorization, environmental monitoring and supply chain management. Although the increased proliferation of tags enables new applications, they also raise many unique and potentially serious security and privacy concerns. Security solutions in RFID systems need to be strengthened to ensure information integrity and to prevent hackers from exploiting the sensitive tag data. In this paper, we address the importance of intrusion detection security paradigm for RFID systems. We present an overview of state of the art in RFID security and investigate the limitations of traditional security solutions based on cryptographic primitives and protocols. We propose an RFID intrusion detection model that integrates information from RFID reader layer and middleware layer to detect anomalous behavior in the network, thus improving their resilience to security attacks.

Security Solution For Data Integrity InWireless BioSensor Networks

In body biosensor networks, which may be classified as a specialized type of ad hoc networks, it is necessary to ensure the authenticity and freshness of the vital medical information. In this work, we propose security solutions to identify attacks on data freshness and preserve message integrity in these networks. We use the measurement of permissible round trip time threshold and computationally feasible authentication solution to address the security threats in the network. We have implemented a prototype framework in GloMoSim to assess and evaluate the robustness of our detection mechanism.

Improving reliability of jamming attack detection in Ad hoc Networks

in a network system, network coding allows intermediate nodes to encode the received messages before forwarding them, thus network coding is vulnerable to pollution attacks. Besides, the attacks are amplified by the network coding process with the result that the whole network maybe polluted. In this paper, we proposed a novel unconditionally secure authentication code for multi-source network coding, which is robust against pollution attacks. For the authentication scheme based on theoretic strength, it is robust against those attackers that have unlimited computational resources, and the intermediate nodes therein can verify the integrity and origin of the encoded messages received without having to decode them, and the receiver nodes can check them out and discard the messages that fail the verification. By this way, the pollution is canceled out before reaching the destinations.

Exploring Cross-layer techniques for Security: Challenges and Opportunities in Wireless Networks

In this paper, we discuss the challenges and opportunities of using cross-layer techniques for enhancing wireless network security. Cross-layer approach has gained considerable interest in performance optimization due to their design advantages. While the architectural modification introduced by the inter-layer interactions show promising results on overall network performance, there is also a growing concern on their limitations. Here, we investigate the impact of cross-layer techniques on security and network performance. An in depth understanding of the strength and weakness of cross-layer methods is necessary in designing robust architectures. To this end, we evaluate different cross-layer architectures and analyze their efficiency in intrusion detection systems.

Game Theoretic Modeling of Jamming Attacks in Ad hoc Networks

The objective of jamming attack in a network is to deny service to the communicating nodes, thus reducing network throughput and availability. In this paper, we propose a game theoretic framework for detecting jamming attacks in wireless ad hoc networks. We formulate jamming as a two- player, non cooperative game to analyze the interaction between attacker and monitoring nodes in the network. We propose hybrid detection strategies at the monitor node using cross-layer features for attack detection. We solve the game by computing the mixed strategy Nash equilibrium and derive optimal attack and detection strategies. Numerical results show that game theoretic analysis can be used to determine optimal steady state monitoring strategies to provide higher detection accuracy, while balancing the monitoring energy costs.

iDetect: an intelligent intrusion detection system for wireless body area networks

Driven by recent technological advances in wireless communications, wireless sensors, and low power networked systems, wireless sensor networks WSNs are emerging as a promising technology in healthcare applications. Since information transmitted in these wireless body area networks WBAN often consists of critical and sensitive patient health and personal information, securing these networks is of central importance to their practical deployment in healthcare applications. The objective of this research is to design and develop intelligent intrusion detection techniques to improve security in WBAN. In this work, we propose iDetect, a multi-objective genetic algorithm based intrusion detection system IDS to provide optimal attack detection in these networks. The proposed algorithm guarantees that only those features necessary for detecting a specific attack is used in the intrusion detection process, thereby decreasing the computational complexity.

MALADY: A Machine Learning-Based Autonomous Decision-Making System for Sensor Networks

As the capabilities of sensor networks evolve, we need to address the challenges that will help in shifting the perception of sensor networks as being merely a data-gathering network to that of a network that is capable of learning and making decisions autonomously. This shift in intelligence from the edge to the nodes in the network is particularly relevant in unattended sensor deployments where there is no continuous access to a remote base station. In this paper, we propose anarchitecture, called MALADY, which uses a machine learningapproach to enable a network of embedded sensor nodes to use the data that they have gathered to learn and make decisions in real-time within the network and thereby, become autonomous. MALADY supports supervised as well as unsupervised learning algorithms. Our implementation of the algorithms introduces some practical optimizations, in order to make them viable for nodes with limited resources. Our experimental results basedon an implementation on the MicaZ mote/TinyOS platformshow that the supervised learning technique based on lineardiscriminant analysis has a higher learning complexity, but allows a sensor node to learn about the data correlations robustly and make decisions accurately, after learning from only a few samples. In comparison, the unsupervised learning technique based on clustering has a low overhead, but requires more learning samples to achieve a high detection accuracy.

CIDS: cross-layer intrusion detection system for mobile ad hoc networks

In mobile ad hoc networks (MANET), lack of centralised monitoring point and lack of clear line of defense pose serious challenges for intrusion detection systems (IDS). In this paper, we present CIDS, a cross-layer based intrusion detection system with enhanced security performance in mobile ad hoc networks. We utilise cross-layer interactions to gather measurements from different protocol layers to detect attacks with increased accuracy. We present a case study to analyse packet drop detection in ad hoc networks using cross-layer techniques. Compared to traditional watchdog detection, CIDS is more effective in distinguishing network misbehaviour from genuine network irregularities. Simulation results based on extensive experiments illustrate the robustness and reliability of CIDS through higher detection rate and lower false positives.