A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security
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ORIGINAL PAPER
A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security R. Geetha1 · T. Thilagam1 Received: 13 January 2020 / Accepted: 17 August 2020 © CIMNE, Barcelona, Spain 2020
Abstract In recent years there exists a wide variety of cyber attacks with the drastic development of the internet technology. Detection of these attacks is of more significant in today’s cyber world scenario. Machine learning (ML) and deep learning (DL) methods have been preferred by researchers across different disciplines for providing solutions to their problems. In this paper we have presented a detailed classification of various DL/ML algorithms. In addition to that a focused survey on the use of various ML/DL methods for the detection of different categories of attacks has been presented. Furthermore the various platforms and tools used for implementing DL/ML methods are explored and the security solutions for the different categories of attacks are summarized.
1 Introduction 1.1 Cyber Security The uncommon utilization of network connected devices and vital dependence on information communication technology throughout the world. Many malicious users try to subvert credentials or simply attack host data. Over the last few years, Loukas et al. [1], there have been different examples of both proofs of concept and real-world attacks. Cyber security analysts Toch et al. [2] and experts have structured and created throughout the years various cyber defense systems to shield resources of associations from malicious attackers. These systems address cyber security threats, for example, virus, Trojans, worms, and botnets, among others Loukas et al. [3]. Existing arrangements dependent on Intrusion Detection Systems (IDS) incorporate (master) dynamic ways to deal with envision and expel vulnerabilities in processing frameworks with which to trigger responsive activities for moderation. Any assurance instrument needs to work by coordinating calculations with great and exact identification capacities, permitting fast handling of the information accumulated by the data sources. Without these capacities, IDSs can’t play out their checking and examination works
continuously, making it relatively difficult to identify potential cyber assaults when they are beginning to occur. This issue is because of the way that present systems give progressively high transmission rates. All the more uncommonly, the rates have expanded from 100 Mbps a couple of years prior to the present information rate of 10CGbps in wired systems. Vast volumes of data owing through systems make IDSs insufficient to assemble and dissect each system parcel. For instance, Deep Packet Inspection (DPI) instruments like Koscher et al. [4], can work appropriately on wired systems up to 1 Gbps, beginning to dispose of parcels because of overhead from 1.5 Gbps Checkoway et al. [5]. An on-going report Ward et al. [6], directed concentrated examinations to extricate a careful execution correlation by utilizing Snort and the utilization of m
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