Correlation Analysis of Dataset Size and Accuracy of the CNN-based Malware Detection Algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 53-60
Author(s):  
Dong Jun Choi ◽  
◽  
Jae Woo Lee
2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Wang ◽  
Zhizhong Wu ◽  
Xi Li ◽  
Xuehai Zhou ◽  
Aili Wang ◽  
...  

This paper presents SmartMal—a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server’s main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users’ regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices.


2018 ◽  
Vol 7 (4.15) ◽  
pp. 113 ◽  
Author(s):  
Fadzli Marhusin ◽  
Christopher John Lokan

This study detects malware as it begins to execute and propose a data mining approach for malware detection using sequences of API calls in a Windows environment. We begin with some background of the study and the influence of Human Immune System in our detection mechanism, i.e. the Natural Killer (NK) and Suppressor (S) Cells. We apply the K = 10 crosses fold data validation against the dataset. We use the n-grams technique to form the data for the purpose of establishing the Knowledge Bases and for the detection stage. The detection algorithm integrates the NK and S to work in unison and statistically determine on whether a particular executable deemed as benign or malicious. The results show that we could preemptively detect malware and benign programs at the very early beginning of their execution upon inspecting the first few hundreds of the targeted API Calls. Depending on the speed of the processor and the ongoing running processes, this could just happen in a split of a second or a few. This research is as part of our initiative to build a behaviour based component of a cyber defence and this will enhance our readiness to combat zero-day attacks. 


2021 ◽  
Author(s):  
Thomas Cochrane ◽  
Peter Foster ◽  
Varun Chhabra ◽  
Maud Lemercier ◽  
Terry Lyons ◽  
...  

2013 ◽  
Vol 39 ◽  
pp. 315-324 ◽  
Author(s):  
Yuxin Ding ◽  
Xuebing Yuan ◽  
Ke Tang ◽  
Xiao Xiao ◽  
Yibin Zhang

2020 ◽  
Vol 29 (6) ◽  
pp. 1054-1060
Author(s):  
Lele Wang ◽  
Binqiang Wang ◽  
Peipei Zhao ◽  
Ruyi Liu ◽  
Jiangang Liu ◽  
...  

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