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

Authors

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 utilising 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.