data

Two papers gain recognition in the hardware security field

Rena Malai — Fri, 25 Feb 2022 19:28:12 +0000

Two papers gain recognition in the hardware security field Rena Malai Fri, 02/25/2022 - 14:28

In This Story

Khaled N. Khasawneh (at right), assistant
professor in the Electrical and Computer Engineering Department, works alongside PHD student Behnam Omidi.

Khaled N. Khasawneh, assistant professor in the Electrical and Computer Engineering Department at ��AV, contributed significant research and work to two papers which have been recognized in the area of hardware security.

Researchers in Khasawneh’s CAMLsec Lab Identified a New Vulnerability in the Cloud Scheduler

A paper entitled “Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks” was written as part of a collaborative research effort with University of California (UC)- Davis and ��AV. Along with his collaborators at UC Davis, Khasawneh helped to discover a new vulnerability in cloud schedulers, which could inadvertently allow targeted micro-architectural attacks in the cloud. This paper pointed out that certain features in the cloud scheduler enables arbitrary users to influence scheduling results. This can help attackers co-locate attacker’s code with a targeted victim’s code in a heterogeneous cloud, which enables a wide variety of micro-architectural attacks that leak sensitive data.

“The current design of scheduling algorithms in the cloud focuses on enhancing workloads performance, resource utilization, and load-balancing without security considerations, which may bring new vulnerability as we showed in our work,” says Khasawneh.

The paper is accepted for publication at the 2022 Network and Distributed System Security Symposium (NDSS), and will be presented there in April.

This paper is co-authored with Behnam Omidi, a second year PHD student within the Electrical and Computer Engineering Department. His research focuses on discovering systems vulnerabilities and hardware support to secure computing systems.

Khasawneh—Omidi’s advisor-- says this paper was a great opportunity to gain useful knowledge as it was a tremendous collaborative effort between both schools.

“I’m more of an expert in microarchitecral attacks, and our co-collaborators at UC Davis, led by Dr. Homayoun, brings expertise of cloud computing and resource scheduling,” says Khasawneh.

Khasawneh’s Paper Selected as a Top Pick in Architecture and Hardware Security 2021

A paper co-authored by Khasawneh was selected as a Top Pick in Architecture and Hardware Security 2021.

The top picks in architecture and embedded security represent the top and most impactful papers that have been published in the area in the last six years, from 2015 to 2020. Top picks are selected from conference papers that have appeared in leading hardware security conferences including but not limited to DAC, ICCAD, DATE, ASPDAC, HOST, Asian HOST, GLSVLSI, VLSI Design, CHES, ETS, VTS, ITC, S&P, Usenix Security, CCS, NDSS, ISCA, MICRO, ASPLOS, HPCA, HASP, ACSAC, Euro S&P, and Asia CCS.

The top pick paper is titled “Spectre Returns! Speculation Attacks using the Return Stack Buffer ” and had previously won the best paper award in the USENIX Workshop on Offensive Technologies (WOOT) in 2018. This paper discovers a new class of Spectre attack, called SpectreRSB, that exploits the return stack buffer (RSB), which is used in modern CPUs to help predict return addresses, instead of the branch predictor unit. SpectreRSB allows malicious software to steal passwords, keys, and other sensitive information, from memory it shouldn't be allowed to touch.

According to Khasawneh, Linux kernel released a patch to protect against SpectreRSB vulnerability (CVE-2018-15572).

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grants

Department of Electrical and Computer Engineering

College of Engineering and Computing

data

C-TASC

Avyuct brings clarity to data

Rena Malai — Mon, 21 Feb 2022 15:54:54 +0000

Avyuct brings clarity to data Rena Malai Mon, 02/21/2022 - 10:54

Rajesh Aggarwal, adjunct professor of data analytics engineering in the College of Engineering and Computing at ��AV, has always been fascinated by data.

“I’ve been working with data science for a long time,” says Aggarwal. “I was working on data models on my own, and decided it was an opportunity to create a company around it.”

Combining his data science training with AI and machine learning, Aggarwal started Avyuct in 2017. A Sanskrit word meaning ‘crystal clear' he says the tagline of Avyuct is exactly that—bringing crystal clear clarity to data.

“At Avyuct we build models using data science techniques and work with small satellite coordination, object detection, and anomaly detection,” Aggarwal says.

Anomaly detection---which tracks any unusual discrepancies in data—is used by a variety of industries, says Aggarwal. It’s a practical tool for targeting underlying issues within a product, concept, or project and it can be a key factor in avoiding problems and determining the best solutions.

“Anomaly detection is used by pharmaceutical companies to detect defective pills, tech companies to detect security threats, manufacturing industries to detect machine defects…it’s also an important tool to incorporate in the area of data science,” he says.

“Right now, we have a couple interesting projects,” Aggarwal says. “We are working to develop the coordination of small satellites for optimal data collection on the ground, and an anomaly detection model.”

Avyuct started as a part-time venture for Aggarwal, as he explored through data concept models in his spare time. Today, the company has grown to include full- time employees, and when space and time allows, interns.

Aggarwal plans to recruit interns during summer 2022 at Avyuct. Those interested in the opportunity can check any open positions on the Mason career portal.

For more information on Avyuct, visit https://avyuct.com.

Topics

data

data analytics

College of Engineering and Computing

Data Analytics Engineering

Mason NSF CAREER award looks at compressing and transmitting panoramic video for accurate analysis

Rena Malai — Mon, 07 Feb 2022 19:37:10 +0000

Mason NSF CAREER award looks at compressing and transmitting panoramic video for accurate analysis Rena Malai Mon, 02/07/2022 - 14:37

In This Story

Zhisheng Yan

Panoramic video footage is a useful tool to capture important information, like identifying suspects or monitoring a natural disaster response during an earthquake or wildfire.

��AV assistant professor Zhisheng Yan in the Department of Information Sciences and Technology will lead a National Science Foundation (NSF) CAREER research project called Machine-centered Cyberinfrastructure for Panoramic Video Analytics in Science and Engineering Monitoring to further develop and enhance machine centric video compression and transmission. This will look at developing a method for video footage captured by a 360 degree panoramic camera—which uses copious amounts of data—to get compressed and transmitted into a more usable, sizeable unit for data analysis in computing servers.

According to Yan, the benefit of 360 video footage is having a larger scope of footage available but transmission can be challenging without efficient compression.

“The use of 360 degree panoramic video is seen as an important tool for data collection in a variety of spaces, particularly when it comes to identifying wildlife, filming airport traffic, and suspect recognition,” says Yan. “All the views are available.”

The end consumer of 360 degree video footage is now not always a human eye, but a computer algorithm, says Yan. This is why redesigning video compression and transmission capabilities is necessary to make sure the algorithms are properly analyzing footage, and retaining the data that’s needed.

“The need to redesign compression and transmission is not necessarily for video quality, but to optimize analytic results and accuracy for the computer systems ‘viewing’ the video,” says Yan.

He adds that research done around traditional camera and video systems have shown that there are often issues when software programs are used to analyze panoramic videos that are too large for the systems to handle. This is where machine centric video compression and transmission can help these systems generate an accurate analysis.

Yan will be the single principal investigator for the project and anticipates working alongside student researchers throughout the duration. The Machine-centered Cyberinfrastructure for Panoramic Video Analytics in Science and Engineering Monitoring project will begin June 2022 and run for about five years.

Until June, Yan says he will focus on some literature reviews and preparation.

“Our first focus will be in compression technology, and then we will focus on the transmission aspect,” he says. “We’ll do testing on 360 degree panoramic video samples to see what works best.”

Topics

visual art

Information Sciences and Technology Department

College of Engineering and Computing

Media

data analytics

data

National Science Foundation

Research