An empirical assessment of ML models for 5G network intrusion detection: A data leakage-free approach

e-Prime - Advances in Electrical Engineering Electronics and Energy, Год журнала: 2024, Номер 8, С. 100590 - 100590

Опубликована: Май 9, 2024

This paper thoroughly compares thirteen unique Machine Learning (ML) models utilized for Intrusion detection systems (IDS) in a meticulously controlled environment. Unlike previous studies, we introduce novel approach that avoids data leakage, enhancing the reliability of our findings. The study draws upon comprehensively labeled 5G-NIDD dataset covering broad spectrum network behaviors, from benign real-user traffic to various attack scenarios. Our preprocessing and experimental design have been carefully structured eradicate any standout feature methodology significantly improves robustness dependability results compared prior studies. ML are evaluated using performance metrics, including accuracy, precision, recall, F1-score, ROC AUC, execution time. reveal K-Nearest Neighbors model is superior accuracy while Voting Classifier stands out precision F1-score. Decision Tree, Bagging, Extra Trees exhibit strong recall scores. In contrast, AdaBoost falls short across all assessed metrics. Despite displaying only modest on other Naive Bayes excels computational efficiency, offering quickest emphasizes importance understanding models' distinct strengths, drawbacks, trade-offs intrusion detection. It highlights no single universally superior, choice hinges nature dataset, specific application requirements, resources available.

Язык: Английский

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Federated Learning-Based Ransomware Detection via Indicators of Compromise

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 18, 2024

Ransomware attacks have become increasingly prevalent and sophisticated, posing significant threats to data security organizational operations worldwide. Leveraging a federated learning-based approach, this research presents novel advancement in ransomware detection by utilizing network file system indicators of compromise while ensuring privacy. The methodology involves the decentralized training machine learning models across multiple clients, which enhances model's robustness adaptability various attack scenarios. Extensive experiments evaluations demonstrate high accuracy, precision, recall, F1-scores achieved proposed model, showcasing its effectiveness real-world applications. innovative combination preprocessing, feature engineering, sophisticated techniques within framework results scalable privacy-preserving solution capable addressing dynamic evolving landscape threats. This study contributes valuable insights into development effective systems, emphasizing importance collaborative enhancing cybersecurity defenses.

Язык: Английский

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SMRD: A Novel Cyber Warfare Modeling Framework for Social Engineering, Malware, Ransomware, and Distributed Denial-of-Service Based on a System of Nonlinear Differential Equations

Journal of Applied Artificial Intelligence, Год журнала: 2024, Номер 5(1)

Опубликована: Март 20, 2024

Cyber warfare has emerged as a critical aspect of modern conflict, state and non-state actors increasingly leverage cyber capabilities to achieve strategic objectives. The rapidly evolving threat landscape demands robust adaptive approaches protect against advanced cyberattacks mitigate their impact on national security. Traditional defense strategies often struggle keep pace with the changing landscape, resulting in need for more cyberattacks. This paper presents novel modeling framework, Social Engineering, Malware, Ransomware, Distributed Denial-of-Service (SMRD), capturing interactions interdependencies between these core components. SMRD framework offers insights enhancing defense, prediction, proactive measures. A mathematical model consisting system nonlinear differential equations is proposed quantify relationships dynamics

Язык: Английский

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Watch the Skies: A Study on Drone Attack Vectors, Forensic Approaches, and Persisting Security Challenges

Future Internet, Год журнала: 2024, Номер 16(7), С. 250 - 250

Опубликована: Июль 13, 2024

In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination security requirements, threat models, solutions pertinent to UAVs, emphasizing importance cybersecurity forensics. research addresses requirements UAV security, outlines various explores diverse ensure data integrity. Drone forensics, field dedicated investigation incidents involving has been extensively examined demonstrates its relevance in identifying attack origins or establishing accident causes. further surveys artifacts, tools, benchmark datasets that are domain providing comprehensive view current capabilities. Acknowledging ongoing particularly given pace technological advancement complex operational environments, this study underscores need for increased collaboration, updated protocols, regulatory frameworks. Ultimately, contributes deeper understanding aids fostering future into secure reliable operation drones.

Язык: Английский

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Protecting Against Social Engineering Using Wireshark

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 149 - 174

Опубликована: Сен. 27, 2024

In the domain of cybersecurity, defending against social engineering attacks remains a critical challenge. This abstract explores effective strategies and real-world examples using Wireshark—a powerful network protocol analyzer—to mitigate risks posed by tactics. Social exploit human psychology rather than technical vulnerabilities, making them difficult to detect through conventional security measures alone. chapter delves into various for leveraging Wireshark in defense engineering. Key aspects include configuring optimal monitoring, setting up filters profiles capture relevant traffic, decrypting SSL/TLS communications uncover malicious intent hidden within encrypted data. Detection techniques encompass monitoring DNS HTTP traffic signs phishing attempts, identifying malware communications, conducting behavioral analysis spot anomalies behavior

Язык: Английский

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