arXiv subjects

Ali Elghariani

Publications and source records attributed to Ali Elghariani.

5 recordsLinked to original sources

arXiv2019

Architectures for Detecting Interleaved Multi-stage Network Attacks Using Hidden Markov Models

With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.

cs.CR↗

arXiv2019

A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications

One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is less than 1ms . The very low latency requirement of URLLC entails a lot of work in all networking layers. In this paper, we focus on the physical layer, and in particular, we propose a novel formulation of the massive MIMO uplink detection problem. We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Proximal Jacobian Alternating Direction Method of Multipliers (PJADMM) is used to solve the new formulated problem in an iterative manner where at every iteration all variables are updated in parallel and in a closed form expression. The proposed algorithm provides a lower complexity and much faster processing time compared to the conventional MMSE detection technique and other iterative-based techniques, especially when the number of single antenna users is close to the number of base station (BS) antennas. This improvement is obtained without any matrix inversion. Simulation results demonstrate the efficacy of the proposed algorithm in reducing detection processing time in the multi-user uplink massive MIMO setting.

cs.IT↗

arXiv2019

A Low Complexity Detection Algorithm Based on Alternating Minimization

In this paper, we propose an algorithm based on the Alternating Minimization technique to solve the uplink massive MIMO detection problem. The proposed algorithm provides a lower complexity compared to the conventional MMSE detection technique, especially when the number of user equipment (UE) antennas is close to the number of base station (BS) antennas. This improvement is obtained without any matrix inversion. Moreover, the algorithm re-formulates the maximum likelihood (ML) detection problem as a sum of convex functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Alternating Minimization is used to solve the new formulated problem in an iterative manner with a closed form solution update in every iteration. Simulation results demonstrate the efficacy of the proposed algorithm in the uplink massive MIMO setting for both coded and uncoded cases

cs.IT↗

arXiv2018

QoE-Aware Resource Allocation for Small Cells

In this paper, we study the problem of Quality of Experience (QoE) aware resource allocation in wireless systems. In particular, we consider application-aware joint Bandwidth-Power allocation for a small cell. We optimize a QoE metric for multi-user video streaming in a small cell that maintains a trade-off between maximizing the playback rate of each user and ensuring proportional fairness (PF) among users. We formulate the application-driven joint bandwidth-power allocation as a non-convex optimization problem. However, we develop a polynomial complexity algorithm, and we show that the proposed algorithm achieves the optimal solution of the proposed optimization problem. Simulation results show that the proposed QoE-aware algorithm significantly improves the average QoE. Moreover, it outperforms the weighted sum rate allocation which is the state-of-the-art physical resource allocation scheme.

cs.IT↗

arXiv2018

Two-Stage LASSO ADMM Signal Detection Algorithm For Large Scale MIMO

This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.

cs.IT↗