A DATA DRIVEN ANOMALY BASED BEHAVIOR DETECTION METHOD FOR ADVANCED PERSISTENT THREATS (APT)

A DATA DRIVEN ANOMALY BASED BEHAVIOR DETECTION METHOD FOR ADVANCED PERSISTENT THREATS (APT)

Abstract:
Advanced Persistent Threats (APTs) pose significant challenges to the security of computer systems and networks. Traditional security measures, such as signature-based detection and rule-based intrusion detection systems, often struggle to detect APTs due to their sophisticated and stealthy nature. Therefore, there is a need for novel approaches that leverage data-driven techniques to identify anomalous behaviors associated with APTs.

This research proposes a data-driven anomaly-based behavior detection method specifically designed for detecting APTs. The method utilizes machine learning algorithms to analyze large volumes of network and system log data, enabling the identification of abnormal activities that may indicate the presence of an APT.

The proposed method consists of several key steps. First, the system gathers and preprocesses relevant log data from various sources, including network traffic, system events, and user behavior. Next, feature engineering techniques are applied to extract meaningful features from the log data, capturing both low-level network attributes and high-level behavioral patterns.

To detect APTs, a machine learning model is trained on a labeled dataset, which includes both normal and APT-related instances. The model learns to differentiate between normal behaviors and the subtle anomalies indicative of APTs. Various machine learning algorithms, such as decision trees, random forests, or deep learning models, can be employed to achieve this objective.

During the detection phase, the trained model is applied to real-time or near-real-time data streams to identify potential APT activities. The model evaluates the extracted features and compares them against the learned patterns, assigning anomaly scores to each instance. Threshold-based techniques or ensemble methods can be used to determine whether an instance is classified as an APT.

The proposed method offers several advantages over traditional APT detection approaches. It leverages the power of machine learning to adaptively detect unknown and evolving APTs by learning from historical data. It can also handle large-scale data streams and provide near-real-time detection capabilities.

Experimental evaluations on real-world datasets and comparison with existing APT detection methods will be conducted to validate the effectiveness and efficiency of the proposed method. The results will demonstrate the potential of data-driven anomaly-based behavior detection for mitigating the risks posed by APTs and enhancing the overall security posture of computer systems and networks.

Keywords: Advanced Persistent Threats (APT), anomaly detection, data-driven methods, machine learning, cybersecurity.

A DATA DRIVEN ANOMALY BASED BEHAVIOR DETECTION METHOD FOR ADVANCED PERSISTENT THREATS (APT). GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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