AMAL: high-fidelity, behavior-based automated malware analysis and classification

A Mohaisen, O Alrawi, M Mohaisen - computers & security, 2015 - Elsevier
This paper introduces AMAL, an automated and behavior-based malware analysis and
labeling system that addresses shortcomings of the existing systems. AMAL consists of two …

Behavior analysis of malware using machine learning

A Dhammi, M Singh - 2015 eighth international conference on …, 2015 - ieeexplore.ieee.org
In today's scenario, cyber security is one of the major concerns in network security and
malware pose a serious threat to cyber security. The foremost step to guard the cyber system …

Structural information based malicious app similarity calculation and clustering

J Kim, TG Kim, EG Im - Proceedings of the 2015 Conference on research …, 2015 - dl.acm.org
Depending on expansion of supply of smartphone, development of mobile application is
more active using various mobile platform. As a result of malicious applications, but also …

Cluster-oriented ensemble classifiers for intelligent malware detection

S Hou, L Chen, E Tas, I Demihovskiy… - Proceedings of the 2015 …, 2015 - ieeexplore.ieee.org
With explosive growth of malware and due to its damage to computer security, malware
detection is one of the cyber security topics that are of great interests. Many research efforts …

[PDF][PDF] Kernel K-means clustering for phishing website and malware categorization

K Sahu, SK Shrivastava - International Journal of Computer Applications, 2015 - Citeseer
In these days there are two famous internet attacks these are malware and phishing.
Malware stands for malicious software. It is designed to damage computer system without …

[PDF][PDF] MrKIP: Rootkit Recognition with Kernel Function Invocation Pattern.

CW Wang, CK Chen, CW Wang, SW Shieh - J. Inf. Sci. Eng., 2015 - Citeseer
Existing mechanisms tracing user-level activities such as system calls and APIs can be
circumvented by the kernel-level rootkits. In this paper, a novel system, MrKIP, is proposed to …

A study on selecting key Opcodes for malware classification and its Usefulness

JB Park, KS Han, TG Kim, EG Im - Journal of KIISE, 2015 - koreascience.kr
Recently, the number of new malware and malware variants has dramatically increased. As
a result, the time for analyzing malware and the efforts of malware analyzers have also …

Malicious software classification based on relations of system-call groups

SD Nikolopoulos, I Polenakis - … of the 19th Panhellenic Conference on …, 2015 - dl.acm.org
In this paper we present a graph-based algorithmic technique for classifying unknown
malware samples. In order for our model to be resistant against strong mutation of malicious …

Automated detection and classification of malware used in targeted attacks via machine learning

Y Korkmaz - 2015 - search.proquest.com
Targeted attacks pose a great threat to governments and commercial entities. Increasing
number of targeted attacks, especially Advanced Persistent Threats, are being discovered …

[图书][B] Calculating malware severity rating using threat tree analysis

A Malhotra - 2015 - search.proquest.com
Malware analysts and researchers around the world are looking for innovative means of
malware detection and classification. However, one concept of malware analysis that lacks …