Maximize resource utilization based channel access model with presence of reactive jammer for underwater wireless sensor network

Underwater sensor networks (UWSNs) are vulnerable to jamming attacks. Especially, reactive jamming which emerged as a greatest security threat to UWSNs. Reactive jammer are difficult to be removed, defended and identified. Since reactive jammer can control and regulate (i.e., the duration of the jam signal) the probability of jamming for maintaining high vulnerability with low detection probability. The existing model are generally designed considering terrestrial wireless sensor networks (TWSNs). Further, these models are limited in their ability to detect jamming correctly, distinguish between the corrupted and uncorrupted parts of a packet, and be adaptive with the dynamic environment. Cooperative jamming model has presented in recent times to utilize resource efficiently. However, very limited work is carried out using cooperative jamming detection. For overcoming research challenges, this work present Maximize Resource Utilization based Channel Access (MRUCA). The MRUCA uses cross layer design for mitigating reactive jammer (i.e., MRUCA jointly optimizes the cooperative hopping probabilities and channel accessibility probabilities of authenticated sensor device). Along with channel, load capacity of authenticated sensor device is estimated to utilize (maximize) resource efficiently. Experiment outcome shows the proposed MRUCA model attain superior performance than state-of-art model in terms of packet transmission, BER and Detection rate

Modeling of Wireless Sensor Networks Jamming Attack Strategies

Jamming attacks like constant jamming, deceptive jamming, random jamming and reactive jamming disrupt the normal operation of nodes in wireless sensor networks thereby drastically affecting the network throughput, delay and energy consumption by sending random signals in the network without following the underlying Media Access Control rules. These signals collide with the legitimate signals causing undue traffic in the network leading to Denial of Service Attack. This work provides a model for the different jamming attacks experienced in wireless sensor networks using Unified Modeling Language. Modeling the different jamming attacks would help in better description of their behavior and strategy. Established models can be employed in the design of jamming detection and mitigation framework for DoS attacks mitigation in wireless sensor networks that involve sensors which are known to have limited and constrained resources

Precise Packet Loss Pattern Generation by Intentional Interference

Abstract—Intermediate-quality links often cause vulnerable connectivity in wireless sensor networks, but packet losses caused by such volatile links are not easy to trace. In order to equip link layer protocol designers with a reliable test and debugging tool, we develop a reactive interferer to generate packet loss patterns precisely. By using intentional interference to emulate parameterized lossy links with very low intrusiveness, our tool facilitates both robustness evaluation of protocols and flaw detection in protocol implementation

Alibi framework for identifying reactive jamming nodes in wireless LAN

Reactive jamming nodes are the nodes of the network that get compromised and become the source of jamming attacks. They assume to know any shared secrets and protocols used in the networks. Thus, they can jam very effectively and are very stealthy. We propose a novel approach to identifying the reactive jamming nodes in wireless LAN (WLAN). We rely on the half-duplex nature of nodes: they cannot transmit and receive at the same time. Thus, if a compromised node jams a packet, it cannot guess the content of the jammed packet. More importantly, if an honest node receives a jammed packet, it can prove that it cannot be the one jamming the packet by showing the content of the packet. Such proofs of jammed packets are called "alibis" - the key concept of our approach. In this paper, we present an alibi framework to deal with reactive jamming nodes in WLAN. We propose a concept of alibi-safe topologies on which our proposed identification algorithms are proved to correctly identify the attackers. We further propose a realistic protocol to implement the identification algorithm. The protocol includes a BBC-based timing channel for information exchange under the jamming situation and a similarity hashing technique to reduce the storage and network overhead. The framework is evaluated in a realistic TOSSIM simulation where the simulation characteristics and parameters are based on real traces on our small-scale MICAz test-bed. The results show that in reasonable dense networks, the alibi framework can accurately identify both non-colluding and colluding reactive jamming nodes. Therefore, the alibi approach is a very promising approach to deal with reactive jamming nodes.published or submitted for publicationnot peer reviewe

Droplet: A New Denial-of-Service Attack on Low Power Wireless Sensor Networks

In this paper we present a new kind of Denial-of-Service attack against the PHY layer of low power wireless sensor networks. Overcoming the very limited range of jamming-based attacks, this attack can penetrate deep into a target network with high power efficiency. We term this the Droplet attack, as it attains enormous disruption by dropping small, payload-less frame headers to its victim's radio receiver, depriving the latter of bandwidth and sleep time. We demonstrate the Droplet attack's high damage rate to full duty-cycle receivers, and further show that a high frequency version of Droplet can even force nodes running on very low duty-cycle MAC protocols to drop most of their packets

Intrusion Detection System for Platooning Connected Autonomous Vehicles

The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks