Malware Detection Using Gist Features and Deep Neural Network

2020 ◽  
Author(s):  
V. Krithika ◽  
M.S. Vijaya
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Malware Detection

Image Recognition-based Deep Neural Network for Packed Malware Detection

2021 ◽  
Author(s):  
Xuchenming Sun ◽  
Yunchun Zhang ◽  
Chengjie Li ◽  
Xin Zhang ◽  
Yuting Zhong
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Deep Neural Network ◽  
Malware Detection

scholarly journals PROUD-MAL: static analysis-based progressive framework for deep unsupervised malware classification of windows portable executable

2021 ◽  
Author(s):  
Syed Khurram Jah Rizvi ◽  
Warda Aslam ◽  
Muhammad Shahzad ◽  
Shahzad Saleem ◽  
Muhammad Moazam Fraz
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Static Analysis ◽  
Deep Neural Network ◽  
Early Stage ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Organizational Network ◽  
Depth Analysis ◽  
Feature Attention

AbstractEnterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet, instant detection using static analysis has become very difficult due to the exponential rise in volume and variety of malware. The compelling need of early stage detection of malware-based attacks significantly motivates research inclination towards automated malware detection. The recent machine learning aided malware detection approaches using static analysis are mostly supervised. Supervised malware detection using static analysis requires manual labelling and human feedback; therefore, it is less effective in rapidly evolutionary and dynamic threat space. To this end, we propose a progressive deep unsupervised framework with feature attention block for static analysis-based malware detection (PROUD-MAL). The framework is based on cascading blocks of unsupervised clustering and features attention-based deep neural network. The proposed deep neural network embedded with feature attention block is trained on the pseudo labels. To evaluate the proposed unsupervised framework, we collected a real-time malware dataset by deploying low and high interaction honeypots on an enterprise organizational network. Moreover, endpoint security solution is also deployed on an enterprise organizational network to collect malware samples. After post processing and cleaning, the novel dataset consists of 15,457 PE samples comprising 8775 malicious and 6681 benign ones. The proposed PROUD-MAL framework achieved an accuracy of more than 98.09% with better quantitative performance in standard evaluation parameters on collected dataset and outperformed other conventional machine learning algorithms. The implementation and dataset are available at https://bit.ly/35Sne3a.

A Novel Framework for Image-Based Malware Detection with a Deep Neural Network

2021 ◽  
pp. 102400
Author(s):  
Yifei Jian ◽  
Hongbo Kuang ◽  
Chenglong Ren ◽  
Zicheng Ma ◽  
Haizhou Wang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Malware Detection

scholarly journals An efficient combined deep neural network based malware detection framework in 5G environment

2021 ◽  
Vol 189 ◽  
pp. 107932
Author(s):  
Ning Lu ◽  
Dan Li ◽  
Wenbo Shi ◽  
Pandi Vijayakumar ◽  
Francesco Piccialli ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Malware Detection

scholarly journals Dynamic Malware Analysis with Feature Engineering and Feature Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 1210-1217
Author(s):  
Zhaoqi Zhang ◽  
Panpan Qi ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Malware Detection ◽  
Feature Representation ◽  
Feature Engineering ◽  
Malware Analysis ◽  
Neural Network Architecture

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

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