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Neural Network Based Anomaly Detection Method for Network Datasets
  • Bilal Zahid Hussain,
  • Yusuf Hasan,
  • Irfan Khan
Bilal Zahid Hussain
Texas A&M University

Corresponding Author:[email protected]

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Yusuf Hasan
Aligarh Muslim University
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Irfan Khan
Texas A&M University
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This research paper presents a comprehensive investigation into the development of an innovative and novel custom neural network model for intrusion detection systems (IDS). In the current era of rapid data transfer facilitated by the internet and advancements in communication technologies, the security of sensitive information is of paramount concern. As attackers continuously devise new methodologies to steal or tamper with data, IDSs face significant challenges in effectively detecting and mitigating intrusions. While extensive research has been conducted to enhance IDS capabilities, the need for improved detection accuracy and reduced false alarm rates remains a pressing issue. Moreover, the identification of zeroday attacks continues to pose a formidable obstacle. In contrast to conventional IDS approaches that heavily rely on statistical methodologies and rule-based expert systems, this study embraces data mining techniques, specifically Neural Networks (NNs), to overcome the limitations associated with large datasets. This research paper proposes a meticulously designed custom neural network model that leverages machine learning (ML) algorithms to analyze contemporary host activity and cloud service data. The paper extensively discusses the utilized dataset, meticulously evaluates the performance of various classifiers, and introduces our innovative neural network model. Emphasizing the significance of our model in anomaly detection, the findings underscore the importance of robust ML models to ensure the efficacy and longevity of deployed defensive systems. By capitalizing on its innovative design and leveraging the power of ML algorithms, our model not only addresses the limitations of traditional IDS approaches but also paves the way for enhanced accuracy, reduced false alarms, and improved resilience against zero-day attacks. This research contributes to the advancement of the field, shedding light on the novel possibilities and remarkable innovation offered by our custom neural network model in safeguarding critical information in an increasingly hostile digital landscape.
26 Feb 2024Submitted to TechRxiv
27 Feb 2024Published in TechRxiv