arXiv subjects

Mounir Bensalem

Publications and source records attributed to Mounir Bensalem.

23 records · Page 2Linked to original sources

arXiv2021

The Role of Intent-Based Networking in ICT Supply Chains

The evolution towards Industry 4.0 is driving the need for innovative solutions in the area of network management, considering the complex, dynamic and heterogeneous nature of ICT supply chains. To this end, Intent-Based networking (IBN) which is already proven to evolve how network management is driven today, can be implemented as a solution to facilitate the management of large ICT supply chains. In this paper, we first present a comparison of the main architectural components of typical IBN systems and, then, we study the key engineering requirements when integrating IBN with ICT supply chain network systems while considering AI methods. We also propose a general architecture design that enables intent translation of ICT supply chain specifications into lower level policies, to finally show an example of how the access control is performed in a modeled ICT supply chain system.

cs.NI↗

arXiv2020

Jamming-Aware Control Plane in Elastic Optical Networks

Physical layer security is essential in optical networks. In this paper, we study a jamming-aware control plane, in which a high power jamming attack exists in the network. The studied control plane considers that the jammed connections can be detected and avoided. We used a physical layer model, in which we embedded the additional jamming power, to evaluate different security in scenarios, such as a jamming-free scenario, jamming with an unaware controller, and jamming with an aware controller. The performance is analyzed in terms of the blocking rate and slots utilization. We analyze the impact of jamming attacks in the least used link and in the most used link on the network. The results demonstrates that the jamming avoidance by the control plane can reach performance near the not jammed scenario.

cs.NI↗

arXiv2020

On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks

Optical networks are prone to power jamming attacks intending service disruption. This paper presents a Machine Learning (ML) framework for detection and prevention of jamming attacks in optical networks. We evaluate various ML classifiers for detecting out-of-band jamming attacks with varying intensities. Numerical results show that artificial neural network is the fastest (10^6 detections per second) for inference and most accurate (~ 100 %) in detecting power jamming attacks as well as identifying the optical channels attacked. We also discuss and study a novel prevention mechanism when the system is under active jamming attacks. For this scenario, we propose a novel resource reallocation scheme that utilizes the statistical information of attack detection accuracy to lower the probability of successful jamming of lightpaths while minimizing lightpaths' reallocations. Simulation results show that the likelihood of jamming a lightpath reduces with increasing detection accuracy, and localization reduces the number of reallocations required

cs.NI↗

arXiv2020

Embedding Jamming Attacks into Physical Layer Models in Optical Networks

Optical networks are prone to physical layer attacks, in particular the insertion of high jamming power. In this paper, we present a study of jamming attacks in elastic optical networks (EON) by embedding the jamming into the physical layer model, and we analyze its impact on the blocking probability and slots utilization. We evaluate our proposed model using a single link and a network topology and we show that for in-band-jamming, the slots utilization decreases with the increase of jamming power, and becomes null when the jamming power is higher than 3 dB, while for out-of-band jamming, the impact is maximal for a specific jamming power, 1.75 dB in our simulation. Considering multiple positions of attackers, we attained the highest blocking probability 32% for a specific jamming power 2 dB. We conclude that the impact of jamming depends on attacker positions as well as the jamming power.

cs.NI↗

arXiv2020

Modeling of Deep Neural Network (DNN) Placement and Inference in Edge Computing

With the edge computing becoming an increasingly adopted concept in system architectures, it is expected its utilization will be additionally heightened when combined with deep learning (DL) techniques. The idea behind integrating demanding processing algorithms in Internet of Things (IoT) and edge devices, such as Deep Neural Network (DNN), has in large measure benefited from the development of edge computing hardware, as well as from adapting the algorithms for use in resource constrained IoT devices. Surprisingly, there are no models yet to optimally place and use machine learning in edge computing. In this paper, we propose the first model of optimal placement of Deep Neural Network (DNN) Placement and Inference in edge computing. We present a mathematical formulation to the DNN Model Variant Selection and Placement (MVSP) problem considering the inference latency of different model-variants, communication latency between nodes, and utilization cost of edge computing nodes. We evaluate our model numerically, and show that for low load increasing model co-location decreases the average latency by 33% of millisecond-scale per request, and for high load, by 21%.

cs.NI↗