The deployment of deep learning models for face detection in edge devices presents challenges related to computational demands, resource constraints, and security. This paper will tackle the optimization for the distribution of face detection with different trained models to various hardware capabilities with the implementation of 4 secure communication protocols. To validate our proposal, we perform matrix evaluations across different NVIDIA edge devices and GPU-enabled PCs (GTX 1070Ti and RTX 2070), measuring performance metrics such as latency, throughput, and resource usage. The results demonstrate that our framework effectively balances accuracy, speed, and efficiency, providing optimal model-protocol combinations for edge devices.
The application of machine learning and deep learning to intrusion detection systems (IDSs) enhances their ability to detect and respond to sophisticated cyber threats efficiently and effectively, providing a robust defense mechanism in the ever-evolving landscape of cybersecurity. However, many environments where IDSs are deployed, such as IoT devices or edge computing nodes, have experienced several challenges with limited computational power, memory, and storage. In this paper, we leverage Explainable Artificial Intelligence (XAI) for feature selection and Knowledge Distillation (KD) to improve performance and decrease the complexity of the model for limited-resource devices. Specifically, XAI can identify features with little to no impact on the predictions, which can be removed to reduce the dimensionality of data and simplify the model. Besides, we leverage the KD technique to transfer the knowledge from a larger model to a small one to obtain a lightweight yet powerful IDS. Moreover, a new optimizer is also utilized to achieve high accuracy and robustness, while maintaining the speed and efficiency necessary for real-time intrusion detection. The experiments on three common IDS datasets, including CIC-IDS2017, UNSW-NB15, and NSL-KDD, prove our proposed method’s effectiveness with high accuracy.
As online services continue to grow, Distributed Denial of Service (DDoS) attacks pose significant threats to network systems. While Machine Learning (ML) techniques have increased adaptability in DDoS detection, they still face challenges in capturing complex patterns in high-dimensional or non-linear network data. Deep Learning (DL) models, such as Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, have advanced detection capabilities but encounter limitations when dealing with spatial and temporal data, sequential information, and computational efficiency. To address these challenges, this study proposes the use of the Transformer model for DDoS detection, enhancing its ability to identify intricate patterns in network traffic. Additionally, we introduce a novel packet-based data preprocessing method that preserves detailed packet-level information while maintaining a comprehensive view of data flows. Together, these contributions aim to significantly improve the accuracy and performance of DDoS detection systems.
In the digital age, electronic devices and digital products have become indispensable. They play an important role in both personal and professional settings for millions of people worldwide. As our dependence on technology increases, so does the urgency of ensuring cybersecurity. Cyber threats, particularly those attacking individual users and organizations, have become more frequent, complex, and difficult to detect. Among these, malicious JavaScript code is one of the most popular tools used by attackers to gain unauthorized access to systems and compromise sensitive information. Detecting these threats requires advanced and creative solutions. This paper introduces a graph learning approach, followed the rules of Markov chains, aims at detecting and mitigating malicious JavaScript attacks. Our approach takes the advantage of the structural properties of JavaScript code, transforming it into graph representation, which allows for a more effective identification of abnormal behavior. To evaluate the performance of our model, we use two popular datasets: Hynek Petrak’s dataset, which includes a variety of malicious JavaScript samples, and the SRILAB dataset, which contains benign JavaScript examples. Our method is compared with other leading graph learning models, and the results demonstrate a notable increase in detection accuracy. This approach offers a powerful tool to strengthen cyber security defenses in facing of evolving digital threats.
This paper investigates the secrecy performance of reconfigurable intelligent surfaces (RISs)-unmanned aerial vehicles (UAVs) within a 6G communication system. Specifically, a base station employing rate-splitting multiple access (RSMA) transmits both common and private messages to Internet of Things (IoT) devices (IDs) via a UAV relay (UR). Additionally, a mobile UAV eavesdropper (UE) is considered, capable of moving to intercept signals relayed by the UR. Therefore, we address the challenge of mobile UE position tracking to enhance physical layer security (PLS). We propose a framework that integrates real-time UE tracking using a particle filter (PF), a sequential Monte Carlo, for estimating the UE’s state. By continuously updating the UE location estimates, the considered system enables a proactive security measure, such as friendly jammers, to minimize the risk of intercepted communications. Simulation results demonstrate that accurate UE tracking significantly improves secrecy capacity and reduces the likelihood of successful eavesdropping.
