Detection of Obfuscated Malicious JavaScript Code
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| Název: | Detection of Obfuscated Malicious JavaScript Code |
|---|---|
| Autoři: | Ammar Alazab, Ansam Khraisat, Moutaz Alazab, Sarabjot Singh |
| Zdroj: | Future Internet, Vol 14, Iss 8, p 217 (2022) |
| Informace o vydavateli: | MDPI AG |
| Rok vydání: | 2022 |
| Sbírka: | Directory of Open Access Journals: DOAJ Articles |
| Témata: | malware detection, intrusion detection, obfuscated malicious, machine learning, malicious JavaScript, Information technology, T58.5-58.64 |
| Popis: | Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect. Numerous recent cyberattacks use JavaScript vulnerabilities, and in some cases employ obfuscation to conceal their malice and elude detection. To secure Internet users, an adequate intrusion-detection system (IDS) for malicious JavaScript must be developed. This paper proposes an automatic IDS of obfuscated JavaScript that employs several features and machine-learning techniques that effectively distinguish malicious and benign JavaScript codes. We also present a new set of features, which can detect obfuscation in JavaScript. The features are selected based on identifying obfuscation, a popular method to bypass conventional malware detection systems. The performance of the suggested approach has been tested on JavaScript obfuscation attacks. The studies have shown that IDS based on selected features has a detection rate of 94% for malicious samples and 81% for benign samples within the dimension of the feature vector of 60. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | https://www.mdpi.com/1999-5903/14/8/217; https://doaj.org/toc/1999-5903; https://doaj.org/article/e2fa7807e7e643e98d2b76506dbc7dd6 |
| DOI: | 10.3390/fi14080217 |
| Dostupnost: | https://doi.org/10.3390/fi14080217 https://doaj.org/article/e2fa7807e7e643e98d2b76506dbc7dd6 |
| Přístupové číslo: | edsbas.273DA8F4 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect. Numerous recent cyberattacks use JavaScript vulnerabilities, and in some cases employ obfuscation to conceal their malice and elude detection. To secure Internet users, an adequate intrusion-detection system (IDS) for malicious JavaScript must be developed. This paper proposes an automatic IDS of obfuscated JavaScript that employs several features and machine-learning techniques that effectively distinguish malicious and benign JavaScript codes. We also present a new set of features, which can detect obfuscation in JavaScript. The features are selected based on identifying obfuscation, a popular method to bypass conventional malware detection systems. The performance of the suggested approach has been tested on JavaScript obfuscation attacks. The studies have shown that IDS based on selected features has a detection rate of 94% for malicious samples and 81% for benign samples within the dimension of the feature vector of 60. |
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| DOI: | 10.3390/fi14080217 |