The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer an additional layer of network protection by detecting and reporting the infamous zero-day attacks. However, the efficiency of real-time detection systems relies on several factors, including the number of features utilized to make a prediction. Thus, minimizing them is crucial as it implies faster prediction and lower storage space. This paper utilizes recursive feature elimination with cross-validation using a decision tree model as an estimator (DT-RFECV) to select an optimal subset of 15 of UNSW-NB15’s 42 features and evaluates them using several ML classifiers, including tree-based ones, such as random forest. The proposed NIDS exhibits an accurate prediction model for network flow with a binary classification accuracy of 95.30% compared to 95.56% when using the entire feature set. The reported scores are comparable to those attained by the state-of-the-art systems despite decreasing the number of utilized features by about 65%.

This paper explores the integration of Artificial Intelligence into 6G networks, focusing on optimizing communication, resource allocation, and enhancing security. As communication systems transition from 5G to 6G, Artificial Intelligence’s role in addressing the increasing complexity of network management becomes pivotal. The paper reviews key AI technologies such as machine learning, deep learning, and reinforcement learning, demonstrating their applications in predictive maintenance, traffic management, and energy efficiency optimization. It also highlights how Artificial Intelligence enables intelligent network slicing, spectrum management, and resource allocation through dynamic algorithms. Furthermore, Artificial Intelligence-driven solutions are presented for addressing security concerns in 6G networks, focusing on intrusion detection, anomaly detection, and blockchain-based decentralized security. Challenges such as computational overhead, data privacy, and ethical concerns in implementing Artificial Intelligence in 6G systems are also discussed, along with future directions, including quantum AI and federated learning. This paper provides a comprehensive analysis of how Artificial Intelligence can enhance the capabilities of 6G networks, ensuring improved performance, security, and scalability for future communication technologies.

In the digital era, ensuring data integrity, authenticity, and confidentiality is critical amid growing interconnectivity and evolving security threats. This paper addresses key limitations of traditional steganographic methods, such as limited payload capacity, susceptibility to detection, and lack of robustness against attacks. A novel multi-layered steganographic framework is proposed, integrating Huffman coding, Least Significant Bit (LSB) embedding, and a deep learning-based encoder–decoder to enhance imperceptibility, robustness, and security. Huffman coding compresses data and obfuscates statistical patterns, enabling efficient embedding within cover images. At the same time, the deep learning encoder adds layer of protection by concealing an image within another. Extensive evaluations using benchmark datasets, including Tiny ImageNet, COCO, and CelebA, demonstrate the approach’s superior performance. Key contributions include achieving high visual fidelity with Structural Similarity Index Metrics (SSIM) consistently above 99%, robust data recovery with text recovery accuracy reaching 100% under standard conditions, and enhanced resistance to common attacks such as noise and compression. The proposed framework significantly improves robustness, security, and computational efficiency compared to traditional methods. By balancing imperceptibility and resilience, this paper advances secure communication and digital rights management, addressing modern challenges in data hiding through an innovative combination of compression, adaptive embedding, and deep learning techniques.

The demand for efficient job scheduling in cloud computing has grown significantly with the rise of dynamic and heterogeneous cloud environments. While effective in simpler systems, traditional scheduling algorithms fail to meet the complex requirements of modern cloud infrastructures. These limitations motivate the need for AI-driven solutions that offer adaptability, scalability, and energy efficiency. This paper comprehensively reviews AI-based job scheduling techniques, addressing several key research gaps in current approaches. The existing methods face challenges such as resource heterogeneity, energy consumption, and real-time adaptability in multi-cloud systems. Accordingly, the support of AI-based job scheduling in cloud computing is summarized here toward machine learning, optimization techniques, heuristic techniques, and hybrid AI models. This paper pointedly underlines the strengths and weaknesses of various approaches through deep comparative analysis and focuses on how AI will overcome traditional algorithm shortcomings. Is worth noticing that several important improvements this kind of AI-driven model provides, for example, in resource allocation, cost efficiency, energy consumption, and complex dependencies between jobs and system faults. In the end, AI-driven job scheduling seems to be a promising avenue toward effectively responding to the booming demands of cloud infrastructures. Future research should concentrate on three major outlooks: scalability, better integration of AI with traditional scheduling methods, and the use of other emerging technologies like edge computing and blockchain for better optimization of cloud-based job scheduling. The paper underscores the need for more adaptive, secure, and energy-efficient scheduling frameworks to meet the evolving challenges of cloud environments.