Network intrusion detection using machine learning approaches: Addressing data imbalance
نویسندگان
چکیده
منابع مشابه
Machine Learning for Network Intrusion Detection
Cyber security is an important and growing area of data mining and machine learning applications. We address the problem of distinguishing benign network traffic from malicious network-based attacks. Given a labeled dataset of some 5M network connection traces, we have implemented both supervised (Decision Trees, Random Forests) and unsupervised (Local Outlier Factor) learning algorithms to sol...
متن کاملMachine Learning for Network Intrusion Detection
3 Reviewed Work 2 3.1 Machine Learning in Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.1.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.1.2 Methods and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.2 Active Learning for Network Intrusion Detection . . . . . . . ...
متن کاملMachine Learning in Network Intrusion Detection
Network security is of great importance to individuals and organizations. Advanced technologies have been developed to protect both incoming and outgoing traffic, e.g. encryption of sensitive information, firewalls to block risky traffic. However, traditional firewalls and Intrusion Detection System (IDS) identify and block suspicious traffic based on preconfigured rules, traffic signatures as ...
متن کاملMachine Learning in Network Intrusion Detection System
During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP’99 is the mostly widely used data set for the evaluation of these systems. As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a cr...
متن کاملMachine Learning for Network Intrusion Detection
In recent years, networks have become an increasingly valuable target of malicious attacks due to the increased amount of user data they contain. In defense, Network Intrusion Detection Systems (NIDSs) have been developed to detect and report suspicious activity (i.e. an attack). In this project, we explore unsupervised learning techniques for building NIDs, which only analyze unencrypted packe...
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ژورنال
عنوان ژورنال: IET Cyber-Physical Systems: Theory & Applications
سال: 2021
ISSN: 2398-3396,2398-3396
DOI: 10.1049/cps2.12013