A Review and Analysis on Various Cyber Security Threats on the Basis of Machine Learning Algorithms - Paper

Aug 3, 2026

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Machine learning enables systems to learn from examples, identify patterns, and support rational decisions with minimal human involvement, which is increasingly relevant to cybersecurity. Cybersecurity safeguards computer systems, servers, mobile devices, networks, and data against malicious attacks, while newer threats can bypass traditional defenses. This work emphasizes two key directions: incorporating security considerations whenever ML is applied and using ML methods to strengthen cybersecurity capabilities, improving detection efficiency, enhancing model safety, and enabling rapid identification of zero-day vulnerabilities.

LanguageEnglish

CategoryResearch & Report

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Pages5

Reading Time13 min

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Abstract

Introduction

Literature Review

A Review and Analysis on Various Cyber Security Threats on the Basis of Machine Learning Algorithms Dr. Abhilash Maroju, Ph.D. Research Graduate, Department of Information Technology, University of the Cumberlands , USA. [doctorabhilashmaroju@gmail.com](doctorabhilashmaroju@gmail.com) Dr. Srinivas A Vaddadi, PhD Research Graduate, Department of Information Technology, University of the Cumberlands , [USA. Vsad93@gmail.com](USA. Vsad93@gmail.com) Dr. Rohith Vallabhaneni, PhD Research Graduate, Department of Information Technology, University of the Cumberlands , USA. [rohit.vallabhaneni.2222@gmail.com](rohit.vallabhaneni.2222@gmail.com) Sravanthi Dontu, PhD Research Student, Department of Information Technology, University of the Cumberlands, USA. [sravanthi.dontu13@gmail.com](sravanthi.dontu13@gmail.com) ABSTRACT The field of Artificial Intelligence (AI) known as machine learning (or ML for short) helps develop systems that can learn from examples, spot trends, and make rational decisions with little to no human intervention. Cybersecurity methods provide up-to-date security answers for detecting and countering dangers. Security solutions that were formerly adequate are now insufficient since criminals may circumvent traditional security procedures. Cybersecurity refers to the practice of keeping computer systems, servers, mobile devices, networks, and the data associated with them safe from malicious attacks. The two most critical parts of combining ML with cyber security are (1) ensuring that cyber security is taken into consideration whenever ML is used and (2) utilising ML to facilitate cyber security. We hope this union will help us in many ways; for example, by making cyber security procedures more efficient, bolstering the safety of machine learning models, and allowing us to detect zero-day vulnerabilities with little human intervention. 1. INTRODUCTION Protecting networks and systems against cyber-anomalies and other types of threats has become more important in recent days. Among these forms of assault are worms, botnets, malware, and denial-of-service (DoS) attacks. When these kinds of abnormalities occurred in large-scale computer networks, they caused damage that was irreparable and financial losses [1, 2] . As an illustration, a single ransomware infection that occurred in May of 2017 resulted in enormous losses for a variety of organisations and industries, such as the banking industry, the medical care industry, the power industry, and colleges, and it caused a loss of eight billion dollars [3] . The cybersecurity community has recently seen arise in discussions surrounding the prevalence of security breaches and invasions. This is true whether we're talking about safeguarding an IoT application or a cyber-system. Although there are a number of traditional ways, such as firewalls, encryption, and so on, that are designed to deal with cyberattacks that are based on the Internet, the most effective way to deal with these problems is to have an intelligent system that can detect anomalies or attacks in an efficient manner. The understanding of artificial intelligence is the primary topic of this research. Machine learning security modelling is of particular interest to us because of the automatic learning it can do with training security data, which could lead to better results in application. Developing security models based on machine learning allows for the aim of delivering intelligent security services by analysing various cyberattacks or abnormalities and, in the end, detecting or forecasting the risks associated with them [4] . As a whole, the detection models can deal with a "binaryclass" issue—the detection of anomalies—or a "multi-class"problem—the handling of numerous interconnected cyberattacks. A plethora of recent studies have covered a wide range of topics in this field, from botnet attack detection [5] to anomaly and attack classification for use in intrusion d

FAQs

Why is combining machine learning with cybersecurity important?

Machine learning can improve how security systems learn from data and recognize patterns, while cybersecurity needs ML to handle evolving threats that bypass traditional protections.

What types of cyber threats and anomalies are discussed in the introduction?

The document highlights worms, botnets, malware, denial-of-service (DoS) attacks, and anomalies such as suspicious or unexpected behaviors in networks.

How are machine learning security models generally evaluated in this research context?

The document describes detection models that can work as binary-class anomaly detection or multi-class classification for multiple interconnected attacks, often supported by feature selection to improve intrusion detection performance.

Aug 3, 2026

Upload by Aurora