Countering Salami Attacks with Hybrid Machine Learning Models

Abstract

Salami attacks, a subtle form of malicious
activity wherein adversaries execute
incremental actions to achieve their goals
while remaining unnoticed, pose a significant
threat in various domains. This paper
introduces a novel approach to mitigate
salami attacks through the integration of
hybrid machine learning models. Leveraging
the strengths of both supervised and
unsupervised learning, our proposed
framework aims to enhance the detection and
prevention capabilities against these
clandestine attacks. The hybrid model
synergistically combines the interpretability
of supervised learning with the adaptability of
unsupervised learning, providing a
comprehensive solution for identifying
incremental threats across diverse datasets.
By analyzing patterns, anomalies, and
deviations from normal behaviour, the model
establishes a robust defence mechanism
capable of discerning subtle slices of
malicious activities characteristic of salami
attacks. Furthermore, our approach
incorporates real-time learning mechanisms
to adapt to evolving attack strategies,
ensuring continuous efficacy in countering
dynamic threats. Through extensive
experimentation on diverse datasets and
simulated attack scenarios, our hybrid
machine-learning model demonstrates
superior accuracy and efficiency compared to
traditional methods. This research
contributes to the ongoing efforts to fortify
cybersecurity measures, offering a proactive
and adaptable solution to safeguard against
the nuanced nature of salami attacks in
contemporary digital environments