Classification Techniques Using Spam Filtering Email

Priyatharsini, P.; Chandrasekar, C.

doi:10.26483/ijarcs.v9i2.5571

Cited by 7 publications

(7 citation statements)

References 1 publication

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“…ML techniques are being employed to detect and classify a message as spam or ham. ML techniques have a significant contribution to detect spam messages on computer [26,27], SMS messages on mobile [28], spam tweets [29], or images/video [30,31].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

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Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

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Section: Introductionmentioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

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“…There are other various ML techniques that can forecast data (Applications et al, n.d.). ML-techniques are significantly involved in detecting computer spam communications (Chandrasekar, 2018), mobile text messages (Abdulhamid et al, 2017), spam tweets, and video pictures. The IDS is a computer network security system for scanning network vulnerabilities against malicious invasions.…”

Section: Literature Reviewmentioning

confidence: 99%

Privacy Prevention of Big Data Applications: A Systematic Literature Review

et al. 2022

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This paper focuses on privacy and security concerns in Big Data. This paper also covers the encryption techniques by taking existing methods such as differential privacy, k-anonymity, T-closeness, and L-diversity. Several privacy-preserving techniques have been created to safeguard privacy at various phases of a large data life cycle. The purpose of this work is to offer a comprehensive analysis of the privacy preservation techniques in Big Data, as well as to explain the problems for existing systems. The advanced repository search option was utilized for the search of the following keywords in the search: “Cyber security” OR “Cybercrime”) AND ((“privacy prevention”) OR (“Big Data applications”)). During Internet research, many search engines and digital libraries were utilized to obtain information. The obtained findings were carefully gathered out of which 103 papers from 2,099 were found to gain the best information sources to address the provided study subjects. Hence a systemic review of 32 papers from 103 found in major databases (IEEExplore, SAGE, Science Direct, Springer, and MDPIs) were carried out, showing that the majority of them focus on the privacy prediction of Big Data applications with a contents-based approach and the hybrid, which address the major security challenge and violation of Big Data. We end with a few recommendations for improving the efficiency of Big Data projects and provide secure possible techniques and proposed solutions and model that minimizes privacy violations, showing four different types of data protection violations and the involvement of different entities in reducing their impacts.

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“…Machine learning methods are used to combat such threats. Machine learning methods make a significant contribution to the detection of spam messages on a computer [16,17], SMS messages on mobile devices [18], spam tweets [19] or images/videos [20,21].…”

Section: Brief Overview Of Used Articlesmentioning

confidence: 99%

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

et al. 2022

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Information security is one of the most important attributes of distributed systems that often operate on unreliable networks. Enabling security features during the development of a distributed system requires the careful analysis of potential attacks or threats in different contexts, a process often referred to as «threat modeling». Information protection should be comprehensive, but it is also necessary to take into account the possibility of the emergence of threats specific to a certain information system. Many public and private organizations are still trying to implement system models and the threats directed at them on their own. The main reason for this is the lack of useful and high-quality methodologies that can help developers design system models. This review explores a variety of the literature on confidentiality- and integrity-aware system design methodologies, as well as threat classification methods, and identifies key issues that may be referenced by organizations to make design system processes easier. In particular, this article takes a look at the extent to which existing methodologies cover objects of protection and methods of classifying threats, as well as whether there are such models of systems in which the object itself and the threats directed at it are described. This includes whether the compiled models exhibit symmetry or asymmetry. This literature research shows that methodologies appear to be heterogeneous and versatile, since existing methodologies often only focus on one object of protection (a system). Based on the given analysis, it can be concluded that the existing methodologies only relate superficially to the description of system models and threats, and it is necessary to develop a more complete abstract model of the protected object and threats aimed at it in order to make this model suitable for any organization and protect it against most threats.

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Classification Techniques Using Spam Filtering Email

Cited by 7 publications

References 1 publication

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Privacy Prevention of Big Data Applications: A Systematic Literature Review

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

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