Intrusion Detection and Prevention Models for Enhancing Organizational Cyber Defense Effectiveness

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@article{1713226,
      author = {Adetomiwa A. Dosunmu, Peter Olusoji Ogundele},
      title = {Intrusion Detection and Prevention Models for Enhancing Organizational Cyber Defense Effectiveness},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {6},
      pages = {310-324},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713226.pdf},
      abstract = {The increasing dependence of organizations on digital infrastructure has amplified exposure to cyber threats that are sophisticated, persistent, and highly adaptive. Traditional perimeter-based security mechanisms have proven insufficient in detecting and mitigating advanced attacks that exploit system vulnerabilities, insider access, and zero-day exploits. Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) have therefore become central components of organizational cyber defense strategies. Over the last two decades, research has produced a wide range of intrusion detection and prevention models, spanning signature-based, anomaly-based, specification-based, and hybrid approaches, as well as centralized, distributed, and collaborative architectures. This paper reviews and synthesizes research on IDS and IPS models published, with the aim of evaluating their effectiveness in enhancing organizational cyber defense. The study examines detection techniques, architectural designs, deployment strategies, performance metrics, and organizational integration challenges. By consolidating existing knowledge, the paper highlights key strengths and limitations of prevailing models and provides a conceptual basis for understanding how intrusion detection and prevention mechanisms contribute to proactive, resilient, and adaptive cyber defense in organizational contexts.},
      keywords = {Intrusion detection systems; Intrusion prevention systems; Cyber defense; Network security; Anomaly detection; Organizational security},
      month = {December},
  }