Khaled N. Khasawneh

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Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use(More)
Security exploits and ensuant malware pose an increasing challenge to computing systems as the variety and complexity of attacks continue to increase. In response, software-based malware detection tools have grown in complexity, thus making it computationally difficult to use them to protect systems in real-time. Therefore, software detectors are applied(More)
General Purpose Graphics Processing Units (GPGPUs) are present in most modern computing platforms. They are also increasingly integrated as a computational resource on clusters, data centers, and cloud infrastructure, making them possible targets for attacks. We present a first study of covert channel attacks on GPGPUs. GPGPU attacks offer a number of(More)
Hardware Malware Detectors (HMDs) have recently been proposed as a defense against the proliferation of malware. These detectors use low-level features, that can be collected by the hardware performance monitoring units on modern CPUs to detect malware as a computational anomaly. Several aspects of the detector construction have been explored, leading to(More)
Recently, side-channel attacks on Last Level Caches (LLCs) were demonstrated. The attacks require the ability to evict critical data from the cache hierarchy, making future accesses visible. We propose Relaxed Inclusion Caches (RIC), a low-complexity cache design protecting against LLC side channel attacks. RIC relaxes inclusion when it is not needed,(More)
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