WisperNet: Anti-Jamming for Wireless Sensor 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 is more energy efficient than low-power listen CSMA protocols such as B-mac and is simple to analyze in terms of effective network throughput, reliability and delay. WisperNet has been implemented on the FireFly sensor network platform

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

Spectrum Sensing and Security Challenges and Solutions: Contemporary Affirmation of the Recent Literature

Cognitive radio (CR) has been recently proposed as a promising technology to improve spectrum utilization by enabling secondary access to unused licensed bands. A prerequisite to this secondary access is having no interference to the primary system. This requirement makes spectrum sensing a key function in cognitive radio systems. Among common spectrum sensing techniques, energy detection is an engaging method due to its simplicity and efficiency. However, the major disadvantage of energy detection is the hidden node problem, in which the sensing node cannot distinguish between an idle and a deeply faded or shadowed band. Cooperative spectrum sensing (CSS) which uses a distributed detection model has been considered to overcome that problem. On other dimension of this cooperative spectrum sensing, this is vulnerable to sensing data falsification attacks due to the distributed nature of cooperative spectrum sensing. As the goal of a sensing data falsification attack is to cause an incorrect decision on the presence/absence of a PU signal, malicious or compromised SUs may intentionally distort the measured RSSs and share them with other SUs. Then, the effect of erroneous sensing results propagates to the entire CRN. This type of attacks can be easily launched since the openness of programmable software defined radio (SDR) devices makes it easy for (malicious or compromised) SUs to access low layer protocol stacks, such as PHY and MAC. However, detecting such attacks is challenging due to the lack of coordination between PUs and SUs, and unpredictability in wireless channel signal propagation, thus calling for efficient mechanisms to protect CRNs. Here in this paper we attempt to perform contemporary affirmation of the recent literature of benchmarking strategies that enable the trusted and secure cooperative spectrum sensing among Cognitive Radios

Wireless communication, sensing, and REM: A security perspective

The diverse requirements of next-generation communication systems necessitate awareness, flexibility, and intelligence as essential building blocks of future wireless networks. The awareness can be obtained from the radio signals in the environment using wireless sensing and radio environment mapping (REM) methods. This is, however, accompanied by threats such as eavesdropping, manipulation, and disruption posed by malicious attackers. To this end, this work analyzes the wireless sensing and radio environment awareness mechanisms, highlighting their vulnerabilities and provides solutions for mitigating them. As an example, the different threats to REM and its consequences in a vehicular communication scenario are described. Furthermore, the use of REM for securing communications is discussed and future directions regarding sensing/REM security are highlighted

CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks

The main goal of the H2020-CARAMEL project is to address the cybersecurity gaps introduced by the new technological domains adopted by modern vehicles applying, among others, advanced Artificial Intelligence and Machine Learning techniques. As a result, CARAMEL enhances the protection against threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture aiming at assessing the integrity of the information transmitted by vehicles, as well as at improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (1) multi-radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support, enabled by the connectivity infrastructure; (2) a MEC platform, where, among others, algorithms for detecting attacks are implemented; (3) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (4) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application, the interaction between the entities of the CARAMEL architecture is showcased in case of a GPS spoofing attack scenario. Adopted attack detection techniques exploit robust in-vehicle and cooperative approaches that do not rely on encrypted GPS signals, but only on measurements available in the CARAMEL architecture.This work was supported by the European Union’s H2020 research and innovation programme under the CARAMEL project (Grant agreement No. 833611). The work of Christian Vitale, Christos Laoudias and Georgios Ellinas was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programmes, Coordination, and Development. The work of Jordi Casademont and Pouria Sayyad Khodashenas was also supported by FEDER and Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya through projects Fem IoT and SGR 2017-00376 and by the ERDFPeer ReviewedPostprint (author's final draft

Radar Signal Processing for Interference Mitigation

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically