arXiv subjects

Majd Latah

Publications and source records attributed to Majd Latah.

7 recordsLinked to original sources

arXiv2026

AgenticNet: Utilizing AI Coding Agents To Create Hybrid Network Experiments

Traditional network experiments focus on validation through either simulation or emulation. Each approach has its own advantages and limitations. In this work, we present a new tool for next-generation network experiments created through Artificial Intelligence (AI) coding agents. This tool facilitates hybrid network experimentation through simulation and emulation capabilities. The tool supports three main operation modes: pure simulation, pure emulation, and hybrid mode. AgenticNet provides a more flexible approach to creating experiments for cases that may require a combination of simulation and emulation. In addition, AgenticNet supports rapid development through AI agents. We experimentally evaluate the tool and present an approach to verify the generated code.

cs.NI↗

arXiv2026

OVS Meets PQ-TLS: Exploring Post-Quantum TLS for SDN's Southbound API

Software-defined networking (SDN) is a novel networking paradigm that enables network programmability and centralized control for network devices. The southbound application programming interface (API) is used to control and manage the underlying data plane devices. The existing southbound API relies on TLS with legacy cryptographic algorithms such as RSA and ECDSA. In this paper, we explore the performance of the southbound API with postquantum TLS (PQ-TLS) support. We present a proof-of-concept of using PQ-TLS in SDN's southbound API. We study the performance of pure and hybrid PQ-TLS modes across different security levels and compare them with legacy TLS in terms of latency and CPU utilization. We also compare the performance of different post-quantum signature and key establishment schemes.

cs.NI↗

arXiv2019

The Art of Social Bots: A Review and a Refined Taxonomy

Social bots represent a new generation of bots that make use of online social networks (OSNs) as a command and control (C\&C) channel. Malicious social bots were responsible for launching large-scale spam campaigns, promoting low-cap stocks, manipulating user's digital influence and conducting political astroturf. This paper presents a detailed review on current social bots and proper techniques that can be used to fly under the radar of OSNs defences to be undetectable for long periods of time. We also suggest a refined taxonomy of detection approaches from social network perspective, as well as commonly used datasets and their corresponding findings. Our study can help OSN administrators and researchers understand the destructive potential of malicious social bots and can improve the current defence strategies against them.

cs.CR↗

arXiv2018

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

cs.AI↗

arXiv2018

Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks

Software-defined networking (SDN) is a new paradigm that allows developing more flexible network applications. SDN controller, which represents a centralized controlling point, is responsible for running various network applications as well as maintaining different network services and functionalities. Choosing an efficient intrusion detection system helps in reducing the overhead of the running controller and creates a more secure network. In this study, we investigate the performance of the well-known anomaly-based intrusion detection approaches in terms of accuracy, false alarm rate, precision, recall, f1-measure, area under ROC curve, execution time and Mc Nemar's test. Precisely, we focus on supervised machine-learning approaches where we use the following classifiers: Decision Trees (DT), Extreme Learning Machine (ELM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Neural Networks (NN), Support Vector Machines (SVM), Random Forest (RT), K Nearest-Neighbour (KNN), AdaBoost, RUSBoost, LogitBoost and BaggingTrees where we employ the well-known NSL-KDD benchmark dataset to compare the performance of each one of these classifiers.

cs.CR↗

arXiv2018

When deep learning meets security

Deep learning is an emerging research field that has proven its effectiveness towards deploying more efficient intelligent systems. Security, on the other hand, is one of the most essential issues in modern communication systems. Recently many papers have shown that using deep learning models can achieve promising results when applied to the security domain. In this work, we provide an overview for the recent studies that apply deep learning techniques to the field of security.

cs.CR↗

arXiv2018

An Efficient Flow-based Multi-level Hybrid Intrusion Detection System for Software-Defined Networks

Software-Defined Networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element in the SDN paradigm is the controller, which is responsible for managing the flows of each correspondence forwarding element (switch or router). Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system. Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system. For the first level, we employ the k-Nearest Neighbor approach (kNN); for the second level, we use the Extreme Learning Machine (ELM); and for the remaining levels, we utilize the Hierarchical Extreme Learning Machine (H-ELM) approach. In comparison with conventional supervised machine learning algorithms based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves the highest level of accuracy (84.29%). Therefore, our approach presents an efficient approach for intrusion detection in SDNs.

cs.CR↗