Detection of cyberattacks using bidirectional generative adversarial network - Vallabhaneni, Rohith; Vaddadi, Srinivas A.; Somanathan Pillai, Sanjaikanth E Vadakkethil; Addula, Santosh Reddy; Ananthan, Bhuvanesh (Indonesian Journal of Electrical Engineering and Computer Science, 2024)
Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.