Efficiency of communication between devices in any network depends on the network architecture and the routing algorithms. This efficiency vastly affects the productivity of the Internet of Things (IoT) nodes when utilized in an industrial environment. The conundrum of using conventional network architecture in an industry/factory connected by IoT devices has paved the way for development of Software Defined Networking (SDN). Traditional network architectures lack network programmability and hinders the testing and performance evaluation of new communication protocols. The use of vendor specific hardware is a major hindrance to create a network where the control is centralized for the deployment of different policies over the network. This paper aims to find an optimal path for efficient communication between the nodes present in a programmable Industrial IoT (IIoT) network. A programmable IIoT network is created and end user routing algorithms are designed to find the optimal path for communication. We integrate a SDN framework with IIoT nodes for creation of a centralized programmable network architecture and deploy custom routing algorithms over it for evaluation. To further reduce the communication latency, we incorporate Edge Computing Servers (ECS) in the network. Three routing algorithms-KOPA, COPA and MIOPA, that can be deployed over a SDN are proposed in this paper. Experimental results reveal that the proposed algorithms are efficient than the existing algorithms.

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05).

In recent years, cyber-physical systems (CPSs) have gained a lot of attention from academia, industry and government agencies, considered to be the world’s third wave of information technology, following computers and the internet

This paper presents a scalable solution for zero-sum, one-sided, partially observable stochastic games (Z-POSGs) in the realm of cybersecurity. Existing heuristic search value iteration (HSVI) methods often face significant challenges with scalability in large and complex networks. To overcome this limitation, we introduce an innovative approach that leverages core attack graphs summarized abstractions that streamline incremental strategy generation. This technique reduces the belief and action spaces, making it possible to manage large-scale networks more efficiently. By focusing the analysis on the core attack graph, our approach minimizes the necessity to process the entire network, leading to substantial reductions in time and memory requirements while maintaining solution accuracy. Our method achieves an average solution accuracy within an epsilon value of 0.68%. Additionally, experimental results show that the execution time on the core attack graph is reduced by around 45% compared to that on the original graph, showcasing its significant performance advantage. Furthermore, we successfully solved the largest instances, comprising up to 300 nodes, underscoring the scalability and efficiency of our approach in handling complex, large-scale networks. Comparative evaluations demonstrate that integrating core attack graphs with existing HSVI techniques not only enhances scalability but also preserves solution quality. These findings highlight the proposed method’s potential for effective and efficient application in large-scale cybersecurity networks, outperforming state-of-the-art solutions.