IEEE Mediterranean Eletrotechnical Conference 2020

IEEE MELECON 2020

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Conference Track

Session 2.3 - Artificial Intelligence and Big Data Analytics

Negative Selection Algorithm Based Intrusion Detection Model

  Taofeekat Tosin Salau-Ibrahim

  • ABSTRACT

The ever-growing security challenges have been a hindrance to the success of Information Technology Innovations due to multifaceted network intrusions. Hence, it becomes imperative to provide tools that can address without compromising integrity, confidentiality and availability of network resources. This paper presents a model for detecting intrusion in a network using Negative Selection Algorithm. Negative Selection which is Human Immune System (HIS) inspired has been used for anomaly detection due to its self-nonself-discrimination potential. However, it suffers from high rate of false positives and scalability issues. This paper addresses the issues using feature selection to reduce the dimensionality of the dataset. The intrusion detection model is evaluated using NSLKDD dataset. The results obtained using the benchmark dataset showed that the scalability issue reduced in the proposed approach.

  • PRESENTER

Taofeekat Tosin Salau-Ibrahim
  Al-Hikmah University Ilorin, Nigeria

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