Results 201 to 210 of about 12,725 (233)

Generative Malware Outbreak Detection

2019 IEEE International Conference on Industrial Technology (ICIT), 2019
Recently several deep learning approaches have been attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they have shown respectable performance on a large corpus of dataset, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are ...
Sean Park, Iqbal Gondal, Joarder Kamruzzaman, Jon Oliver   +3 more
openaire   +1 more source

Infrastructure for Detecting Android Malware

2013
Malware for smartphones have sky-rocketed these last years, particularly for Android platforms. To tackle this threat, services such as Google Bouncer have intended to counter-attack. However, it has been of short duration since the malware have circumvented the service by changing their behaviors.
Laurent Delosières, David García
openaire   +1 more source

Zero-day malware detection

2016 Sixth International Symposium on Embedded Computing and System Design (ISED), 2016
The increasing volume and variety of malware is posing a serious security threat to the Internet today and is one of the main apprehensions for the security community for the last few years. The traditional security systems like Intrusion Detection System/Intrusion Prevention System and Anti-Virus (AV) software are not able to detect unknown malware as
Ekta Gandotra, Divya Bansal, Sanjeev Sofat   +2 more
openaire   +1 more source

Detecting Mobile Malware with TMSVM

2015
With the rapid development of Android devices, mobile malware in Android becomes more prevalent. Therefore, it is rather important to develop an effective model for malware detection. Permissions, system calls, and control flow graphs have been proved to be important features in detection.
Xi Xiao, Xianni Xiao, Yong Jiang 0001, Qing Li 0006   +3 more
openaire   +1 more source

Two Methods for Detecting Malware

2013
In this paper, we present two ways of detecting malware. The first one takes advantage of a platform that we have developed. The platform includes tools for capturing malware, running code in a controlled environment, and analyzing its interactions with external entities.
Maciej Korczynski, Gilles Berger-Sabbatel, Andrzej Duda   +2 more
openaire   +1 more source

Using IRP for Malware Detection

2010
Run-time malware detection strategies are efficient and robust, which get more and more attention. In this paper, we use I/O Request Package (IRP) sequences for malware detection. N-gram will be used to analyze IRP sequences for feature extraction. Integrated use of Negative Selection Algorithm (NSA) and Positive Selection Algorithm (PSA), we get more ...
FuYong Zhang, DeYu Qi 0001, JingLin Hu
openaire   +1 more source

Malware Detection in Ubiquitous Environments

2012
Today, almost every environment (e.g. airports, home, office, etc.) are populated with a high number of heterogeneous devices like smart-phones, sensors, laptops, tablets or hotspots. Taking advantage of the different communications capabilities that these devices have, researches have been studying how to make people collaborate in spontaneous or ...
Manuel García-Cervigón, Roberto Morales   +1 more
openaire   +1 more source

A comprehensive survey on deep learning based malware detection techniques

Computer Science Review, 2023
Sibi Chakkaravarthy Sethuraman
exaly  

Malware Detection Issues, Challenges, and Future Directions: A Survey

Applied Sciences (Switzerland), 2022
Faitouri A Aboaoja, Anazida Zainal, Fuad A Ghaleb   +2 more
exaly  

EfficientNet convolutional neural networks-based Android malware detection

Computers and Security, 2022
Neeraj Menon, Vinayakumar Ravi, V Sowmya   +2 more
exaly