Summary of Malware Classification Using a Hybrid Hidden Markov Model-convolutional Neural Network, by Ritik Mehta and Olha Jureckova and Mark Stamp
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network
by Ritik Mehta, Olha Jureckova, Mark Stamp
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a novel approach to malware detection using a hybrid architecture that combines features extracted from opcode sequences with a Convolutional Neural Network (CNN) for classification. The model integrates Hidden Markov Models (HMMs) and CNNs, leveraging their strengths in capturing sequential patterns and extracting hierarchical features respectively. The authors demonstrate the effectiveness of this approach on the Malicia dataset, achieving superior performance compared to other machine learning methods, including an HMM-Random Forest model. This research has implications for bolstering malware classification capabilities and offers promising avenues for further exploration in cybersecurity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect malware is developed by combining two types of computer models: Hidden Markov Models (HMMs) and Convolutional Neural Networks (CNNs). The idea is inspired by previous work that showed a strong combination of HMMs and Random Forest. In this study, the researchers use HMMs to analyze patterns in code sequences and then use CNNs to classify the malware. They test their approach on a popular dataset called Malicia and find that it works better than other machine learning methods. This new way of detecting malware could help keep computers safer and is an area where further research could be done. |
Keywords
» Artificial intelligence » Classification » Cnn » Machine learning » Neural network » Random forest
