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Advanced Data Mining Techniques: Security, Intelligent Systems and Applications

Submission Deadline: 31 March 2024 Submit to Special Issue

Guest Editors

Prof. Xingsi Xue, Fujian University of Technology, China.
Prof. Jeng-Shyang Pan, Shandong University of Science and Technology, China.
Dr. Pei-Wei Tsai, Swinburne University of Technology, Australia.

Summary

In our rapidly advancing digital age, we continuously generate a staggering amount of data spanning across various sectors such as healthcare, finance, marketing, education, and government. The advent of big data has yielded both opportunities and challenges, positioning data mining at the core of deriving valuable insights and facilitating data-driven decisions. With its intricate algorithms, data mining has the capability of revealing essential patterns, associations, and knowledge hidden within large and complex datasets. Nevertheless, these extracted insights often necessitate sophisticated computational intelligence techniques to interpret and understand. Techniques like artificial neural systems, swarm intelligence, and evolutionary programming, among others, have become vital in deciphering the knowledge unveiled by data mining and optimizing its algorithms for efficiency and accuracy. Moreover, in our interconnected world, data security holds paramount importance. Data mining, which often involves accessing and analyzing sensitive information, presents numerous challenges relating to information security, such as data privacy, cryptography, secure data sharing, and system integrity. The development of robust solutions to tackle these issues is an ongoing endeavor in the realm of data mining.

 

The proposed special issue aims to shed light on recent developments and findings in these closely related domains. By offering a platform for expert researchers, scholars, and practitioners from computational intelligence, information security, and data mining to share their latest findings, we hope to encourage cross-disciplinary dialogue and stimulate innovative approaches. Through this convergence of expertise, we aim to expedite the development of more secure, efficient, and intelligent data mining systems, thus contributing to the progression of this critical field. Essentially, the proposed special issue strives to depict not only the state-of-the-art in data mining, computational intelligence, and information security, but also to chart the trajectory for future research directions and applications.

 

The potential topics include but are not limited to:

 

1. Computational Intelligence in Data Mining

(1) Novel data mining algorithms based on Artificial Neural Systems, Evolutionary Programming, and Bayesian Learning

(2) Autonomy-oriented computing for data mining

(3) Application of Reinforcement Learning and Supervised Learning in data mining

(4) Intelligent systems for data mining

(5) Integration of Swarm Intelligence and Multi-agent systems in data mining

(6) Bio-inspired computing methods such as Artificial Immune Systems, DNA Computing, and Biological Computing in data mining

(7) Use of Particle Swarm Optimization, Multi-Objective EA, and other evolutionary algorithms in data mining

(8) Knowledge Discovery techniques for complex datasets

(9) Information Security in Data Mining

 

2. Cryptography and coding techniques for secure data mining

(1) Data Privacy and Information hiding in data mining

(2) Authentication and Authorization in data mining processes

(3) Security management and Internet/Intranet Security in data mining

(4) Mobile Communications Security and Network & Wireless Security for data mining

(5) Cryptanalysis and Public Key Infrastructure in data mining

(6) Applications of Data Mining

 

3. Biometrics and Digital Signatures applications in data mining

(1) Database Security and System Security in data mining applications

(2) Financial Security and Electronic Commerce Security in data mining

(3) Application of data mining in Statistical Data Analysis and Detection of Abnormality

(4) Web Privacy, Web Authentication, and Web Security and Integrity in data mining applications

 

We invite high-quality original research papers as well as review articles. Submissions will undergo a rigorous review process to assess their novelty, significance to the field, and technical quality.


Keywords

Data Mining
Computational Intelligence
Intelligent Systems
Information Security
Knowledge Engineering

Published Papers


  • Open Access

    ARTICLE

    Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

    Ruchun Jia, Jianwei Zhang, Yi Lin
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.044149
    (This article belongs to this Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
    Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through privacy, integrity, and availability. The… More >

  • Open Access

    ARTICLE

    Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

    Ali Hamid Farea, Omar H. Alhazmi, Kerem Kucuk
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.045794
    (This article belongs to this Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
    Abstract While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work. AI2AI employs AI techniques to… More >

  • Open Access

    ARTICLE

    Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

    Liya Yue, Pei Hu, Shu-Chuan Chu, Jeng-Shyang Pan
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046962
    (This article belongs to this Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
    Abstract Speech emotion recognition (SER) uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions. The number of features acquired with acoustic analysis is extremely high, so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system. The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy. First, we use the information gain and Fisher Score to sort the features extracted from signals. Then, we employ a multi-objective ranking method to evaluate these features and… More >

  • Open Access

    ARTICLE

    Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

    Ming Wan, Minglei Hao, Jiawei Li, Jiangyuan Yao, Yan Song
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3573-3592, 2023, DOI:10.32604/cmc.2023.044857
    (This article belongs to this Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
    Abstract The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged into different functional patterns due… More >

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