arXiv subjects

AhmadReza Montazerolghaem

Publications and source records attributed to AhmadReza Montazerolghaem.

3 recordsLinked to original sources

arXiv2026

XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems

An Intrusion Detection System (IDS) is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT nodes to ship data to a server, adding overhead and offering no privacy guarantees. Moreover, conventional models focus solely on flagging attacks, without explaining how individual features influence those decisions. This research aims to address these dual limitations by first proposing a solution for privacy preservation and then adding explainability to the new system. We introduce an innovative framework called XAI FL-IDS, which integrates Federated Learning (FL) with Explainable AI (XAI). The XAI FL-IDS system eliminates concerns over data transfer because each node trains its data locally and only sends the necessary update parameters to the server. Additionally, all detections, both at the local node and central server levels, are scrutinized using SHapley Additive exPlanations (SHAP), providing detailed insight into the decision-making process. This system consists of a central server and 10 clients and utilizes the Edge-IIoTset dataset, which is distributed among all clients with careful attention paid to class balancing. On each client, the XGBoost model is executed on local data. The proposed method demonstrates robust efficiency and strong performance in intrusion detection, achieving an accuracy of over 99% and, at times, reaching 100%. By incorporating FL, the confidentiality of the network information on every local node is guaranteed.

cs.CR↗

arXiv2025

BSAGIoT: A Bayesian Security Aspect Graph for Internet of Things (IoT)

IoT is a dynamic network of interconnected things that communicate and exchange data, where security is a significant issue. Previous studies have mainly focused on attack classifications and open issues rather than presenting a comprehensive overview on the existing threats and vulnerabilities. This knowledge helps analyzing the network in the early stages even before any attack takes place. In this paper, the researchers have proposed different security aspects and a novel Bayesian Security Aspects Dependency Graph for IoT (BSAGIoT) to illustrate their relations. The proposed BSAGIoT is a generic model applicable to any IoT network and contains aspects from five categories named data, access control, standard, network, and loss. This proposed Bayesian Security Aspect Graph (BSAG) presents an overview of the security aspects in any given IoT network. The purpose of BSAGIoT is to assist security experts in analyzing how a successful compromise and/or a failed breach could impact the overall security and privacy of the respective IoT network. In addition, root cause identification of security challenges, how they affect one another, their impact on IoT networks via topological sorting, and risk assessment could be achieved. Hence, to demonstrate the feasibility of the proposed method, experimental results with various scenarios has been presented, in which the security aspects have been quantified based on the network configurations. The results indicate the impact of the aspects on each other and how they could be utilized to mitigate and/or eliminate the security and privacy deficiencies in IoT networks.

cs.CR↗

arXiv2021

Flow aware Forwarding in SDN Datacenters Using a Knapsack PSO Based Solution

With the rapid growth of different massive applications and parallel flow requests in Data Center Networks (DCNs), today's providers are confronting challenges in flow forwarding decisions. Since Software Defined Networking (SDN) provides fine granular control, it can be intelligently programmed to distinguish between flow requirements. The present article proposes a knapsack model in which the link bandwidth and incoming flows are modeled as a knapsack capacity and items, respectively. Furthermore, each flow consists of two size and value aspects, acquired through flow size extraction and the type of service value assigned by the SDN controller decision. Indeed, the current work splits the incoming flow size range into Type of Service (ToS) decimal value numbers. The lower the flow size category, the higher the value dedicated to the flow. Particle Swarm Optimization (PSO) optimizes the knapsack problem and first forwards the selected flows by KP-PSO, and the non-selected-flows second. To address the shortcomings of these methods in the event of dense parallel flow detection, the present study puts the link under the threshold of a 70 percent load by simultaneous requests. Experimental results indicate that the proposed method outperforms Sonum, Hedera, and ECMP in terms of flow completion time, packet loss rate, and goodput regarding flow size requirements.

cs.NI↗