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

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.Comment: 17 pages, 5 figures, and 4 tables. Submitted for publication

A qualitative cybersecurity analysis of time-triggered communication networks in automotive systems

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/).Security is gaining increasing importance in automotive systems, driven by technical innovations. For example, automotive vehicles become more open systems, allowing the communication with other traffic participants and road infrastructure. Also, automotive vehicles are provided with increased autonomy which raises severe safety concerns, and consequently also security concerns – both concerns that interweave in such systems. In this paper we present a qualitative cybersecurity analysis by comparing different time-triggered (TT) communication networks. While TT communication networks have been analysed extensively for dependability, the contribution of this work is to identify security-related benefits that TT communication networks can provide. In particular, their mechanisms for spacial and temporal encapsulation of network traffic are instrumental to improve network security. The security arguments can be used as a design guide for implementing critical communication in flexible network standards like TSN.Peer reviewe

Examining Machine Learning for 5G and Beyond through an Adversarial Lens

Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/RL) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally

Cognitive Networks with In-Band Full-Duplex Radios: Jamming Attacks and Countermeasures

© 2015 IEEE. Although in-band full-duplex (IBFD) radios promise to double the throughput of a wireless link, they are more vulnerable to jamming attacks than their out-of-band full-duplex (OBFD) counterparts. For two communicating OBFD nodes, a jammer needs to attack both the uplink and the downlink channels to completely break the communication link. In contrast, only one common channel needs to be jammed in the case of two IBFD nodes. Even worse, a jammer with self-interference suppression (SIS) capabilities (the underlying technique of IBFD radios) can learn the transmitters' activity while injecting interference, allowing it to react instantly to the transmitter's strategies. In this work, we consider a power-constrained IBFD 'reactive-sweep' jammer that sweeps through the set of channels by jamming a subset of them simultaneously. We model the interactions between the IBFD radios and the jammer as a stochastic constrained zero-sum Markov game in which nodes adopt the frequency hopping (FH) technique as their strategies to counter jamming attacks. Beside the IBFD transmission-reception (TR) mode, we introduce an additional operation mode, called transmission-detection (TD), in which an IBFD radio transmits and leverages its SIS capability to detect jammers. The aim of the TD mode is to make IBFD radios more cognitive to jamming. The nodes' optimal defense strategy that guides them when to hop and which operational mode (TD or TR) to use is then established from the equilibrium of the stochastic Markov game. We prove that this optimal policy has a threshold structure, in which IBFD nodes stay on the same channel up to a certain number of time slots before hopping. Simulation results show that our policy significantly improves the throughput of IBFD nodes under jamming attacks

REDESIGNING THE COUNTER UNMANNED SYSTEMS ARCHITECTURE

Includes supplementary material. Please contact [email protected] for access.When the Islamic State used Unmanned Aerial Vehicles (UAV) to target coalition forces in 2014, the use of UAVs rapidly expanded, giving weak states and non-state actors an asymmetric advantage over their technologically superior foes. This asymmetry led the Department of Defense (DOD) and the Department of Homeland Security (DHS) to spend vast sums of money on counter-unmanned aircraft systems (C-UAS). Despite the market density, many C-UAS technologies use expensive, bulky, and high-power-consuming electronic attack methods for ground-to-air interdiction. This thesis outlines the current technology used for C-UAS and proposes a defense-in-depth framework using airborne C-UAS patrols outfitted with cyber-attack capabilities. Using aerial interdiction, this thesis develops a novel C-UAS device called the Detachable Drone Hijacker—a low-size, weight, and power C-UAS device designed to deliver cyber-attacks against commercial UAVs using the IEEE 802.11 wireless communication specification. The experimentation results show that the Detachable Drone Hijacker, which weighs 400 grams, consumes one Watt of power, and costs $250, can interdict adversarial UAVs with no unintended collateral damage. This thesis recommends that the DOD and DHS incorporates aerial interdiction to support its C-UAS defense-in-depth, using technologies similar to the Detachable Drone Hijacker.DASN-OE, Washington DC, 20310Captain, United States Marine CorpsApproved for public release. Distribution is unlimited

SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications

© IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TII.2022.3165890In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.Peer reviewe