Deep Learning for Anomaly Detection in Cybersecurity: Methods and Performance Evaluation

Cybersecurity is essential in today's digital world, where connected devices are prevalent, and large volumes of sensitive data transfer are common. This growing dependency exposes both organizations and individuals to increasingly sophisticated attacks that traditional security solutions fail to mitigate. Traditional methods that rely on rule-based or signature-based approaches are unable to keep up with the pace of these emerging threats. The evolution of cyberattacks shows how resilient malicious actors can be. Older attacks were comparatively straightforward to today’s threats. Modern cyberattacks employ advanced persistent threats, polymorphic malware, and social engineering techniques. They frequently take advantage of zero-day vulnerabilities—security holes known to no one who sells software and without any patch—so that defences that would ordinarily stop them do not. The tendency of new malware or attack techniques to drift from established patterns makes them difficult—even impossible—to spot, which offers opportunities to prey on systems. Furthermore, the sheer amount of data produced in modern networks outstrips the processing capabilities of traditional security tools, which introduces latency into threat detection. Those delays leave time for attackers to breach the systems, extract sensitive data, or launch attacks that can disrupt vital operations. To meet each one of these challenges, Artificial Intelligence (AI) has become a game-changer in the cybersecurity space. Two branches of AI—machine learning and deep learning—have the ability to process large sets of data, identifying patterns and adapting to new threats. Unlike static security solutions, AI-powered models leverage data processed in real-time, increasing their ability to detect abnormal activities as well as zero-day attacks, by analysing events in network traffic, system logs, and user behaviour. We focus on examining the efficacy of advanced machine learning and deep learning models for detecting targeted cyberattacks. One major step in this process is to analyse raw data correctly and transform it in a way that optimizes AI training. Thorough analysis and preprocessing of raw data are crucial steps before developing a neural network. This process includes: •??Data Understanding: Identifying inconsistencies, biases, and hidden patterns to enhance attack visibility. •??Feature Engineering: Transforming raw data into valuable features using methods such as normalization and dimensionality reduction. •??Data Preprocessing: Cleaning and structuring data to improve model performance. •??Labeling and Balancing Data: Ensuring the dataset is balanced so that the model is not biased towards normal activity and can efficiently flag threats. Designing the Architecture of the Neural Network After preparing the data, the next step is to design the neural network architecture. This process is deciding the number of layers, the number of neurons in each layer, and how to connect neurons based on the complexity of the problem and the type of data. Designing an ideal neural network requires the evaluation of various architectures and hyperparameters before identifying the best-performing setup. This study trains on a huge normal dataset by combining a well-structured network and its well-prepared input data toward building a reliable model toward building an efficient cyberattack detection system. Employing the capabilities of AI will enable better cybersecurity and more effectively guard against emerging threats.