Volumetric Distributed Denial of Service (DDoS) attacks such as the SYN flood and UDP flood attacks are designed to cause service outage by overwhelming the resources of the targeted host or network. Early detection of such attacks using computationally frugal means is highly desirable. Therefore, we propose a hybrid, fast and reliable approach to volumetric DDoS attack detection. Our hybrid model uses Machine Learning (ML) based feature selection and Fuzzy Cognitive Map (FCM) based attack prediction (classification). We test nine different Machine Learning (ML) feature selection algorithms to first identify a set of ten features from SYN and UDP flood attack packets that serve as reliable indicators of the attack. Next, we construct FCMs with these selected features and with algorithmically assigned and updated weights; these FCMs can predict with high accuracy, whether each packet is an attack packet or a benign packet. We test our model on the publicly available CICDDoS2019 dataset to demonstrate that our FCM based attack prediction is very effective when paired with a few specific feature selection algorithms. We also demonstrate that our “ML-feature-select, FCM-predict” hybrid approach is more reliable and computationally much faster than the traditional “ML train and test” approach within the context of SYN and UDP flood attack prediction. A light-weight solution such as ours is suitable for various edge devices including resource constraint IoT hosts.
The objective of this article is to propose a new generation of security tailored to future networks, particularly 6G. Several demonstrators have been carried out to test the feasibility of implementing this new security architecture. These demonstrators need to be improved and tested on different platforms to optimize the final architecture. This new generation is based on two key paradigms: 1. The development of a new generation of low-cost, energy-efficient HSMs, enabling all smart devices to be equipped with them. 2. The establishment of a federated blockchain based on HSMs embedded in the network. Finally, the article outlines how these two paradigms can be used to implement a security technology for large networks, such as 6G, through micro-segmentation and security protocols capable of managing access to segments, all with the aim of providing high-level security at a low cost.
Access Control List (ACL) management in complex, distributed network environments poses significant challenges for organizations relying on heterogeneous infrastructures. This paper proposes a novel architecture leveraging Infrastructure as Code principles, containerization, and Kubernetes orchestration to automate and streamline ACL management at scale. Our solution incorporates a CI/CD pipeline for ACL generation, utilizing Capirca for platform-agnostic policy definition and Docker for consistent packaging. A Kubernetes Deployment Controller manages the safe rollout of ACLs across diverse network devices, employing a phased approach with canary deployments. A Drift Detection Controller ensures continuous compliance by monitoring and rectifying unauthorized changes. The architecture integrates with external systems like NetBox for efficient device inventory management. By automating the entire ACL lifecycle, our approach significantly reduces manual errors, enhances security posture, and improves operational efficiency. Performance evaluation reveals strong scalability, with optimization opportunities for large-scale deployments. This work contributes to the evolving field of network security automation, offering a framework for managing network security policies in modern, complex infrastructures.
The traditional approach to software security often fails to detect vulnerabilities early enough. This research explores how artificial intelligence (AI) can be integrated into the software development cycle to improve the identification of vulnerabilities and anti patterns. In the context of DevSecOps, we propose a new methodology that detects antipatterns automatically using machine learning and natural language processing techniques. Also, we explore the possibility of using a large language model (LLM) in a CI (Continuous Integration) and CD (Continuous Delivery) pipeline, to allow the developers real-time feedback, with it the initial phases of the software development cycle can improve the security and quality of the software. Further study is necessary to determine the AI model's true capability in managing real-world challenges.
Passwords are the most widely used authentication method and play a crucial role in the field of information security. In this study, we explore the effectiveness of applying machine learning (ML) and natural language processing (NLP) techniques to password classification. We compare the performance of classifiers by using eight ML techniques and four NLP techniques to classify user-created passwords. The experimental results show that the classifier using a combination of Bag-of-Words and Logistic Regression outperforms other classifiers, achieving an accuracy of 98.53% and a recall for weak passwords of 99.68%.
