ConRec: malware classification using convolutional recurrence

Mallik, Abhishek; Khetarpal, Anavi; Kumar, Sanjay

doi:10.1007/s11416-022-00416-3

Cited by 30 publications

(9 citation statements)

References 36 publications

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“…By transforming malware binaries into grayscale images, and employing CNNs, the proposed method achieved the impressive accuracy of 97.4%. Mallik et al [50] utilized convolutional recurrence with grayscale images, BiLSTM layers, data augmentation, and convolutional neural networks, achieving the remarkable accuracy of 98.36% in malware classification. K Gupta et al [51] utilized an artificial neural network architecture to precisely classify malware variants, effectively tackling the challenges posed by obfuscation and compression techniques.…”

Section: Related Workmentioning

confidence: 99%

Mitigating the Risks of Malware Attacks with Deep Learning Techniques

et al. 2023

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Malware has become increasingly prevalent in recent years, endangering people, businesses, and digital assets worldwide. Despite the numerous techniques and methodologies proposed for detecting and neutralizing malicious agents, modern automated malware creation methods continue to produce malware that can evade modern detection techniques. This has increased the need for advanced and accurate malware classification and detection techniques. This paper offers a unique method for classifying malware, using images that use dual attention and convolutional neural networks. Our proposed model has demonstrated exceptional performance in malware classification, achieving the remarkable accuracy of 98.14% on the Malimg benchmark dataset. To further validate its effectiveness, we also evaluated the model’s performance on the big 2015 dataset, where it achieved an even higher accuracy rate of 98.95%, surpassing previous state-of-the-art solutions. Several metrics, including the precision, recall, specificity, and F1 score were used to evaluate accuracy, showing how well our model performed. Additionally, we used class-balancing strategies to increase the accuracy of our model. The results obtained from our experiments indicate that our suggested model is of great interest, and can be applied as a trustworthy method for image-based malware detection, even when compared to more complex solutions. Overall, our research highlights the potential of deep learning frameworks to enhance cyber security measures, and mitigate the risks associated with malware attacks.

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Section: Related Workmentioning

confidence: 99%

Mitigating the Risks of Malware Attacks with Deep Learning Techniques

et al. 2023

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“…Effectively expanding the network's informational pool is Bi-LSTM technology. Mallik A et al [16] retrieved features are processed by the BiLSTM layers, who then improve them to disclose hidden traits of the structural identities in the malware samples. Processing the derived features of an image depends on BiLSTM's ability to process the inputs consecutively.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

A Survey on Deep Learning Techniques for Darknet Traffic Malware Detection

Cynthia¹

2023

IJSREM

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Encrypted data that is sent over Tor or a VPN is referred to as "darknet traffic." In order to identify network activity brought on by a cyberattack, it is crucial to be able to recognise, find, and explain darknet activities. Cyberattacks that pose a serious danger to network security and management can be effectively observed through darknet monitoring and classification. Despite the fact that many surveys were conducted on network traffic classification using machine learning techniques, only a small number of researchers have the means to review their work on classifying network traffic using deep learning techniques. In this article, we introduce a novel idea for research on the classification of malware used in darknet traffic that uses Deep Learning techniques, which will aid researchers in improving their surveys. First, based on the survey, we chose a few techniques, including Deep Neural Network (DNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and AutoEncoder (AE) that have proven to be more effective in recent study. Second, the dataset CIC-DarkNet-2020, which contains a variety of darknet activities including VPN and TOR traffic, is used to develop the models. Finally, after analysing the information, we discovered the best Deep Learning Model for classifying Darknet traffic, which has the potential to enhance the efficiency of malware variant detection using constrained system and network resources. Index Terms – Darknet, DNN, CNN, RNN, AE, Deep Learning, Classifying Darknet.

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“…When it comes to military usage of malware, we may see more events like Stuxnet in the future. It remains to be seen how antivirus firms will cope with attackers with almost unlimited resources to create malware and those motivated only by profit [ 44 , 45 ]. However, with occurrences like Stuxnet, we may see alternative uses for malware in the future and malware classification evolution is shown in Figure 1 .…”

Section: Related Workmentioning

confidence: 99%

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition

Naeem

Amin

Alshamrani

et al. 2022

Computational Intelligence and Neuroscience

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The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. A malware classification method is frequently required to prioritize these occurrences because security teams cannot address all of that malware at once. Malware’s variety, volume, and sophistication are all growing at an alarming rate. Hackers and attackers routinely design systems that can automatically rearrange and encrypt their code to escape discovery. Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. Recently, deep convolutional neural networks (CNNs) successfully identified and classified malware. To categorize malware, a smart system has been suggested in this research. A novel model of deep learning is introduced to categorize malware families and multiclassification. The malware file is converted to a grayscale picture, and the image is then classified using a convolutional neural network. To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications. The findings stood out among the deep learning models with 99.97% accuracy for nine malware types.

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ConRec: malware classification using convolutional recurrence

Cited by 30 publications

References 36 publications

Mitigating the Risks of Malware Attacks with Deep Learning Techniques

Mitigating the Risks of Malware Attacks with Deep Learning Techniques

A Survey on Deep Learning Techniques for Darknet Traffic Malware Detection

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition

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