Review of malicious code detection in data mining applications: challenges, algorithms, and future direction

Razaque A. Bektemyssova G. Yoo J. Hariri S. Khan M.J. Nalgozhina N. Hwang J. Khan M.A.
June 2025 Springer

Cluster Computing
2025 #28 Issue 3

In an era where machine learning critically underpins business operations, detecting vulnerabilities introduced by malicious code has become increasingly essential. Although prior research has extensively explored malicious code within machine learning algorithms, a targeted analysis specifically designed to identify and address these threats remains necessary. This paper presents an exhaustive literature review, focusing on the key processes of insertion, recognition, decision-making, and selection of malicious codes. We aim to uncover architectural weaknesses in data mining applications that amplify system vulnerabilities. Leveraging an integrative review covering publications from 2008 to 2024, we synthesize insights from a diverse array of academic and digital sources, examining 167 pertinent articles. This rigorous approach reveals the nuanced effects of malicious code on feature selection algorithms, crucial for maintaining data integrity. Our findings indicate that malicious code can significantly disrupt various sectors, including industrial, telecommunications, and biological data mining, adversely affecting clustering, classification, and regression algorithms. However, an encouraging outcome is observed in advanced feature selection algorithms that demonstrate resilience by effectively filtering out irrelevant data inputs. The paper concludes with a strong call for the development of sophisticated detection methods, which are vital for mitigating the growing risks associated with malicious code. It stresses the importance of proactive algorithm identification and classification to preserve the efficacy of data mining. Current challenges in accurately classifying machine learning algorithms raise concerns about data privacy, security, and potential biases. Ongoing research is crucial for improving data interoperability and algorithm transparency, thereby strengthening the defense mechanisms of machine learning applications against the complex and evolving landscape of cyber threats.

Cyber threats , Data mining , Industrial applications , Malicious code detection , Vulnerabilities

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School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-Gu, Gyeonggi-do, Seongnam-si, South Korea
Department of Computer Engineering, International Information Technology University, Manas 34/1, Almaty, Kazakhstan
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States
Department of Cyber Science, R.O.K Naval Academy, Changwon, South Korea
Department of Electrical and Computer Engineering, Ohio Northern University, Ada, United States
International Department, Satbayev University, Almaty, Kazakhstan

School of Computing
Department of Computer Engineering
Department of Electrical and Computer Engineering
Department of Cyber Science
Department of Electrical and Computer Engineering
International Department

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