An autoML network traffic analyzer for cyber threat detection

Παπανικολάου, Αλέξανδρος; Alevizopoulos, Aggelos; Ilioudis, Christos; Demertzis, Konstantinos; Rantos, Konstantinos

doi:10.1007/s10207-023-00703-0

Cited by 5 publications

(1 citation statement)

References 96 publications

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“…Network traffic analysis involves monitoring and inspecting network traffic patterns, data packets, and communication protocols to gain useful insights into the behavior of network users and identify any malicious activities [2]. By leveraging approaches such as deep packet inspection, flow analysis, and statistical anomaly detection, network traffic analysis allows for identifying suspicious activities, abnormal traffic patterns, and potential security breaches [3]. On the other hand, IDS is a security mechanism that monitors network traffic in real-time and compares it against a predefined set of attack signatures or behavioral patterns to detect and mitigate network attacks.…”

Section: Introductionmentioning

confidence: 99%

Novel Class Probability Features for Optimizing Network Attack Detection With Machine Learning

Raza,

Munir,

Almutairi

et al. 2023

IEEE Access

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Network attacks refer to malicious activities exploiting computer network vulnerabilities to compromise security, disrupt operations, or gain unauthorized access to sensitive information. Common network attacks include phishing, malware distribution, and brute-force attacks on network devices and user credentials. Such attacks can lead to financial losses due to downtime, recovery costs, and potential legal liabilities. To counter such threats, organizations use Intrusion Detection Systems (IDS) that leverage sophisticated algorithms and machine learning techniques to detect network attacks with enhanced accuracy and efficiency. Our proposed research aims to detect network attacks effectively and timely to prevent harmful losses. We used a benchmark dataset named CICIDS2017 to build advanced artificial intelligencebased machine learning methods. We propose a novel approach called Class Probability Random Forest (CPRF) for network attack detection performance enhancement. We created a novel feature set using the proposed CPRF approach. The CPRF approach predicts the class probabilities from the network attack dataset, which are then used as features for building applied machine learning methods. The comprehensive research results demonstrated that the random forest approach outperformed the state-of-the-art approach with a high-performance accuracy of 99.9%. The performance of each applied technique is validated using a k-fold approach and optimized with hyperparameter tuning. Our novel proposed research has revolutionized network attack detection, effectively preventing unauthorized access, service disruptions, sensitive information theft, and data integrity compromise.

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Section: Introductionmentioning

confidence: 99%