Case Study On The Application Of Deep Learning To Network Intruder Detection
Authors: Chithik Raja Mohamed
Journal: Emirati Journal Of Policing And Security Studies
Publication Date: Vol 2 Issue 1
Keywords:
Deep Learning, Intruder Detection System, Deep Neural Network, Threshold, Auto Encoder, Anomaly Detection, Host-Based Intruder
Abstract
Deep Learning Has Seen Considerable Success In Several Application Sectors. Unfortunately, Little Research Has Been Done On Its Efficacy In The Context Of Network Intrusion Detection. This Article Includes Case Studies That Use Deep Learning To Identify Network Anomalies Both Supervised And Unsupervised. It Has Been Demonstrated That Deep Neural Networks (DNNs) Outperform Current Machine Learning-Based Intrusion Detection Systems In The Presence Of Shifting IP Addresses. We Also Demonstrate How Auto Encoders Can Support Network Anomaly Detection.
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A software development agency can leverage deep learning to enhance network intruder detection systems. By integrating advanced algorithms agencies can develop robust security solutions that identify and mitigate threats in real time. This case study highlights how deep learning optimizes detection accuracy ensuring superior protection for clients’ network infrastructures.
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