Suman Kumar

Byungkwan Jung, Suman Kumar, Adityasinh Manthansinh Chauhan

Smart home automation systems represent a seamless integration of Internet of Things technologies, facilitating the monitoring, management, and regulation of various aspects of our daily life. By leveraging advancements in communication, computing, sensing, and actuator technologies, they hold promises for enhancing the living experience. However, they face challenges such as the need for timely updates, efficient data management, real-time Big data processing, robust security measures, and advanced analytics. In this paper, we propose a novel framework that capitalizes on the capabilities of 6G networks and 6G-enabled cloud computing to address these challenges and improve the overall landscape of smart cities. This framework features enhanced security, data pre-processing, big data intelligence, and security service virtualization in the cloud. Through various application scenarios and a case study-focusing on safe routing during disasters, we demonstrate the utility of this framework and the critical role 6G networks and 6G-enabled cloud computing play in smart home automation.

Melissa Pappy, Linh Nguyen, Suman Kumar et al.

Cybercrime has grown exponentially in both scale and sophistication, posing significant threats. As attack methods evolve rapidly, traditional classification schemes often fail to capture the complexity and diversity of modern threats. To address this gap, we introduce STRIKE,a Structured Taxonomy for Risk, Impact, Knowledge, and Emerging Threats, which provides a unified, multi-dimensional framework for categorizing cybercrimes. STRIKE spans both conventional and emerging domains, including ransomware, phishing, network intrusion, child sexual abuse material (CSAM), cryptojacking, deepfakes, and supply chain attacks. It organizes threats using criteria such as attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies. Alongside the taxonomy, we review recent advances in detection methodologies and present a response workflow to assist practitioners under active threat conditions. This work offers researchers, security professionals, and policymakers a practical foundation for threat analysis, comparative evaluation, and adaptive cyber defense.

Qun Liu, Suman Kumar, Vijay Mago

World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers safety in a modern technology rich transportation system.