A Novel Malware Detection and Classification Method Based on Capsule Network

2019 ◽  
pp. 573-584 ◽  
Author(s):  
Shu-wei Wang ◽  
Gang Zhou ◽  
Ji-cang Lu ◽  
Feng-juan Zhang
Keyword(s):  
Malware Detection ◽  
Classification Method ◽  
Malware Detection And Classification

scholarly journals Deep learning based Sequential model for malware analysis using Windows exe API Calls

2020 ◽  
Vol 6 ◽  
pp. e285 ◽  
Author(s):  
Ferhat Ozgur Catak ◽  
Ahmet Faruk Yazı ◽  
Ogerta Elezaj ◽  
Javed Ahmed
Keyword(s):  
Short Term Memory ◽  
Malware Detection ◽  
Classification Performance ◽  
Research Literature ◽  
Classification Method ◽  
Sequential Data ◽  
Malware Analysis ◽  
New Research ◽  
Metamorphic Malware ◽  
Malware Detection And Classification

Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection. This study is focused on metamorphic malware, which is the most advanced member of the malware family. It is quite impossible for anti-virus applications using traditional signature-based methods to detect metamorphic malware, which makes it difficult to classify this type of malware accordingly. Recent research literature about malware detection and classification discusses this issue related to malware behavior. The main goal of this paper is to develop a classification method according to malware types by taking into consideration the behavior of malware. We started this research by developing a new dataset containing API calls made on the windows operating system, which represents the behavior of malicious software. The types of malicious malware included in the dataset are Adware, Backdoor, Downloader, Dropper, spyware, Trojan, Virus, and Worm. The classification method used in this study is LSTM (Long Short-Term Memory), which is a widely used classification method in sequential data. The results obtained by the classifier demonstrate accuracy up to 95% with 0.83 $F_1$-score, which is quite satisfactory. We also run our experiments with binary and multi-class malware datasets to show the classification performance of the LSTM model. Another significant contribution of this research paper is the development of a new dataset for Windows operating systems based on API calls. To the best of our knowledge, there is no such dataset available before our research. The availability of our dataset on GitHub facilitates the research community in the domain of malware detection to benefit and make a further contribution to this domain.

Deep Learning-Based Malware Detection and Classification

2021 ◽  
pp. 93-116
Author(s):  
Mirnalinee T. T. ◽  
Bhuvana J. ◽  
Arul Thileeban S. ◽  
Daniel Jeswin Nallathambi ◽  
Anirudh Muthukumar
Keyword(s):  
Neural Network ◽  
Cyber Security ◽  
Input Data ◽  
Malware Detection ◽  
Primary Objective ◽  
Malware Analysis ◽  
Detection Techniques ◽  
The Family ◽  
Threat Level ◽  
Malware Detection And Classification

Malware analysis is an important aspect of cyber security and is a key component in securing systems from attackers. New malware signatures are being created continuously and detection techniques need to keep pace with them. The primary objective is to propose a solution which detects malicious files in real time by evaluating each file. Other objectives are to assess the threat level of the malware and recognize the family of malicious file. Hence, to cover all the needs and to fulfill the motivation, a deep neural network is more suitable to detect and classify the malware. Convolutional neural network-based system MalNet-D is designed to detect the presence of malware, and subsequently, to classify the detected malware into the family in which it belongs, a variation of MalNet-D termed as MalNet-C is proposed. Images of the executable files, both malign and benign, are used as input data, which is trained by the respective MalNet. This is used to detect and classify malware into families. The system achieved 93% accuracy in malware detection and 96% accuracy in malware classification.

Feature Reduction and Optimization of Malware Detection System Using Ant Colony Optimization and Rough Sets

2020 ◽  
Vol 14 (3) ◽  
pp. 95-114
Author(s):  
Ravi Kiran Varma Penmatsa ◽  
Akhila Kalidindi ◽  
S. Kumar Reddy Mallidi
Keyword(s):  
Ant Colony Optimization ◽  
Detection System ◽  
Malware Detection ◽  
Feature Reduction ◽  
Ant Colony ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Feature Significance ◽  
Malware Detection And Classification ◽  
Malicious Program

Malware is a malicious program that can cause a security breach of a system. Malware detection and classification is one of the burning topics of research in information security. Executable files are the major source of input for static malware detection. Machine learning techniques are very efficient in behavioral-based malware detection and need a dataset of malware with different features. In windows, malware can be detected by analyzing the portable executable (PE) files. This work contributes to identifying the minimum feature set for malware detection employing a rough set dependent feature significance combined with Ant Colony Optimization (ACO) as the heuristic-search technique. A malware dataset named claMP with both integrated features and raw features was considered as the benchmark dataset for this work. The analytical results prove that 97.15% and 92.8% data size optimization has been achieved with a minimum loss of accuracy for claMP integrated and raw datasets, respectively.

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