A Hybrid Model for Anomaly-Based Intrusion Detection System

The ever-increasing number of multi-vector cyberattacks has become a concern for all levels of organizations. Attackers are infecting Internet-enabled devices and exploiting them to carry out attacks. These devices are unwittingly becoming part of carrying out cyberattacks. Many studies have proposed machine learning–based promising solutions to stamp out cyberattacks preemptively. We review the machine learning techniques and highlight some promising solutions in recent studies. This study provides the advantage of experimenting with the developed solutions on modern datasets. This survey aims to provide an insightful organization of current developments in cybersecurity datasets and give suggestions for further research. We identified the most frightful cyberattacks and suitable datasets having records related to the attack. This paper discusses modern datasets such as CICIDS2017, CSE-CIC-IDS-2018, CIC-DDoS2019, UNSW-NB15, UNSW-TonIOT, UNSW-BotIoT, DoHBrw2020, and ISCX-URL-2016, which include records of recent sophisticated cyberattacks. This paper will focus on these modern datasets, retrieve detailed knowledge, and experiment with the most commonly used machine learning algorithms. We identify datasets as a significant centric topic that can be addressed with innovative machine learning approaches and solutions to defend against cyberattacks.

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“…Confusion matrix is a two-dimensional matrix that provides the complete classification performance of the model. 39 It includes…”

Section: Confusion Matrixmentioning

confidence: 99%

Machine learning to combat cyberattack: a survey of datasets and challenges

Prasad

Chandra

2022

Journal of Defense Modeling & Simulation

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“…Due to this, the SIDS approach is hardly detecting a new attack which has no previous pattern in the database that represents the main weak point of this approach [5]. In the AIDS which is the focusing of this work, a model for the normal behavior of a computer system is build based on machine learning techniques, any remarkable deviation between the model and the observed behavior can be considered as an intrusion [6]. In contrary with SIDS approach, the update on data is not required to detect new attacks.…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection system based on machine learning techniques

Riyadh

Alshibani

2021

IJEECS

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Recently, the data flow over the internet has exponentially increased due to the massive growth of computer networks connected to it. Some of these data can be classified as a malicious activity which cannot be captured by firewalls and anti-malwares. Due to this, the intrusion detection systems are urgent need in order to recognize malicious activity to keep data integrity and availability. In this study, an intrusion detection system based on cluster feature concepts and KNN classifier has been suggested to handle the various challenges issues in data such as in complete data, mixed-type and noise data. To streng then the proposed system a special kind of patterns similarity measures are supported to deal with these types of challenges. The experimental results show that the classification accuracy of the suggested system is better than K-nearest neighbor (KNN) and support vector machine classifiers when processing incomplete data set, inspite of droping down the overall detection accuracy.

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