Libyan Journal of Informatics

Image Encryption Based on Chaotic Logistic Map

Mahmoud Hasan — 2025-06-30

One of the most efficient methods for ensuring privacy in the digital
world is image encryption. We should facilitate and secure the transfer of private
images from one location to another when sent over a public network, such as
when sending them to be stored on a cloud drive. This paper proposed an algorithm to encrypt and decrypt the images. The approach uses a chaotic logistic
map to generate a keystream and then applies an XOR operation for the encryption and decryption of images. The findings indicate that the proposed technique
is an effective method for image encryption and decryption. We evaluate the metrics for encryption performance analysis: information entropy, histogram analysis, correlation coefficients between plain and cipher images, energy, contrast,
and homogeneity, and the analysis of the key's sensitivity. The results indicate
that the proposed technique demonstrates efficacy.

Evaluating Redundancy and Failure Detection.

Mai Elbaabaa — 2025-06-30

The exponential growth of the Internet and its integration into daily life underscore the critical importance of resilient networks. Service outages can cause significant financial losses and damage reputation. First-hop redundancy protocols (FHRPs) are commonly used to enhance virtual gateway resilience and reduce downtime, but they can suffer from slow failure detection, leading to packet loss. Bidirectional routing detection (BFD) provides a rapid mechanism for link failure detection and connectivity monitoring.

This paper explores the intricate landscape of network reliability, investigates the benefits of combining BFD with three prominent FHRPs (HSRP, VRRP, and GLBP) to improve network performance, increase availability, and reduce downtime. The evaluation is based on metrics of convergence time, packet loss, CPU utilization, and bandwidth consumption. Results from PNETLAB simulations indicate that using BFD greatly speeds up the detection of failures and reduces packet loss for all three protocols. GLBP achieved the fastest convergence, while VRRP exhibited the lowest CPU utilization. The findings indicate that the integration of Bidirectional Forwarding Detection (BFD) with First Hop Redundancy Protocol (FHRP) gateways significantly enhance network convergence times, thereby improving overall network reliability and stability.

Improving Security for the Libyan E-Government Portal

Hamdi Ahmed Jaber — 2025-06-30

The expansion of digital services in Libya has created an urgent need to secure access to sensitive government portals. This study proposes an enhanced multi-factor authentication system within the Libyan e-Government Portal Project by leveraging the government’s policy of linking Subscriber Identification Module (SIM) cards to the National Identity Number (NID) database for all Libyan citizens' mobile phone users. This linkage adds another authentication layer as an identity verification factor, leveraging the solution to the multi-factor authentication (MFA) level, ensuring that only users with verified identities can register mobile numbers to receive one-time passwords (OTPs).
Furthermore, this research highlights the successful implementation of NID-SIM linkage by the Central Bank of Libya (CBL) within their Foreign Currency Management System (FCMS) authentication process, providing a practical demonstration of the effectiveness of this linkage. A comparative analysis of authentication methods shows that using OTP via SMS authentication with NID-SIM linkage is a secure approach, thereby ensuring the integrity of sensitive government services. The proposed system addresses the common challenges facing traditional two-factor authentication systems, thereby enhancing

A Malware Detection and Classification using Artificial Neural Networks

Mohammed Abosaeeda — 2025-06-30

The rapid evolution of malware, particularly polymorphic and metamorphic variants, has rendered traditional detection methods, such as signature-based and behavioural detection, increasingly ineffective. This paper's objective is a comprehensive review of Artificial Neural Networks (ANNs) for malware detection and classification via a comprehensive review of the most widely used ANNs. The study focuses on supervised models, unsupervised models, and hybrid architectures across diverse environments. The study results indicate that the supervised models achieve exceptional accuracy (>95%); the unsupervised models offer interpretability and adaptability to evolving threats but face challenges in generalising to unseen data. Conversely, hybrid models combine spatial and temporal feature extraction, achieving 99.4% accuracy, albeit with higher computational costs. This study emphasises the importance of the need for robust frameworks against obfuscation, efficient architectures for resource-constrained environments, and enhanced generalisation across malware families.