EXPLOITING AND SECURING MACHINE LEARNING: A CYBERSECURITY PERSPECTIVE ON ADVERSARIAL VULNERABILITIES AND COUNTERMEASURES

Machine learning (ML) is now the new focus of cybersecurity, as it can be used to perform automated intrusion detection, malware classification, and anomaly recognition. ML models are extremely susceptible to adversarial perturbations, or small, intentional manipulation of data that can severely misclassify the input data but leave no discernible effects on the input data. The research discusses the vulnerabilities of the ML models to adversarial attacks and examines effective and reproducible countermeasures that enhance capacity against cybersecurity-related apps. The performance drops because of adversarial attacks, and determines the performance of adversarial retraining in causing model resilience against adversarial attacks. An open-source adversarial dataset was used in a semi-empirical experiment utilizing Fast Gradient Sign Method (FGSM). To evaluate the performance of a feed-forward neural network under clean, adversarial and post-defence settings, Python-based frameworks were trained, attacked and re-trained. The FGSM attack decreased model accuracy by about 19% which validates that adversarial noise is highly vulnerable. The model regained about 14% of the lost performance, and this enhanced the classification stability and detection accuracy after retraining on combined clean and perturbed data. The results indicate that reproducible experiments of lightweight can be used effectively to test adversarial threats. Although the retraining approaches are not complex, even basic methods lead to a substantial enhancement of ML resilience and facilitate reliable and trusted AI-determined cybersecurity infrastructures.