Search

As the complexity and integration of electronic devices increase, understanding and mitigating side-channel vulnerabilities will remain a critical area of cybersecurity research. The new and intriguing software-based thermal side-channel attacks and countermeasures use thermal emissions from a device to extract or defend sensitive information, by reading information from the built-in thermal sensors via software. This work extends the Hot-n-Cold anomaly detection technique, applying it in circumstances much closer to the real-world computational environments by detecting irregularities in the Linux command behavior through CPU temperature monitoring. The novelty of this approach lies in the introduction of five types of noise across the CPU, including moving files, performing extended math computations, playing songs, and browsing the web while the attack detector is running. We employed Hot-n-Cold to monitor core temperatures on three types of CPUs utilizing two commonly used Linux terminal commands, ls and chmod. The results show a high correlation, approaching 0.96, between the original Linux command and a crafted command, augmented with vulnerable system calls. Additionally, a Machine Learning algorithm was used to classify whether a thermal trace is augmented or not, with an accuracy of up to 88%. This research demonstrates the potential for detecting attacks through thermal sensors even when there are different types of noise in the CPU, simulating a real-world scenario. [ABSTRACT FROM AUTHOR]

As the complexity and integration of electronic devices increase, understanding and mitigating side-channel vulnerabilities will remain a critical area of cybersecurity research. The new and intriguing software-based thermal side-channel attacks and countermeasures use thermal emissions from a device to extract or defend sensitive information, by reading information from the built-in thermal sensors via software. This work extends the Hot-n-Cold anomaly detection technique, applying it in circumstances much closer to the real-world computational environments by detecting irregularities in the Linux command behavior through CPU temperature monitoring. The novelty of this approach lies in the introduction of five types of noise across the CPU, including moving files, performing extended math computations, playing songs, and browsing the web while the attack detector is running. We employed Hot-n-Cold to monitor core temperatures on three types of CPUs utilizing two commonly used Linux terminal commands, ls and chmod. The results show a high correlation, approaching 0.96, between the original Linux command and a crafted command, augmented with vulnerable system calls. Additionally, a Machine Learning algorithm was used to classify whether a thermal trace is augmented or not, with an accuracy of up to 88%. This research demonstrates the potential for detecting attacks through thermal sensors even when there are different types of noise in the CPU, simulating a real-world scenario.

From general systems to mission-critical systems at financial and government institutions, the application scope of cloud computing services is continuously expanding. Therefore, there is a need for better methods to ensure the stability and security of the cloud data and services. Monitoring the abnormal behavior of virtual machines (VMs) is one of the most-important means to identify the causes of security incidents related to the cloud. However, current traditional abnormal-behavior-detection methods for VMs on cloud platforms face multiple challenges such as privacy protection and the semantic gap. Virtualization technology plays a key role in cloud computing. Meanwhile, virtualization security is the core issue of cloud computing security as well. To address these issues, this paper proposes a feature-fusion-based abnormal-behavior-detection method (FFABD) in a virtualization environment. This method acquires the hardware features and syscalls of the VM at the physical machine level and the virtualization level, respectively. Therefore, this method is not limited by the operating system running on the VM. This makes our method more efficient and universally applicable compared to traditional abnormal-VM-detectionmethods. The ensemble learning model performs the best among all the models, achieving an Accuracy of 99.7%. [ABSTRACT FROM AUTHOR]