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Heterogeneous Graph Attention Network
TLDR
We first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Expand
A Survey on Malware Detection Using Data Mining Techniques
TLDR
In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious and evolving security threats to Internet users. Expand
IMDS: intelligent malware detection system
TLDR
The proliferation of malware has presented a serious threat to the security of computer systems. Expand
An intelligent PE-malware detection system based on association mining
TLDR
The proliferation of malware has presented a serious threat to the security of computer systems. Expand
Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs
TLDR
In this paper, we propose a novel dynamic analysis method named Component Traversal that can automatically execute the code routines of each given Android application (app) as completely as possible. Expand
Malicious sequential pattern mining for automatic malware detection
TLDR
An efficient sequential pattern mining algorithm for discovering discriminative patterns between malware and benign samples.A new nearest neighbor classifier as the detection module to identify unknown malware. Expand
CIMDS: Adapting Postprocessing Techniques of Associative Classification for Malware Detection
Malware is software designed to infiltrate or damage a computer system without the owner's informed consent (e.g., viruses, backdoors, spyware, trojans, and worms). Nowadays, numerous attacks made byExpand
Automatic malware categorization using cluster ensemble
TLDR
We develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. Expand
DL 4 MD : A Deep Learning Framework for Intelligent Malware Detection
In the Internet-age, malware poses a serious and evolving threat to security, making the detection of malware of utmost concern. Many research efforts have been conducted on intelligent malwareExpand
SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging
TLDR
We develop interpretable string based malware detection system (SBMDS), which is based on interpretable strings analysis and uses support vector machine (SVM) ensemble with Bagging to classify the file samples and predict the exact types of the malware. Expand
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