Results 31 to 40 of about 707,343 (186)

A Survey on Malware Detection with Graph Representation Learning [PDF]

ACM Computing Surveys, 2023
Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization ...
Tristan Bilot, Nour El Madhoun, Khaldoun Al Agha, Anis Zouaoui   +3 more
semanticscholar   +1 more source

Automated Machine Learning for Deep Learning based Malware Detection [PDF]

Computers & security, 2023
Deep learning (DL) has proven to be effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural architecture ...
Austin R. Brown, Maanak Gupta, Mahmoud Abdelsalam   +2 more
semanticscholar   +1 more source

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild [PDF]

, 2018
In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Chen, Kai, Diao, Wenrui, Dong, Shuaike, Li, Menghao, Li, Zhou, Liu, Jian, Liu, Xiangyu, Wang, Xiaofeng, Xu, Fenghao, Zhang, Kehuan   +9 more
core   +2 more sources

BotDet: A System for Real Time Botnet Command and Control Traffic Detection

IEEE Access, 2018
Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of ...
Ibrahim Ghafir, Vaclav Prenosil, Mohammad Hammoudeh, Thar Baker, Sohail Jabbar, Shehzad Khalid, Sardar Jaf   +6 more
doaj   +1 more source

Deep-Layer Clustering to Identify Permission Usage Patterns of Android App Categories

IEEE Access, 2022
With the increasing usage of smartphones in banks, medical services and m-commerce, and the uploading of applications from unofficial sources, security has become a major concern for smartphone users. Malicious apps can steal passwords, leak details, and
Zakeya Namrud, Segla Kpodjedo, Ahmed Bali, Chamseddine Talhi   +3 more
doaj   +1 more source

Combined dynamic multi-feature and rule-based behavior for accurate malware detection

International Journal of Distributed Sensor Networks, 2019
Malware have become the scourge of the century, as they are continuously evolving and becoming more complex with increasing damages. Therefore, an adequate protection against such threats is vital.
Mohamed Belaoued, Abdelaziz Boukellal, Mohamed Amir Koalal, Abdelouahid Derhab, Smaine Mazouzi, Farrukh Aslam Khan   +5 more
doaj   +1 more source

MalHyStack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis

Intelligent Systems with Applications, 2023
Since the advent of malware, it has reached a toll in this world that exchanges billions of data daily. Millions of people are victims of it, and the numbers are not decreasing as the year goes by.
Kowshik Sankar Roy, Tanim Ahmed, Pritom Biswas Udas, Md. Ebtidaul Karim, Sourav Majumdar   +4 more
doaj   +1 more source

Security Toolbox for Detecting Novel and Sophisticated Android Malware

, 2015
This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project.
Deering, Tom, Holland, Benjamin, Kothari, Suresh, Mathews, Jon, Ranade, Nikhil   +4 more
core   +1 more source

Malware Detection Using Deep Learning and Correlation-Based Feature Selection

Symmetry, 2023
Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical.
E. Alomari, R. R. Nuiaa, Z. Alyasseri, Husam Jasim Mohammed, N. Sani, Mohd Isrul Esa, Bashaer Abbuod Musawi   +6 more
semanticscholar   +1 more source

Malware Detection using Machine Learning and Deep Learning

, 2019
Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware.
A Nappa, I Santos, MM Masud, R Moskovitch, SK Sahay, Y Ye, Y Ye   +6 more
core   +1 more source