Abstract
Cybercrimes are cases of indictable offences and misdemeanors that involve computers or communication tools as targets and commission instruments or are associated with the prevalence of computer technology. Common forms of cybercrimes are child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing, and other forms of cyber hacking. They mostly lead to a privacy breach, security violation, business loss, financial fraud, or damage in public and government properties. Thus, this study intensively reviews cybercrime detection and prevention techniques. It first explores the different types of cybercrimes and discusses their threats against privacy and security in computer systems. Then, it describes the strategies that cybercriminals may utilize in committing these crimes against individuals, organizations, and societies. It also reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyzes the vulnerabilities of each technique. Finally, it provides recommendations for the development of a cybercrime detection model that can detect cybercrimes effectively compared with the existing techniques.
Highlights
Cybercrime is defined as any crime conducted using computers or other communication tools to cause fear and anxiety to people or damage, harm, and destroy properties
This study provides a comprehensive review of cybercrime detection techniques, which are categorized based on the use of different detection methods
It comprehensively reviews the existing techniques of cybercrime detection and classifies them into the following categorized techniques: 1) Statistical-based techniques, which focus on analyzing and extracting information from research data to develop effective methods for cybercrime detection; 2) machine learning techniques, which focus on predicting outputs according to a given input data; 3) neural network-based techniques, which are used to find reasonable solutions for cybercrimes; 4) fuzzy logic classifier and genetic algorithm, which intends to minimize possible false alerts that rise during the detection of cybercrimes; and 5) data-mining-based techniques, which are developed to detect cybercrimes using apriori algorithm
Summary
Cybercrime is defined as any crime conducted using computers or other communication tools to cause fear and anxiety to people or damage, harm, and destroy properties. The study first presents the different types of cybercrimes and discusses their consequences against individuals, organizations, and societies It comprehensively reviews the existing techniques of cybercrime detection and classifies them into the following categorized techniques: 1) Statistical-based techniques, which focus on analyzing and extracting information from research data to develop effective methods for cybercrime detection; 2) machine learning techniques, which focus on predicting outputs according to a given input data; 3) neural network-based techniques, which are used to find reasonable solutions for cybercrimes; 4) fuzzy logic classifier and genetic algorithm, which intends to minimize possible false alerts that rise during the detection of cybercrimes; and 5) data-mining-based techniques, which are developed to detect cybercrimes using apriori algorithm.
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