Trends and Challenges in Ensuring Security for Low-Power and High-Performance Embedded SoCs

Rahimi, Parisa; Singh, Amit Kumar; Wang, Xiaohang; Prakash, Alok

doi:10.1109/mcsoc51149.2021.00041

Cited by 7 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Investigations into the cyclicality of network usage, especially in correlation with shifts, patient visit patterns, and procedural timings, established a clear baseline of normal operational behaviors [16]. Deviations from their norms usually signaled potential security incidents or system malfunctions, thereby necessitating sophisticated monitoring systems to ensure data integrity and continuous patient care [17,18,19]. Additionally, the high volume of data transfers involving sensitive information accentuates the need for robust cybersecurity measures tailored to these unique operational characteristics [20,8].…”

Section: Healthcare Environments Inherently Exhibited Distinct Data Flowmentioning

confidence: 99%

Novel Detection of Hospital Malware Using Network Pattern Analysis

郝品量,

Liu,

Wang

2024

Preprint

View full text Add to dashboard Cite

Advancements in machine learning offer potent solutions for cybersecurity, particularly in sensitive environments like hospital networks where the integrity and availability of data are paramount. In response to the escalating sophistication of cyber threats, a novel system utilizing adaptive machine learning algorithms was developed to detect malware with enhanced accuracy and responsiveness. This system demonstrated a notable capability to identify various types of malware, including zero-day attacks, achieving a detection accuracy of 92%. Comparative evaluations showed that this system significantly outperforms traditional signature-based systems and other contemporary machine learning-based approaches, which typically show lower accuracy and adaptability. The study revealed the importance of integrating real-time data analysis to adaptively refine detection algorithms, allowing the system to effectively respond to evolving threats, which not only enhances the security of hospital networks but also supports the broader application of intelligent, predictive cybersecurity measures in healthcare. Looking ahead, future work will focus on expanding the dataset to include more varied network conditions, integrating more diverse data sources, and employing federated learning techniques. These enhancements aim to improve scalability, enhance data privacy, and foster a collaborative security framework among healthcare institutions, further strengthening defenses against an ever-evolving landscape of cyber threats.

show abstract

Section: Healthcare Environments Inherently Exhibited Distinct Data Flowmentioning

confidence: 99%

Novel Detection of Hospital Malware Using Network Pattern Analysis

郝品量,

Liu,

Wang

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The decision module transmits a migration request packet (event 1) to the source PE (2). The source PE runs the task to be migrated until it reaches a safe migration state (3). Reaching this state, the OS stalls the task, sending a migration acknowledge (4) to the M P E .…”

Section: Os Layer Local Memorymentioning

confidence: 99%

“…Recent advances in the industry, such as the ET-SoC-1 [1], emphasize that many-core system-on-chip (MCSoC) is the predominant paradigm employed to meet high-performance computing required by Machine Learning applications or Cyber-Physical systems [2]. In this context, application testing and management of security, power consumption, and performance are important issues in MCSoCs design [3].…”

Section: Introductionmentioning

confidence: 99%

Chronos-v: a many-core high-level model with support for management techniques

Weber,

Dal Zotto,

Moraes

2023

Analog Integr Circ Sig Process

View full text Add to dashboard Cite

This work presents Chronos-V, a Many-Core System-on-Chip (MCSoC) that adopts abstract hardware modeling, executing the FreeRTOS Operating System (OS) at each processing element (PE). Chronos-V is a heterogeneous architecture with two regions: (i) General Purpose Processing Elements (GPPE), responsible for executing user applications; (ii) peripherals that provide IO capabilities or hardware acceleration to the system. Besides the standard goal of high-level models, design space exploration at early design stages with reduced simulation time, our goal is to advance the state-of-the-art in the MCSoC research field by proposing an architecture with hardware and software support for management techniques. As a case study, we present an ODA (Observe-Decide-Actuate) loop for thermal management, comparing it to a dark silicon patterning mapping in a platform with 196 PEs. Thermal maps show the benefits of using dynamic thermal management in terms of hotspot avoidance and temperature reduction.

show abstract

“…• The first and most important advantage of this work is the high bit error rate, which means that the vulnerability of the system will decrease significantly. Therefore, any possible TCC attacks can hardly happen in this methodology; • Low power consumption: one of the most important metrics of any embedded system is power consumption [51]. This is mainly because the increase in power consumption is a significant factor for elevated temperatures.…”

Section: Referencesmentioning

confidence: 99%

Selective Noise Based Power-Efficient and Effective Countermeasure against Thermal Covert Channel Attacks in Multi-Core Systems

Rahimi

Singh

Wang

2022

JLPEA

View full text Add to dashboard Cite

With increasing interest in multi-core systems, such as any communication systems, infra-structures can become targets for information leakages via covert channel communication. Covert channel attacks lead to leaking secret information and data. To design countermeasures against these threats, we need to have good knowledge about classes of covert channel attacks along with their properties. Temperature–based covert communication channel, known as Thermal Covert Channel (TCC), can pose a threat to the security of critical information and data. In this paper, we present a novel scheme against such TCC attacks. The scheme adds selective noise to the thermal signal so that any possible TCC attack can be wiped out. The noise addition only happens at instances when there are chances of correct information exchange to increase the bit error rate (BER) and keep the power consumption low. Our experiments have illustrated that the BER of a TCC attack can increase to 94% while having similar power consumption as that of state-of-the-art

show abstract

Trends and Challenges in Ensuring Security for Low-Power and High-Performance Embedded SoCs

Cited by 7 publications

References 43 publications

Novel Detection of Hospital Malware Using Network Pattern Analysis

Novel Detection of Hospital Malware Using Network Pattern Analysis

Chronos-v: a many-core high-level model with support for management techniques

Selective Noise Based Power-Efficient and Effective Countermeasure against Thermal Covert Channel Attacks in Multi-Core Systems

Contact Info

Product

Resources

About