Research Trends for Malware and Intrusion Detection on Network Systems

Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar

2021Advances in information security, privacy, and ethics book series13 citationsDOI

Abstract

With more uses of internet-based services, the risk of cyberattacks is growing continuously. To analyze these research trends for malware and intrusion detection, the authors applied the topic modeling approach in the study by using the LDA (latent dirichlet allocation) and calculating the maximum and minimum probability of the words, which appears in the large collection of text. The LDA technique is useful in finding the hidden topics for further research in the areas of network and cybersecurity. In this chapter, they collected the abstract of two thousand papers from the Scopus library from 2014 to 2021. These collected papers are from reputed publications such as Elsevier, Springer, and IEEE Transactions. The main aim of this study is to find research trends based on keywords that are untouched or on which less research work has been done. To the best of the authors' knowledge, this will be the first study done by using the LDA technique for topic modeling in the areas of network security to demonstrate the research gap and trends for malware and intrusion detection systems.

Topics & Concepts

Latent Dirichlet allocationMalwareIntrusion detection systemComputer scienceTopic modelNetwork securityThe InternetData scienceData miningComputer securityWorld Wide WebArtificial intelligenceNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesInformation and Cyber Security