Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics
Cybercriminals are becoming increasingly intelligent and aggressive, making them more adept at covering their tracks, and the global epidemic of cybercrime necessitates significant efforts to enhance cybersecurity in a realistic way. The COVID-19 pandemic has accelerated the cybercrime threat landsc...
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| 格式: | 文件 |
| 语言: | English |
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MDPI AG
2023-07-01
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| 丛编: | Sensors |
| 主题: | |
| 在线阅读: | https://www.mdpi.com/1424-8220/23/14/6302 |
| _version_ | 1827731828515012608 |
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| author | Amir Djenna Ezedin Barka Achouak Benchikh Karima Khadir |
| author_facet | Amir Djenna Ezedin Barka Achouak Benchikh Karima Khadir |
| author_sort | Amir Djenna |
| collection | DOAJ |
| description | Cybercriminals are becoming increasingly intelligent and aggressive, making them more adept at covering their tracks, and the global epidemic of cybercrime necessitates significant efforts to enhance cybersecurity in a realistic way. The COVID-19 pandemic has accelerated the cybercrime threat landscape. Cybercrime has a significant impact on the gross domestic product (GDP) of every targeted country. It encompasses a broad spectrum of offenses committed online, including hacking; sensitive information theft; phishing; online fraud; modern malware distribution; cyberbullying; cyber espionage; and notably, cyberattacks orchestrated by botnets. This study provides a new collaborative deep learning approach based on unsupervised long short-term memory (LSTM) and supervised convolutional neural network (CNN) models for the early identification and detection of botnet attacks. The proposed work is evaluated using the CTU-13 and IoT-23 datasets. The experimental results demonstrate that the proposed method achieves superior performance, obtaining a very satisfactory success rate (over 98.7%) and a false positive rate of 0.04%. The study facilitates and improves the understanding of cyber threat intelligence, identifies emerging forms of botnet attacks, and enhances forensic investigation procedures. |
| first_indexed | 2024-03-11T00:41:02Z |
| format | Article |
| id | doaj.art-6a3f7216b375471bb482f15d927f19ae |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:41:02Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6a3f7216b375471bb482f15d927f19ae2023-11-18T21:15:49ZengMDPI AGSensors1424-82202023-07-012314630210.3390/s23146302Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity AnalyticsAmir Djenna0Ezedin Barka1Achouak Benchikh2Karima Khadir3College of New Technologies of Information and Communication, University of Constantine 2, Constantine 25000, AlgeriaCollege of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17555, United Arab EmiratesCollege of New Technologies of Information and Communication, University of Constantine 2, Constantine 25000, AlgeriaCollege of New Technologies of Information and Communication, University of Constantine 2, Constantine 25000, AlgeriaCybercriminals are becoming increasingly intelligent and aggressive, making them more adept at covering their tracks, and the global epidemic of cybercrime necessitates significant efforts to enhance cybersecurity in a realistic way. The COVID-19 pandemic has accelerated the cybercrime threat landscape. Cybercrime has a significant impact on the gross domestic product (GDP) of every targeted country. It encompasses a broad spectrum of offenses committed online, including hacking; sensitive information theft; phishing; online fraud; modern malware distribution; cyberbullying; cyber espionage; and notably, cyberattacks orchestrated by botnets. This study provides a new collaborative deep learning approach based on unsupervised long short-term memory (LSTM) and supervised convolutional neural network (CNN) models for the early identification and detection of botnet attacks. The proposed work is evaluated using the CTU-13 and IoT-23 datasets. The experimental results demonstrate that the proposed method achieves superior performance, obtaining a very satisfactory success rate (over 98.7%) and a false positive rate of 0.04%. The study facilitates and improves the understanding of cyber threat intelligence, identifies emerging forms of botnet attacks, and enhances forensic investigation procedures.https://www.mdpi.com/1424-8220/23/14/6302artificial intelligencecyber threat intelligencedigital forensics investigationcyber criminalitycybersecurity analytics |
| spellingShingle | Amir Djenna Ezedin Barka Achouak Benchikh Karima Khadir Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics Sensors artificial intelligence cyber threat intelligence digital forensics investigation cyber criminality cybersecurity analytics |
| title | Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics |
| title_full | Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics |
| title_fullStr | Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics |
| title_full_unstemmed | Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics |
| title_short | Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics |
| title_sort | unmasking cybercrime with artificial intelligence driven cybersecurity analytics |
| topic | artificial intelligence cyber threat intelligence digital forensics investigation cyber criminality cybersecurity analytics |
| url | https://www.mdpi.com/1424-8220/23/14/6302 |
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