Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics

Amir Djenna, Ezedin Barka, Achouak Benchikh, Karima Khadir

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6302
JournalSensors
Volume23
Issue number14
DOIs
Publication statusPublished - Jul 2023

Keywords

  • artificial intelligence
  • cyber criminality
  • cyber threat intelligence
  • cybersecurity analytics
  • digital forensics investigation

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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