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In this paper, we study the reliability of a three-state k-out-of-n:G system. We consider the situation where the system components are non-homogeneous Markov dependent, and we derive a closed-form formula for the system reliability, including increasing three-state k-out-of-n:G system and decreasing three-state k-out-of-n:G system. Our study is based on the probability generating function method. Two numerical examples are presented to demonstrate the use of the formula., Comment: 10 pages

In this paper, we present an adaptive framework designed for the continuous detection, identification and classification of emerging attacks in network traffic. The framework employs a transformer encoder architecture, which captures hidden patterns in a bidirectional manner to differentiate between malicious and legitimate traffic. Initially, the framework focuses on the accurate detection of malicious activities, achieving a perfect recall of 100\% in distinguishing between attack and benign traffic. Subsequently, the system incrementally identifies unknown attack types by leveraging a Gaussian Mixture Model (GMM) to cluster features derived from high-dimensional BERT embeddings. This approach allows the framework to dynamically adjust its identification capabilities as new attack clusters are discovered, maintaining high detection accuracy. Even after integrating additional unknown attack clusters, the framework continues to perform at a high level, achieving 95.6\% in both classification accuracy and recall.The results demonstrate the effectiveness of the proposed framework in adapting to evolving threats while maintaining high accuracy in both detection and identification tasks. Our ultimate goal is to develop a scalable, real-time intrusion detection system that can continuously evolve with the ever-changing network threat landscape., Comment: 8 pages, 11 figures

The fifth-generation (5G) offers advanced services, supporting applications such as intelligent transportation, connected healthcare, and smart cities within the Internet of Things (IoT). However, these advancements introduce significant security challenges, with increasingly sophisticated cyber-attacks. This paper proposes a robust intrusion detection system (IDS) using federated learning and large language models (LLMs). The core of our IDS is based on BERT, a transformer model adapted to identify malicious network flows. We modified this transformer to optimize performance on edge devices with limited resources. Experiments were conducted in both centralized and federated learning contexts. In the centralized setup, the model achieved an inference accuracy of 97.79%. In a federated learning context, the model was trained across multiple devices using both IID (Independent and Identically Distributed) and non-IID data, based on various scenarios, ensuring data privacy and compliance with regulations. We also leveraged linear quantization to compress the model for deployment on edge devices. This reduction resulted in a slight decrease of 0.02% in accuracy for a model size reduction of 28.74%. The results underscore the viability of LLMs for deployment in IoT ecosystems, highlighting their ability to operate on devices with constrained computational and storage resources.

Threshold cryptography has gained momentum in the last decades as a mechanism to protect long term secret keys. Rather than having a single secret key, this allows to distribute the ability to perform a cryptographic operation such as signing or encrypting. Threshold cryptographic operations are shared among different parties such that a threshold number of them must participate in order to run the operation. This makes the job of an attacker strictly more difficult in the sense that they would have to corrupt at least a threshold number of parties to breach the security. Most works in this field focus on asymmetric-key schemes that allow threshold signing or decrypting. We focus on the symmetric-key setting, allowing both threshold encryption and threshold decryption. Previous work relies on the presence of a trusted third party. Such a party may not exist in some use cases, and it represents a single point of failure. We propose to remove the requirement of a trusted third party by designing a dealer-free setup in which no entity can at any point obtain full knowledge of the secret keys. We implement a proof of concept of our construction in Python. We evaluate the proof of concept with timing metrics to compare to theoretical expectations and assess the cost in complexity of not relying on a trusted third party. While the setup phase suffers moderate additional cost, the encryption and decryption phases perform the same as the original algorithm., Comment: 13 pages, Submitted to IEEE Transactions on Information Forensics & Security

The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples presents a significant challenge. These crafted perturbations mislead ML models, enabling attackers to evade detection or trigger false alerts. As a reaction, adversarial purification has emerged as a compelling solution, particularly with diffusion models showing promising results. However, their purification potential remains unexplored in the context of intrusion detection. This paper demonstrates the effectiveness of diffusion models in purifying adversarial examples in network intrusion detection. Through a comprehensive analysis of the diffusion parameters, we identify optimal configurations maximizing adversarial robustness with minimal impact on normal performance. Importantly, this study reveals insights into the relationship between diffusion noise and diffusion steps, representing a novel contribution to the field. Our experiments are carried out on two datasets and against 5 adversarial attacks. The implementation code is publicly available.

Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant performance in well-known environments, it remains susceptible to minor condition variations, raising concerns about its reliability in real-world applications. To improve usability, DRL must demonstrate trustworthiness and robustness. A way to improve the robustness of DRL to unknown changes in the environmental conditions and possible perturbations is through Adversarial Training, by training the agent against well-suited adversarial attacks on the observations and the dynamics of the environment. Addressing this critical issue, our work presents an in-depth analysis of contemporary adversarial attack and training methodologies, systematically categorizing them and comparing their objectives and operational mechanisms., Comment: 61 pages, 17 figues, 1 table