Results 151 to 160 of about 4,213 (184)

Code Graph for Malware Detection

2008 International Conference on Information Networking, 2008
When an application program is executed for the first time, the results of its execution are not always predictable. Since the host will be damaged by a malware as soon as it is executed, detecting and blocking the malware before its execution is the most effective means of protection.
Kyoochang Jeong, Heejo Lee
openaire   +1 more source

From Plagiarism to Malware Detection

2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2013
We have often seen how malware families evolve over time: the malware authors add new features, change the order of functions, modify some strings or add random useless code. They do all that to evade detection. In a similar way, computer science students that copy homework will change variable and function names, rephrase comments or even replace some
Ciprian Oprisa, George Cabau, Adrian Colesa   +2 more
openaire   +1 more source

Adversarial Examples for Malware Detection

2017
Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick McDaniel   +4 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

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

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

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

A comprehensive survey on deep learning based malware detection techniques

Computer Science Review, 2023
Sibi Chakkaravarthy Sethuraman
exaly  

Federated Learning in Malware Detection

2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), 2023
Dimitrios Serpanos, Georgios Xenos
openaire   +1 more source

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

Applied Sciences (Switzerland), 2022
Fuad A Ghaleb, Anazida Zainal, Bander Al-Rimy   +2 more
exaly