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Advances in chip manufacturing technologies have enabled computer architects to utilize System-on-Chip (SoC) to integrate the intellectual property cores as well as other components. Network-on-Chip (NoC) is widely used to fulfill communication requirements in SoC architectures. Securing NoC is vital for designing trustworthy SoCs. Eavesdropping attacks can exploit NoC vulnerabilities to extract secret information. In this paper, we propose a machine learning based detection of eavesdropping attacks. Our machine learning models are trained offline and have been used for runtime detection with a collective decision making strategy. Experimental results demonstrate that our approach can provide high accuracy with minimal overhead. 

Network-on-Chip (NoC) fulfills the communication requirements of modern System-on-Chip (SoC) architectures. Due to the resource-constrained nature of NoC-based SoCs, it is a major challenge to secure on-chip communication against eavesdropping attacks using traditional encryption methods. In this paper, we propose a lightweight encryption technique using chaffing and winnowing (C&W) with all-or-nothing transform (AONT) that benefits from the unique NoC traffic characteristics. Our experimental results demonstrate that our proposed encryption technique provides the required security with significantly less area and energy overhead compared to the state-of-the-art approaches. 

State-of-the-art System-on-Chip (SoC) designs consist of many Intellectual Property (IP) cores that interact using a Network-on-Chip (NoC) architecture. SoC designers increasingly rely on global supply chains for obtaining third-party IPs. In addition to inherent vulnerabilities associated with utilizing third-party IPs, NoC based SoCs enable attackers to exploit the distributed nature of NoC and its connectivity with various IPs to launch a plethora of attacks. Specifically, Denial-of-Service (DoS) attacks pose a serious threat in degrading the SoC performance by flooding the NoC with unnecessary packets. In this paper, we present a machine learning-based runtime monitoring mechanism to detect DoS attacks. The models are statically trained and used for runtime attack detection leading to minimum runtime performance overhead. Our approach is capable of detecting DoS attacks with high accuracy, even in the presence of unpredictable NoC traffic patterns caused by various application mappings. We extensively explore machine learning models and features to provide a comprehensive study on how to use machine learning for DoS attack detection in NoC-based SoCs. 

Multithreaded applications are capable of exploiting the full potential of many-core systems. However, network-on-chip (NoC)-based intercore communication in many-core systems is responsible for 60%-75% of the miss latency experienced by multithreaded applications. Delay in the arrival of critical data at the requesting core severely hampers performance. This brief presents some interesting insights about how critical data are requested from the memory by multithreaded applications. Then it investigates the cause of delay in NoC and how it affects the performance. Finally, this brief shows how NoC-aware memory access optimizations can significantly improve performance. Our experimental evaluation considers early restart memory access optimization and demonstrates that by exploiting available NoC resources, critical data can be prioritized to reduce miss penalty by 11% and improve overall system performance by 9%. 

With the advances of chip manufacturing technologies, computer architects have been able to integrate an increasing number of processors and other heterogeneous components on the same chip. Network-on-Chip (NoC) is widely employed by multicore System-on-Chip (SoC) architectures to cater to their communication requirements. NoC has received significant attention from both attackers and defenders. The increased usage of NoC and its distributed nature across the chip has made it a focal point of potential security attacks. Due to its prime location in the SoC coupled with connectivity with various components, NoC can be effectively utilized to implement security countermeasures to protect the SoC from potential attacks. There is a wide variety of existing literature on NoC security attacks and countermeasures. In this article, we provide a comprehensive survey of security vulnerabilities in NoC-based SoC architectures and discuss relevant countermeasures. 

Malicious software, popularly known as malware, is a serious threat to modern computing systems. A comprehensive cybercrime study by Ponemon Institute highlights that malware is the most expensive attack for organizations, with an average revenue loss of $2.6 million per organization in 2018 (11% increase compared to 2017). Recent high-profile malware attacks coupled with serious economic implications have dramatically changed our perception of threat from malware. Software-based solutions, such as anti-virus programs, are not effective since they rely on matching patterns (signatures) that can be easily fooled by carefully crafted malware with obfuscation or other deviation capabilities. Moreover, software-based solutions are not fast enough for real-time malware detection in safety-critical systems. In this paper, we investigate promising approaches for hardware-assisted malware detection using machine learning. Specifically, we explore how machine learning can be effective for malware detection utilizing hardware performance counters, embedded trace buffer as well as on-chip network traffic analysis. 

Vehicular communication has emerged as a powerful tool for providing a safe and comfortable driving experience for users. Long Term Evolution (LTE) supports and enhances the quality of vehicular communication due to its properties such as, high data rate, spatial reuse, and low delay. However, high mobility of vehicles introduces a wide variety of security threats, including Denial-of-Service (DoS) attacks. In this paper, we propose an effective solution for real-time detection and localization of DoS attacks in an LTE-based vehicular network with mobile network components (e.g., vehicles, femto access points, etc.). We consider malicious data transmission by vehicles in two ways - using real identification (unintentional) and using fake identification. Our attack detection technique is based on data packet counter and average packet delivery ratio which helps to efficiently detect attack faster than traditional approaches. We use triangulation method for localizing the attacker, and analyze average packet delay incurred by vehicles by modelling the system as an M/M/m queue. Simulation results demonstrate that our proposed technique significantly outperforms state-of-the-art techniques.