Anti-Jamming for Embedded Wireless Networks

Resilience to electromagnetic jamming and its avoidance are difficult problems. It is often both hard to distinguish malicious jamming from congestion in the broadcast regime and a challenge to conceal the activity patterns of the legitimate communication protocol from the jammer. In the context of energy-constrained wireless sensor networks, nodes are scheduled to maximize the common sleep duration and coordinate communication to extend their battery life. This results in well-defined communication patterns with possibly predictable intervals of activity that are easily detected and jammed by a statistical jammer. We present an anti-jamming protocol for sensor networks which eliminates spatio-temporal patterns of communication while maintaining coordinated and contention-free communication across the network. Our protocol, WisperNet, is time-synchronized and uses coordinated temporal randomization for slot schedules and slot durations at the link layer and adapts routes to avoid jammers in the network layer. Through analysis, simulation and experimentation we demonstrate that WisperNet reduces the efficiency of any statistical jammer to that of a random jammer, which has the lowest censorship-to-link utilization ratio. WisperNet has been implemented on the FireFly sensor network platform

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

Cluster based jamming and countermeasures for wireless sensor network MAC protocols

A wireless sensor network (WSN) is a collection of wireless nodes, usually with limited computing resources and available energy. The medium access control layer (MAC layer) directly guides the radio hardware and manages access to the radio spectrum in controlled way. A top priority for a WSN MAC protocol is to conserve energy, however tailoring the algorithm for this purpose can create or expose a number of security vulnerabilities. In particular, a regular duty cycle makes a node vulnerable to periodic jamming attacks. This vulnerability limits the use of use of a WSN in applications requiring high levels of security. We present a new WSN MAC protocol, RSMAC (Random Sleep MAC) that is designed to provide resistance to periodic jamming attacks while maintaining elements that are essential to WSN functionality. CPU, memory and especially radio usage are kept to a minimum to conserve energy while maintaining an acceptable level of network performance so that applications can be run transparently on top of the secure MAC layer. We use a coordinated yet pseudo-random duty cycle that is loosely synchronized across the entire network via a distributed algorithm. This thwarts an attacker\u27s ability to predict when nodes will be awake and likewise thwarts energy efficient intelligent jamming attacks by reducing their effectiveness and energy-efficiency to that of non-intelligent attacks. Implementing the random duty cycle requires additional energy usage, but also offers an opportunity to reduce asymmetric energy use and eliminate energy use lost to explicit neighbor discovery. We perform testing of RSMAC against non-secure protocols in a novel simulator that we designed to make prototyping new WSN algorithms efficient, informative and consistent. First we perform tests of the existing SMAC protocol to demonstrate the relevance of the novel simulation for estimating energy usage, data transmission rates, MAC timing and other relevant macro characteristics of wireless sensor networks. Second, we use the simulation to perform detailed testing of RSMAC that demonstrates its performance characteristics with different configurations and its effectiveness in confounding intelligent jammers

Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT.

PhD Theses.Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT su ers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attacks autonomously. In this context, sub-band information from the Orthogonal Frequency Division Multiplexing (OFDM) modulated transmission in the spectrum has been extracted from the radio device receiver terminal, and a generalized state vector (GS) is formed containing low dimension in-phase and quadrature components. Accordingly, a probabilistic method based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission with no abnormalities has been proposed to statistically model signal behaviors inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF), is implemented to perform state estimation and capture malicious attacks. Subsequently, GS containing a higher number of subcarriers has been investigated. In this connection, Variational autoencoders (VAE) is used as a deep learning technique to extract features from high dimension radio signals into low dimension latent space z, and DBN is learned based on GS containing latent space data. Afterward, to perform state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that appears due to either jammer attacks in transmission or cognitive devices in a network experiencing di erent transmission sources that have not been observed previously. The performance is assessed using the receiver

Anti-jamming for embedded wireless networks

Resilience to electromagnetic jamming and its avoidance are difficult problems. It is often both hard to distinguish malicious jamming from congestion in the broadcast regime and a challenge to conceal the activity patterns of the legitimate communication protocol from the jammer. In the context of energy-constrained wireless sensor networks, nodes are scheduled to maximize the common sleep duration and coordinate communication to extend their battery life. This results in well-defined communication patterns with possibly predictable intervals of activity that are easily detected and jammed by a statistical jammer. We present an anti-jamming protocol for sensor networks which eliminates spatio-temporal patterns of communication while maintaining coordinated and contention-free communication across the network. Our protocol, WisperNet, is time-synchronized and uses coordinated temporal randomization for slot schedules and slot durations at the link layer and adapts routes to avoid jammers in the network layer. Through analysis, simulation and experimentation we demonstrate that WisperNet reduces the efficiency of any statistical jammer to that of a random jammer, which has the lowest censorship-to-link utilization ratio. WisperNet has been implemented on the FireFly sensor network platform