An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies

12/09/2020
by   Rony Chowdhury Ripan, et al.
0

Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps. Therefore, detecting numerous cyber-attacks in a network and creating an effective intrusion detection system plays a vital role in today's security. In this paper, we present an Isolation Forest Learning-Based Outlier Detection Model for effectively classifying cyber anomalies. In order to evaluate the efficacy of the resulting Outlier Detection model, we also use several conventional machine learning approaches, such as Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost Classifier (ABC), Naive Bayes (NB), and K-Nearest Neighbor (KNN). The effectiveness of our proposed Outlier Detection model is evaluated by conducting experiments on Network Intrusion Dataset with evaluation metrics such as precision, recall, F1-score, and accuracy. Experimental results show that the classification accuracy of cyber anomalies has been improved after removing outliers.

READ FULL TEXT
research
10/15/2021

A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods

Modern scientific advancements often contribute to the introduction and ...
research
03/28/2021

CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks

Detecting cyber-anomalies and attacks are becoming a rising concern thes...
research
11/25/2021

A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

The digital transformation faces tremendous security challenges. In part...
research
12/10/2022

Performance Evaluation of Apache Spark MLlib Algorithms on an Intrusion Detection Dataset

The increase in the use of the Internet and web services and the advent ...
research
08/05/2020

Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

Network attacks have been very prevalent as their rate is growing tremen...
research
04/20/2022

ARLIF-IDS – Attention augmented Real-Time Isolation Forest Intrusion Detection System

Distributed Denial of Service (DDoS) attack is a malicious attempt to di...
research
04/04/2019

GAN-based method for cyber-intrusion detection

Ubiquitous cyber-intrusions endanger the security of our devices constan...

Please sign up or login with your details

Forgot password? Click here to reset