MALWARE DETECTION AND BEHAVIOURAL CLASSIFICATION USING AI

Abstract

The increasing complexity of malware attacks
poses a serious challenge to modern cybersecurity
systems. Traditional detection techniques, which depend
on predefined signatures, are no longer effective against
advanced threats such as polymorphic, metamorphic,
and zero-day malware. These types of malicious
software continuously alter their structure and behavior
to avoid detection, making conventional rule-based
approaches insufficient for ensuring system security. To
overcome these limitations, Artificial Intelligence (AI),
particularly Machine Learning (ML) and Deep
Learning (DL), has emerged as a powerful solution for
proactive malware detection.
AI-based malware detection systems utilize large
datasets and sophisticated algorithms to identify
harmful patterns through both static and dynamic
analysis. Unlike traditional approaches that require
frequent manual updates, AI models can adapt to
evolving threats by learning from new data, enabling
them to detect previously unseen malware variants. By
examining features such as file properties, behavioral
activities, network traffic, and system interactions, these
models can effectively identify anomalies that indicate
potential threats. Various machine learning techniques
are applied for classification tasks, including Decision
Trees, Support Vector Machines (SVMs), and Random
Forest algorithms, which rely on extracted features for
accurate detection. In addition, deep learning models
such as Convolutional Neural Networks (CNNs) and
Long Short-Term Memory (LSTM) networks provide
enhanced capability in recognizing complex patterns
and sequential behaviors. Overall, AI-driven
approaches significantly improve the efficiency and
accuracy of malware detection systems, offering a
robust defense against evolving cyber threats.