Machine Learning Models for Cybersecurity: Techniques for Monitoring and Mitigating Threats

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@article{1705138,
      author = {Arnab Kar, Vanitha Sivasankaran Balasubramaniam, Phanindra Kumar, Niharika Singh, Prof. (Dr) Punit Goel; Om Goel},
      title = {Machine Learning Models for Cybersecurity: Techniques for Monitoring and Mitigating Threats},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {3},
      pages = {620-634},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705138.pdf},
      abstract = {In an era where digital transformation is paramount, the need for robust cybersecurity measures has never been more critical. With the increasing frequency and sophistication of cyber threats, traditional security protocols are often inadequate to combat evolving risks. This paper explores the integration of machine learning (ML) techniques into cybersecurity frameworks, focusing on their ability to enhance threat detection and mitigation strategies. By leveraging the computational power of ML algorithms, organizations can significantly improve their ability to identify, predict, and respond to potential security incidents in real time.
The research begins by providing an overview of the current landscape of cybersecurity, detailing the challenges faced by organizations in protecting sensitive information against various forms of attacks, such as malware, phishing, and insider threats. It emphasizes the inadequacies of conventional methods, which often rely on predefined rules and signatures that fail to keep pace with new attack vectors. In contrast, ML models offer the advantage of adaptive learning, enabling systems to analyze patterns, identify anomalies, and refine their detection capabilities based on historical data.
A comprehensive literature review highlights the diverse range of ML techniques applied in cybersecurity, including supervised, unsupervised, and reinforcement learning models. The paper discusses the strengths and limitations of each approach, citing notable studies that demonstrate the efficacy of ML in enhancing cybersecurity measures. Key models, such as support vector machines, decision trees, and deep learning frameworks, are evaluated for their performance in various threat detection scenarios.
The methodology section outlines the architecture for implementing these ML models, detailing the data collection, preprocessing, feature extraction, and model training processes. By employing a dataset comprising both benign and malicious activities, the research utilizes a range of performance metrics?such as accuracy, precision, recall, and F1 score?to evaluate the effectiveness of the proposed models.
In conclusion, this paper highlights the transformative potential of machine learning in cybersecurity, advocating for its integration into existing security protocols. Future research directions emphasize the need for further advancements in ML algorithms and the exploration of hybrid models that can adapt to the ever-evolving cybersecurity landscape.},
      keywords = {Machine Learning, Cybersecurity, Threat Detection, Anomaly Detection, Monitoring, Mitigation, Intrusion Detection, Predictive Analytics},
      month = {September},
  }