When Two-Layer Federated Learning and Mean-Field Game Meet 5G and Beyond Security: Cooperative Defense Systems for 5G and Beyond Network Slicing

Cyber security for 5G and Beyond (5GB) network slicing is drawing much attention due to the increase of complex and dangerous cyber-attacks that could target the critical components of network slicing, such as radio access and core network. This paper proposes a new cyber defense approach based on two-layer Federated Learning (FL) to protect 5GB network slicing from the most dangerous network attacks and a mean-field game to safeguard the FL-enabled defense system from poisoning attacks. Our proposed distributed defense systems cooperate, intending to detect internal and external attacks targeting the critical components of 5GB network slicing and detecting infected parts in the 5GB defense system. Our experimental results show that our cooperative defense systems exhibit high accuracy detection rates against network attacks, namely (distributed) denial of service and botnets while being robust against poisoning attacks and requiring a few overheads generated by defense systems. To the best of our knowledge, we are the first to propose lightweight and accurate cooperative defense systems based on two-layer FL and non-cooperative games to enhance security against attackers in 5GB network slicing

The Future of Cybercrime Prevention Strategies: Human Factors and A Holistic Approach to Cyber Intelligence

New technology is rapidly emerging to fight increasing cybercrime threats, however, there is one important component of a cybercrime that technology cannot always impact and that is human behavior. Unfortunately, humans can be vulnerable and easily deceived making technological advances alone inadequate in the cybercrime fight. Instead, we must take a more holistic approach by using technology and better understanding the human factors that make cybercrime possible. In this issue of the International Journal of Cybersecurity Intelligence and Cybercrime, three studies contribute to our knowledge of human factors and emerging cybercrime technology so that more effective comprehensive cybercrime prevention strategies can be developed

Electrically Small Multimodal 3D Beamforming MIMO Antenna for PHY-Layer Security

This work proposes an electrically small 3D beamforming antenna for PHYsical Layer (PHY-layer) security. The antenna comprises two layers of stacked patch structures and is a five-mode five-port MIMO system operating around 1.85 GHz with electrical size ${ka=0.98}$ and radiation efficiency of up to ${55\%}$. By studying the properties of the excited modes, phase and amplitude control allow for unidirectional beam scanning towards any direction around the elevation and azimuth planes. PHY-layer security is investigated using the directional modulation (DM) technique, which transmits unscrambled baseband constellation symbols to a pre-specified secure direction while simultaneously spatially distorting the same constellations in all other directions. Bit Error Rate (BER) calculations reveal very low values of ${2\times10^{-5}}$ for the desired direction of the legitimate receiver, with BER${<10^{-2}}$ beamwidths of ${55^{\circ}}$ and ${58^{\circ}}$ for the azimuth and elevation planes, respectively.Comment: Conference pape

Physical Layer Security of Large Reflecting Surface Aided Communications with Phase Errors

The physical layer security (PLS) performance of a wireless communication link through a large reflecting surface (LRS) with phase errors is analyzed. Leveraging recent results that express the \ac{LRS}-based composite channel as an equivalent scalar fading channel, we show that the eavesdropper's link is Rayleigh distributed and independent of the legitimate link. The different scaling laws of the legitimate and eavesdroppers signal-to-noise ratios with the number of reflecting elements, and the reasonably good performance even in the case of coarse phase quantization, show the great potential of LRS-aided communications to enhance PLS in practical wireless set-ups.Comment: This work has been submitted to the IEEE for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A General Security Approach for Soft-information Decoding against Smart Bursty Jammers

Malicious attacks such as jamming can cause significant disruption or complete denial of service (DoS) to wireless communication protocols. Moreover, jamming devices are getting smarter, making them difficult to detect. Forward error correction, which adds redundancy to data, is commonly deployed to protect communications against the deleterious effects of channel noise. Soft-information error correction decoders obtain reliability information from the receiver to inform their decoding, but in the presence of a jammer such information is misleading and results in degraded error correction performance. As decoders assume noise occurs independently to each bit, a bursty jammer will lead to greater degradation in performance than a non-bursty one. Here we establish, however, that such temporal dependencies can aid inferences on which bits have been subjected to jamming, thus enabling counter-measures. In particular, we introduce a pre-decoding processing step that updates log-likelihood ratio (LLR) reliability information to reflect inferences in the presence of a jammer, enabling improved decoding performance for any soft detection decoder. The proposed method requires no alteration to the decoding algorithm. Simulation results show that the method correctly infers a significant proportion of jamming in any received frame. Results with one particular decoding algorithm, the recently introduced ORBGRAND, show that the proposed method reduces the block-error rate (BLER) by an order of magnitude for a selection of codes, and prevents complete DoS at the receiver.Comment: Accepted for GLOBECOM 2022 Workshops. Contains 7 pages and 7 figure

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