Dynamic malware analysis in the modern era—A state of the art survey

O Or-Meir, N Nissim, Y Elovici, L Rokach - ACM Computing Surveys …, 2019 - dl.acm.org
Although malicious software (malware) has been around since the early days of computers,
the sophistication and innovation of malware has increased over the years. In particular, the …

Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective

G Kornaros - IEEE Access, 2022 - ieeexplore.ieee.org
As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart
devices act in concert, rarely requiring human intervention, posing significant challenges in …

{TESSERACT}: Eliminating experimental bias in malware classification across space and time

F Pendlebury, F Pierazzi, R Jordaney, J Kinder… - 28th USENIX security …, 2019 - usenix.org
Is Android malware classification a solved problem? Published F1 scores of up to 0.99
appear to leave very little room for improvement. In this paper, we argue that results are …

Sok: The challenges, pitfalls, and perils of using hardware performance counters for security

S Das, J Werner, M Antonakakis… - … IEEE Symposium on …, 2019 - ieeexplore.ieee.org
Hardware Performance Counters (HPCs) have been available in processors for more than a
decade. These counters can be used to monitor and measure events that occur at the CPU …

Deepware: Imaging performance counters with deep learning to detect ransomware

GO Ganfure, CF Wu, YH Chang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In the year passed, rarely a month passes without a ransomware incident being published in
a newspaper or social media. In addition to the rise in the frequency of ransomware attacks …

Hardware-assisted malware detection and localization using explainable machine learning

Z Pan, J Sheldon, P Mishra - IEEE Transactions on Computers, 2022 - ieeexplore.ieee.org
Malicious software, popularly known as malware, is widely acknowledged as a serious
threat to modern computing systems. Software-based solutions, such as anti-virus software …

Evading behavioral classifiers: a comprehensive analysis on evading ransomware detection techniques

F De Gaspari, D Hitaj, G Pagnotta, L De Carli… - Neural Computing and …, 2022 - Springer
Recent progress in machine learning has led to promising results in behavioral malware
detection. Behavioral modeling identifies malicious processes via features derived by their …

HEAVEN: A Hardware-Enhanced AntiVirus ENgine to accelerate real-time, signature-based malware detection

M Botacin, MZ Alves, D Oliveira, A Grégio - Expert Systems with …, 2022 - Elsevier
Antiviruses (AVs) are computing-intensive applications that rely on constant monitoring of
OS events and on applying pattern matching procedures on binaries to detect malware. In …

Sok: Can we really detect cache side-channel attacks by monitoring performance counters?

W Kosasih, Y Feng, C Chuengsatiansup… - Proceedings of the 19th …, 2024 - dl.acm.org
Sharing microarchitectural components between co-resident programs leads to potential
information leaks, with devastating implications on security. Over the last decade, multiple …

Adversarial attack on microarchitectural events based malware detectors

SMP Dinakarrao, S Amberkar, S Bhat… - Proceedings of the 56th …, 2019 - dl.acm.org
To overcome the performance overheads incurred by the traditional software-based
malware detection techniques, Hardware-assisted Malware Detection (HMD) using machine …