arXiv subjects

Amina Makhlouf

Publications and source records attributed to Amina Makhlouf.

4 recordsLinked to original sources

arXiv2026

Hybrid IDS Using Signature-Based and Anomaly-Based Detection

Intrusion detection systems (IDS) are essential for protecting computer systems and networks against a wide range of cyber threats that continue to evolve over time. IDS are commonly categorized into two main types, each with its own strengths and limitations, such as difficulty in detecting previously unseen attacks and the tendency to generate high false positive rates. This paper presents a comprehensive survey and a conceptual overview of Hybrid IDS, which integrate signature-based and anomaly-based detection techniques to enhance attack detection capabilities. The survey examines recent research on Hybrid IDS, classifies existing models into functional categories, and discusses their advantages, limitations, and application domains, including financial systems, air traffic control, and social networks. In addition, recent trends in Hybrid IDS research, such as machine learning-based approaches and cloud-based deployments, are reviewed. Finally, this work outlines potential future research directions aimed at developing more cost-effective Hybrid IDS solutions with improved ability to detect emerging and sophisticated cyberattacks.

cs.CR↗

arXiv2026

Developing and Evaluating Lightweight Cryptographic Algorithms for Secure Embedded Systems in IoT Devices

The high rate of development of Internet of Things (IoT) devices has brought to attention new challenges in the area of data security, especially within the resource-limited realm of RFID tags, sensors, and embedded systems. Traditional cryptographic implementations can be of inappropriate computational complexity and energy usage and hence are not suitable on these platforms. This paper examines the design, implementation, and testing of lightweight cryptographic algorithms that have been specifically designed to be used in secure embedded systems. A comparison of some of the state-of-the-art lightweight encryption algorithms, that is PRESENT, SPECK, and SIMON, focuses on the main performance indicators, i.e., throughput, use of memory, and energy utilization. The study presents novel lightweight algorithms that are founded upon the Feistel-network architecture and their safety under cryptanalytic attacks, e.g., differential and linear cryptanalysis. The proposed solutions are proven through hardware implementation on the FPGA platform. The results have shown that lightweight cryptography is an effective strategy that could be used to establish security and maintain performance in the IoT and other resource-limited settings.

cs.CR↗

arXiv2026

Applications of Secure Multi-Party Computation in Financial Services

The concept of Secure Multi-Party Computation (SMPC) is a cryptographic service that allows generating analysis of sensitive data related to finance under the collaboration of all stakeholders without violating the privacy of the research participants. This article shows the increasing significance of privacy protection in the contemporary financial services, where various stakeholders should comply with stringent security and regulatory standards. It discusses the main issues of scalability, computational efficiency, and working with very large datasets, and it identifies the directions of future research to make SMPC protocols more practical and efficient. The results highlight the possibility of SMPC to facilitate safe, transparent, and trustful financial transactions in an ecosystem that is becoming more digital.

cs.CR↗

arXiv2026

Exploring the Integration of Differential Privacy in Cybersecurity Analytics: Balancing Data Utility and Privacy in Threat Intelligence

To resolve the acute problem of privacy protection and guarantee that data can be used in the context of threat intelligence, this paper considers the implementation of Differential Privacy (DP) in cybersecurity analytics. DP, which is a sound mathematical framework, ensures privacy by adding a controlled noise to data outputs and thus avoids sensitive information disclosure even with auxiliary datasets. The use of DP in Security Information and Event Management (SIEM) systems is highlighted, and it can be seen that DP has the capability to protect event log and threat data analysis without interfering with the analytical efficiency. The utility versus privacy trade-offs linked to the maximization of the epsilon parameter, which is one of the critical components of DP mechanisms, is pointed out. The article shows the transformative power of DP in promoting safe sharing of data and joint threat intelligence through real-world systems and case studies. Finally, this paper makes DP one of the key strategies to improve privacy-preserving analytics in the field of cybersecurity.

cs.CR↗