Results 31 to 40 of about 6,055 (181)

Investigation of bypassing malware defences and malware detections [PDF]

2011 7th International Conference on Information Assurance and Security (IAS), 2011
Nowadays, malware incident is one of the most expensive damages caused by attackers. Malwares are caused different attacks, so considerations and implementations of malware defences for internal networks are important. In this papers, different techniques such as repacking, reverse engineering and hex editing for bypassing host-based Anti Virus (AV ...
Farid Daryabar, Ali Dehghantanha, Nur Izura Udzir   +2 more
openaire   +1 more source

Research on the Construction of Malware Variant Datasets and Their Detection Method

Applied Sciences, 2022
Malware detection is of great significance for maintaining the security of information systems. Malware obfuscation techniques and malware variants are increasingly emerging, but their samples and API (application programming interface) sequences are ...
Faming Lu, Zhaoyang Cai, Zedong Lin, Yunxia Bao, Mengfan Tang   +4 more
doaj   +1 more source

Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm

IEEE Access, 2020
Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems.
S. Abijah Roseline, S. Geetha, Seifedine Kadry, Yunyoung Nam   +3 more
doaj   +1 more source

Task-Aware Meta Learning-Based Siamese Neural Network for Classifying Control Flow Obfuscated Malware

Future Internet, 2023
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware ...
Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, Paul A. Watters, Seyit Camtepe   +4 more
doaj   +1 more source

Securing Linux Cloud Environments: Privacy-Aware Federated Learning Framework for Advanced Malware Detection in Linux Clouds

IEEE Access
Cloud computing is integral to modern IT infrastructure, with Linux-based virtual machines (VMs) comprising 95% of public cloud environments. This widespread use makes Linux VMs a prime target for cyberattacks, particularly advanced malware designed for ...
Tom Landman, Nir Nissim
doaj   +1 more source

FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis

IEEE Access, 2022
To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection,
Younghoon Ban, Sunjun Lee, Dokyung Song, Haehyun Cho, Jeong Hyun Yi   +4 more
doaj   +1 more source

Generative Adversarial Network for Global Image-Based Local Image to Improve Malware Classification Using Convolutional Neural Network

Applied Sciences, 2020
Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it.
Sejun Jang, Shuyu Li, Yunsick Sung
doaj   +1 more source

Multifamily malware models

Journal of Computer Virology and Hacking Techniques, 2020
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the
Samanvitha Basole, Fabio Di Troia, Mark Stamp 0001   +2 more
openaire   +3 more sources

Morphological detection of malware [PDF]

2008 3rd International Conference on Malicious and Unwanted Software (MALWARE), 2008
In the field of malware detection, method based on syntactical consideration are usually efficient. However, they are strongly vulnerable to obfuscation techniques. This study proposes an efficient construction of a morphological malware detector based on a syntactic and a semantic analysis, technically on control flow graphs of programs (CFG).
Bonfante, Guillaume, Kaczmarek, Matthieu, Marion, Jean-Yves   +2 more
openaire   +2 more sources

Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection

International Journal of Computational Intelligence Systems, 2018
Nowadays, security of the computer systems has become a major concern of security experts. In spite of many antivirus and malware detection systems, the number of malware incidents are increasing day by day.
Hongwei Zhao, Mingzhao Li, Taiqi Wu, Fei Yang   +3 more
doaj   +1 more source