Machine learning based fileless malware traffic classification using image visualization
Abstract In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have...
| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
SpringerOpen
2023-12-01
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| coleção: | Cybersecurity |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1186/s42400-023-00170-z |
| _version_ | 1827603406606303232 |
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| author | Fikirte Ayalke Demmese Ajaya Neupane Sajad Khorsandroo May Wang Kaushik Roy Yu Fu |
| author_facet | Fikirte Ayalke Demmese Ajaya Neupane Sajad Khorsandroo May Wang Kaushik Roy Yu Fu |
| author_sort | Fikirte Ayalke Demmese |
| collection | DOAJ |
| description | Abstract In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software. |
| first_indexed | 2024-03-09T05:39:04Z |
| format | Article |
| id | doaj.art-83991ffeaeb1448382d6fae18f3aeba0 |
| institution | Directory Open Access Journal |
| issn | 2523-3246 |
| language | English |
| last_indexed | 2024-03-09T05:39:04Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj.art-83991ffeaeb1448382d6fae18f3aeba02023-12-03T12:26:44ZengSpringerOpenCybersecurity2523-32462023-12-016111810.1186/s42400-023-00170-zMachine learning based fileless malware traffic classification using image visualizationFikirte Ayalke Demmese0Ajaya Neupane1Sajad Khorsandroo2May Wang3Kaushik Roy4Yu Fu5Department of Computer Science, College of Engineering, North Carolina A&T State UniversityPalo Alto Networks, Inc.Department of Computer Science, College of Engineering, North Carolina A&T State UniversityPalo Alto Networks, Inc.Department of Computer Science, College of Engineering, North Carolina A&T State UniversityPalo Alto Networks, Inc.Abstract In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software.https://doi.org/10.1186/s42400-023-00170-zNetwork securityTraffic classificationFileless malwareImage visualizationMachine learningIntrusion detection |
| spellingShingle | Fikirte Ayalke Demmese Ajaya Neupane Sajad Khorsandroo May Wang Kaushik Roy Yu Fu Machine learning based fileless malware traffic classification using image visualization Cybersecurity Network security Traffic classification Fileless malware Image visualization Machine learning Intrusion detection |
| title | Machine learning based fileless malware traffic classification using image visualization |
| title_full | Machine learning based fileless malware traffic classification using image visualization |
| title_fullStr | Machine learning based fileless malware traffic classification using image visualization |
| title_full_unstemmed | Machine learning based fileless malware traffic classification using image visualization |
| title_short | Machine learning based fileless malware traffic classification using image visualization |
| title_sort | machine learning based fileless malware traffic classification using image visualization |
| topic | Network security Traffic classification Fileless malware Image visualization Machine learning Intrusion detection |
| url | https://doi.org/10.1186/s42400-023-00170-z |
| work_keys_str_mv | AT fikirteayalkedemmese machinelearningbasedfilelessmalwaretrafficclassificationusingimagevisualization AT ajayaneupane machinelearningbasedfilelessmalwaretrafficclassificationusingimagevisualization AT sajadkhorsandroo machinelearningbasedfilelessmalwaretrafficclassificationusingimagevisualization AT maywang machinelearningbasedfilelessmalwaretrafficclassificationusingimagevisualization AT kaushikroy machinelearningbasedfilelessmalwaretrafficclassificationusingimagevisualization AT yufu machinelearningbasedfilelessmalwaretrafficclassificationusingimagevisualization |