The rapid growth of sophisticated malware and the increasing diversity of computing environments have exposed critical limitations in traditional centralized malware detection systems, particularly in data privacy, scalability, and adaptability. This study proposes a privacy-preserving, collaborative malware-detection framework that leverages federated learning to improve detection accuracy while keeping sensitive data local to participating devices. The objective is to address emerging malware threats by combining behavioral and memory-based analysis within a decentralized learning paradigm. The proposed framework employs federated learning to train a global malware detection model without transferring raw data. Each client locally extracts discriminative features derived from system behavior and memory artifacts, including process activity patterns, memory access characteristics, and runtime indicators. Local deep learning models are trained independently, and only model parameters are shared with a central aggregator, which constructs an optimized global model through iterative parameter aggregation. This approach significantly reduces privacy risks and communication overhead compared to centralized training. Experimental evaluations on benchmark malware datasets demonstrate that the proposed federated approach achieves detection performance comparable to, and in some cases exceeding, that of centralized deep learning models. The results indicate improved robustness against previously unseen malware variants, with high detection accuracy and reduced false positive rates. Furthermore, privacy is preserved throughout the learning process, making the framework suitable for real-world distributed, resource-constrained environments. The findings confirm that federated learning, combined with memory and behavioral feature analysis, provides an effective, privacy-aware solution for modern malware detection. This work contributes to recent advances in cybersecurity by offering a scalable, secure, and practical detection framework that can be deployed across distributed systems, including enterprise networks and edge computing environments.

The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust comparative analysis highlights the CNN model’s outstanding performance, achieving an accuracy of 99.89%, precision of 99.90%, recall of 99.88%, and an F1 score of 99.89% in binary classification tasks, outperforming other evaluated models significantly. These results not only confirm the superior detection capabilities of CNNs in distinguishing between benign and malicious network activities but also illustrate the model’s effectiveness in multiclass classification tasks, addressing various attack vectors prevalent in IoT setups. The empirical findings from this research demonstrate deep learning’s transformative potential in fortifying network security infrastructures against sophisticated cyber threats, providing a scalable, high-performance solution that enhances security measures across increasingly complex IoT ecosystems. This study’s outcomes are critical for security practitioners and researchers focusing on the next generation of cyber defense mechanisms, offering a data-driven foundation for future advancements in IoT security strategies.

The increasing deployment of Internet of Things (IoT) devices has introduced significant security challenges, including identity spoofing, unauthorized access, and data integrity breaches. Traditional security mechanisms rely on centralized frameworks that suffer from single points of failure, scalability issues, and inefficiencies in real-time security enforcement. To address these limitations, this study proposes the Blockchain-Enhanced Trust and Access Control for IoT Security (BETAC-IoT) model, which integrates blockchain technology, smart contracts, federated learning, and Merkle tree-based integrity verification to enhance IoT security. The proposed model eliminates reliance on centralized authentication by employing decentralized identity management, ensuring tamper-proof data storage, and automating access control through smart contracts. Experimental evaluation using a synthetic IoT dataset shows that the BETAC-IoT model improves access control enforcement accuracy by 92%, reduces device authentication time by 52% (from 2.5 to 1.2 s), and enhances threat detection efficiency by 7% (from 85% to 92%) using federated learning. Additionally, the hybrid blockchain architecture achieves a 300% increase in transaction throughput when comparing private blockchain performance (1200 TPS) to public chains (300 TPS). Access control enforcement accuracy was quantified through confusion matrix analysis, with high precision and minimal false positives observed across access decision categories. Although the model presents advantages in security and scalability, challenges such as computational overhead, blockchain storage constraints, and interoperability with existing IoT systems remain areas for future research. This study contributes to advancing decentralized security frameworks for IoT, providing a resilient and scalable solution for securing connected environments.