Article
Computer Science, Information Systems
Seyed Mohammad AghamirMohammadAli, Behnam Momeni, Solmaz Salimi, Mehdi Kharrazi
Summary: This article introduces Oxpecker, a virtual machine introspection platform that allows for active modification of the VM's internal state. Oxpecker can detect and neutralize malware threats in the guest OS by monitoring VM state changes. A tool based on Oxpecker is also developed to terminate guest VM processes.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
M. Benchadi Djafer Yahia, Bojan Batalo, Kazuhiro Fukui
Summary: In this paper, a new framework for classifying and visualizing malware files using subspace-based methods is proposed. The framework utilizes representative image patterns to analyze malware features and uses subspace representation and occlusion sensitivity analysis for visualization. The proposed methods outperform previous techniques and demonstrate high accuracy in malware classification.
Article
Computer Science, Theory & Methods
Zhi Zhang, Yueqiang Cheng, Yansong Gao, Surya Nepal, Dongxi Liu, Yi Zou
Summary: In recent years, the deployment of virtualization techniques has become more widespread. Hardware-assisted virtualization has significantly enhanced transparency and difficulty in detection. The study identified three low-level inconspicuous features that can be leveraged to effectively detect hardware-assisted virtualization.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Article
Computer Science, Artificial Intelligence
Muhammad Irfan Yousuf, Izza Anwer, Ayesha Riasat, Khawaja Tahir Zia, Suhyun Kim
Summary: The researchers propose a static malware detection system that can detect Portable Executable (PE) malware in Windows environment with high accuracy. By collecting malware samples and extracting relevant information, they combine machine learning, ensemble learning, and dimensionality reduction techniques to construct a system with a detection rate of 99.5% and an error rate of only 0.47%.
PEERJ COMPUTER SCIENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Mohammad Nadim, David Akopian, Wonjun Lee
Summary: The kernel is the core part of a computer operating system, and kernel-level rootkits present a significant security threat by hiding their presence and malicious activities. Detection systems based on learning are effective in automatically detecting both known and unknown attacks.
2021 7TH INTERNATIONAL CONFERENCE ON ENGINEERING AND EMERGING TECHNOLOGIES (ICEET 2021)
(2021)
Article
Computer Science, Information Systems
Mulhem Ibrahim, Bayan Issa, Muhammed Basheer Jasser
Summary: Android is dominating the global smartphone market, leading to a strong need for effective security measures. This research proposes a new method for detecting and classifying Android malware using deep learning models and static analysis, achieving high accuracy in malware detection and classification.
Review
Computer Science, Artificial Intelligence
Rosmalissa Jusoh, Ahmad Firdaus, Shahid Anwar, Mohd Zamri Osman, Mohd Faaizie Darmawan, Mohd Faizal Ab Razak
Summary: Android is a free open-source operating system widely used by manufacturers to produce mobile devices, but unethical authors often develop malware for various purposes. While practitioners conduct intrusion detection analyses like static analysis, there is a lack of review articles discussing research efforts in this area.
PEERJ COMPUTER SCIENCE
(2021)
Review
Computer Science, Information Systems
Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md. Zahidul Islam
Summary: This study comprehensively reviews the evolution and current attack trends of malware, and explores the corresponding defense strategies. The findings highlight the increasing sophistication of malware attacks and the need for multilayered security measures. Despite advancements, there are still challenges and research gaps.
Article
Computer Science, Hardware & Architecture
Jinchun Choi, Afsah Anwar, Abdulrahman Alabduljabbar, Hisham Alasmary, Jeffrey Spaulding, An Wang, Songqing Chen, DaeHun Nyang, Amro Awad, David Mohaisen
Summary: This paper analyzes IoT malware and focuses on endpoints reachable on the public Internet, revealing patterns of affinity between sources and targets of attacks and the exposure of attacks by Internet infrastructure. This investigation provides profound insights into the role of endpoints in IoT malware attacks, deepening our understanding of IoT malware ecosystems and aiding future defenses.
Article
Computer Science, Information Systems
Daniel Gibert, Carles Mateu, Jordi Planes, Joao Marques-Silva
Summary: Malicious software poses a serious threat on the internet, with traditional detection methods struggling to keep up. Machine learning and deep learning engines have shown promise in handling complex malware and new variants effectively. Further research is needed to improve classification performance and vulnerabilities to adversarial examples.
COMPUTERS & SECURITY
(2021)
Article
Automation & Control Systems
Sumit Kumar, S. Indu, Gurjit Singh Walia
Summary: An optimal solution for the unification of static and dynamic features in Android smartphones is proposed to detect malicious applications. Experimental results show that the suggested solution outperforms existing methods.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Syed Khurram Jah Rizvi, Warda Aslam, Muhammad Shahzad, Shahzad Saleem, Muhammad Moazam Fraz
Summary: Enterprises are facing challenges in detecting malware through static analysis due to the exponential growth of malware. To address this, machine learning aided static analysis for malware detection has become a focus of research to achieve early stage detection and improve accuracy.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Software Engineering
Matin Katebi, Afshin RezaKhani, Saba Joudaki, Mohammad Ebrahim Shiri
Summary: This article proposes RAPSAMS, a method that extends affinity propagation clustering to robustly cluster malware streams. The approach uses AP for clustering samples and introduces adversarial examples to attack the clustering algorithm and create a robust defense. The proposed method addresses the challenges of finding appropriate representations for clustering and managing patterns with different distributions. Experimental results demonstrate the adaptability and effectiveness of the proposed methods. AP clustering is shown to be robust against label flipping attacks.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Engineering, Civil
Mohamed Hedi Baccour, Frauke Driewer, Tim Schaeck, Enkelejda Kasneci
Summary: Driver drowsiness poses a significant threat to road safety. This study compares the performance of indirect and direct driver monitoring systems (DMSs) in detecting drowsiness. The direct DMS, utilizing a driver monitoring camera, outperforms the indirect DMS, which uses vehicle-based features, achieving a balanced accuracy of 87.1% compared to 77.9%. The hybrid DMS, combining vehicle-based and driver-based features, achieves a slightly higher balanced accuracy of 87.7%. This work emphasizes the importance of developing and using direct or hybrid DMSs to enhance road safety.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Chandra Shekhar Yadav, Jagendra Singh, Aruna Yadav, Himansu Sekhar Pattanayak, Ravindra Kumar, Arfat Ahmad Khan, Mohd Anul Haq, Ahmed Alhussen, Sultan Alharby
Summary: The Internet of Things (IoT) and Android operating system have made advanced technology accessible to the general public. This article provides a comprehensive study of the IoT and Android systems, including different attack classifications and mitigation strategies. It highlights the importance of secure application design and explores malware detection methods. This study expands the understanding of application-hardening strategies and aims to help experts and researchers design more efficient and robust solutions. It also discusses attack vectors and mitigation strategies for developers and the open domain.
Article
Computer Science, Software Engineering
Sadegh Momeni Milajerdi, Mehdi Kharrazi
JOURNAL OF SYSTEMS AND SOFTWARE
(2015)
Article
Computer Science, Interdisciplinary Applications
B. Momeni, M. Kharrazi
JOURNAL OF SIMULATION
(2016)
Article
Education, Scientific Disciplines
Behnam Momeni, Mehdi Kharrazi
IEEE TRANSACTIONS ON EDUCATION
(2012)
Article
Computer Science, Theory & Methods
Mohammad Hashem Haghighat, Mehdi Tavakoli, Mehdi Kharrazi
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2013)
Article
Computer Science, Software Engineering
Behnam Momeni, Mehdi Kharrazi
SOFTWARE-PRACTICE & EXPERIENCE
(2018)
Article
Engineering, Multidisciplinary
B. Momeni, M. Kharrazi
Article
Engineering, Multidisciplinary
E. Soltanaghaei, M. Kharrazi
Article
Engineering, Electrical & Electronic
Mehdi Kharrazi, Husrev T. Sencar, Nasir Mernon
JOURNAL OF ELECTRONIC IMAGING
(2006)
Article
Engineering, Electrical & Electronic
I Avcibas, M Kharrazi, N Memon, B Sankur
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
(2005)