14 research outputs found
Jamming energy allocation in training-based multiple access systems
We consider the problem of jamming attack in a multiple access channel with training-based transmission. First, we derive upper and lower bounds on the maximum achievable ergodic sum-rate which explicitly shows the impact of jamming during both the training phase and the data transmission phase. Then, from the jammer's design perspective, we analytically find the optimal jamming energy allocation between the two phases that minimizes the derived bounds on the ergodic sum-rate. Numerical results demonstrate that the obtained optimal jamming design reduces the ergodic sum-rate of the legitimate users considerably in comparison to fixed power jamming.The work of X. Zhou was supported by the Australian Research Council's Discovery Projects funding scheme (Project No. DP110102548)
Mitigating Smart Jammers in Multi-User MIMO
Wireless systems must be resilient to jamming attacks. Existing mitigation
methods based on multi-antenna processing require knowledge of the jammer's
transmit characteristics that may be difficult to acquire, especially for smart
jammers that evade mitigation by transmitting only at specific instants. We
propose a novel method to mitigate smart jamming attacks on the massive
multi-user multiple-input multiple-output (MU-MIMO) uplink which does not
require the jammer to be active at any specific instant. By formulating an
optimization problem that unifies jammer estimation and mitigation, channel
estimation, and data detection, we exploit that a jammer cannot change its
subspace within a coherence interval. Theoretical results for our problem
formulation show that its solution is guaranteed to recover the users' data
symbols under certain conditions. We develop two efficient iterative algorithms
for approximately solving the proposed problem formulation: MAED, a
parameter-free algorithm which uses forward-backward splitting with a box
symbol prior, and SO-MAED, which replaces the prior of MAED with soft-output
symbol estimates that exploit the discrete transmit constellation and which
uses deep unfolding to optimize algorithm parameters. We use simulations to
demonstrate that the proposed algorithms effectively mitigate a wide range of
smart jammers without a priori knowledge about the attack type.Comment: arXiv admin note: text overlap with arXiv:2201.0877
Attacking Massive MIMO Cognitive Radio Networks by Optimized Jamming
Massive multiple-input multiple-output (MaMIMO) and cognitive radio networks (CRNs) are two promising technologies for improving spectral efficiency of next-generation wireless communication networks. In this paper, we investigate the problem of physical layer security in the networks that jointly use both technologies, named MaMIMO-CRN. Specifically, to investigate the vulnerability of this network, we design an optimized attacking scenario to MaMIMO-CRNs by a jammer. For having the most adversary effect on the uplink transmission of the legitimate MaMIMO-CRN, we propose an efficient method for power allocation of the jammer. The legitimate network consists of a training and a data transmission phase, and both of these phases are attacked by the jammer using an optimized power split between them. The resulting power allocation problem is non-convex. We thus propose three different efficient methods for solving this problem, and we show that under some assumptions, a closed-form solution can also be obtained. Our results show the vulnerability of the MaMIMO-CRN to an optimized jammer. It is also shown that increasing the number of antennas at the legitimate network does not improve the security of the network
Universal MIMO Jammer Mitigation via Secret Temporal Subspace Embeddings
MIMO processing enables jammer mitigation through spatial filtering, provided
that the receiver knows the spatial signature of the jammer interference.
Estimating this signature is easy for barrage jammers that transmit
continuously and with static signature, but difficult for more sophisticated
jammers: Smart jammers may deliberately suspend transmission when the receiver
tries to estimate their spatial signature, they may use time-varying
beamforming to continuously change their spatial signature, or they may stay
mostly silent and jam only specific instants (e.g., transmission of control
signals). To deal with such smart jammers, we propose MASH, the first method
that indiscriminately mitigates all types of jammers: Assume that the
transmitter and receiver share a common secret. Based on this secret, the
transmitter embeds (with a linear time-domain transform) its signal in a secret
subspace of a higher-dimensional space. The receiver applies a reciprocal
linear transform to the receive signal, which (i) raises the legitimate
transmit signal from its secret subspace and (ii) provably transforms any
jammer into a barrage jammer, which makes estimation and mitigation via MIMO
processing straightforward. We show the efficacy of MASH for data transmission
in the massive multi-user MIMO uplink.Comment: submitted to Asilomar 202
A critical review of cyber-physical security for building automation systems
Modern Building Automation Systems (BASs), as the brain that enables the
smartness of a smart building, often require increased connectivity both among
system components as well as with outside entities, such as optimized
automation via outsourced cloud analytics and increased building-grid
integrations. However, increased connectivity and accessibility come with
increased cyber security threats. BASs were historically developed as closed
environments with limited cyber-security considerations. As a result, BASs in
many buildings are vulnerable to cyber-attacks that may cause adverse
consequences, such as occupant discomfort, excessive energy usage, and
unexpected equipment downtime. Therefore, there is a strong need to advance the
state-of-the-art in cyber-physical security for BASs and provide practical
solutions for attack mitigation in buildings. However, an inclusive and
systematic review of BAS vulnerabilities, potential cyber-attacks with impact
assessment, detection & defense approaches, and cyber-secure resilient control
strategies is currently lacking in the literature. This review paper fills the
gap by providing a comprehensive up-to-date review of cyber-physical security
for BASs at three levels in commercial buildings: management level, automation
level, and field level. The general BASs vulnerabilities and protocol-specific
vulnerabilities for the four dominant BAS protocols are reviewed, followed by a
discussion on four attack targets and seven potential attack scenarios. The
impact of cyber-attacks on BASs is summarized as signal corruption, signal
delaying, and signal blocking. The typical cyber-attack detection and defense
approaches are identified at the three levels. Cyber-secure resilient control
strategies for BASs under attack are categorized into passive and active
resilient control schemes. Open challenges and future opportunities are finally
discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC