Special Issues
Table of Content

Advances in Information Security Application

Submission Deadline: 31 January 2023 (closed) View: 54

Guest Editors

Prof. Ilsun You, Kookmin University, South Korea.
Prof. Isaac Woungang, Ryerson University, Canada.
Prof. Hwankuk Kim, Sangmyung University, South Korea.


World Conference on Information Security Applications (WISA) is one of the main security research venues hosted by the Korea Institute of Information Security and Cryptology (KIISC) and sponsored by the Ministry of Science, ICT and Future Planning (MSIP), and co-sponsored by the Electronics and Telecommunications Research Institute (ETRI), the Korea Internet and Security Agency (KISA), and the National Security Research Institute (NSRI). The primary focus of WISA 2022 will be on systems and network security, including all other technical and practical aspects of security applications. This Special Issue will include extended versions of selected papers from WISA 2022, along with general papers closely related to the conference themes.


Potential topics include but are not limited to:

- Analysis of network and security protocols

- Anonymity and censorship-resistant technologies

- Applications of cryptographic techniques

- Authentication and authorization

- Automated tools for source code/binary analysis

- Automobile security

- Botnet defense

- Blockchain security

- Critical infrastructure security

- Denial-of-service attacks and countermeasures

- Digital Forensics

- Embedded systems security

- Exploit techniques and automation

- Hardware and physical security

- HCI security and privacy

- Intrusion detection and prevention

- Malware analysis

- Mobile/wireless/cellular system security

- Network-based attacks

- Network infrastructure security

- Operating system security

- Practical cryptanalysis (hardware, DRM, etc.)

- Security policy

- Side channel attacks and countermeasures

- Storage and file systems security

- Techniques for developing secure systems

- Trustworthy computing

- Trusted execution environments

- Unmanned System Security for Vehicle/Drone/Ship Systems

- Vulnerability research

- Web security


Information Security, Privacy and Trust, Cybersecurity, IoT/CPS security

Published Papers

  • Open Access


    Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity

    Ju Hyeon Lee, Jiho Shin, Jung Taek Seo
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 757-779, 2023, DOI:10.32604/cmc.2023.039461
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract As energy-related problems continue to emerge, the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration. Renewable energy is becoming increasingly important, with solar power accounting for the most significant proportion of renewables. As the scale and importance of solar energy have increased, cyber threats against solar power plants have also increased. So, we need an anomaly detection system that effectively detects cyber threats to solar power plants. However, as mentioned earlier, the existing solar power plant anomaly detection system monitors only operating information such as power generation, making… More >

  • Open Access


    Efficient Remote Identification for Drone Swarms

    Kang-Moon Seo, Jane Kim, Soojin Lee, Jun-Woo Kwon, Seung-Hyun Seo
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2937-2958, 2023, DOI:10.32604/cmc.2023.039459
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract With the advancement of unmanned aerial vehicle (UAV) technology, the market for drones and the cooperation of many drones are expanding. Drone swarms move together in multiple regions to perform their tasks. A Ground Control Server (GCS) located in each region identifies drone swarm members to prevent unauthorized drones from trespassing. Studies on drone identification have been actively conducted, but existing studies did not consider multiple drone identification environments. Thus, developing a secure and effective identification mechanism for drone swarms is necessary. We suggested a novel approach for the remote identification of drone swarms. For… More >

  • Open Access


    A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

    Muhammad Firdaus, Harashta Tatimma Larasati, Kyung-Hyune Rhee
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2959-2978, 2023, DOI:10.32604/cmc.2023.039487
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks… More >

  • Open Access


    FedTC: A Personalized Federated Learning Method with Two Classifiers

    Yang Liu, Jiabo Wang, Qinbo Liu, Mehdi Gheisari, Wanyin Xu, Zoe L. Jiang, Jiajia Zhang
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3013-3027, 2023, DOI:10.32604/cmc.2023.039452
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Centralized training of deep learning models poses privacy risks that hinder their deployment. Federated learning (FL) has emerged as a solution to address these risks, allowing multiple clients to train deep learning models collaboratively without sharing raw data. However, FL is vulnerable to the impact of heterogeneous distributed data, which weakens convergence stability and suboptimal performance of the trained model on local data. This is due to the discarding of the old local model at each round of training, which results in the loss of personalized information in the model critical for maintaining model accuracy… More >

  • Open Access


    Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

    Borja Bordel Sánchez, Ramón Alcarria, Tomás Robles
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3189-3218, 2023, DOI:10.32604/cmc.2023.039466
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Cyber-Physical Systems are very vulnerable to sparse sensor attacks. But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely. Therefore, in this paper, we propose a new non-linear generalized model to describe Cyber-Physical Systems. This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and random effects in the physical and computational worlds. Besides, the digitalization stage in hardware devices is represented too. Attackers and most critical sparse sensor attacks are described through a stochastic process. The reconstruction and protection mechanisms… More >

  • Open Access


    A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems

    Seyoung Lee, Wonsuk Choi, Insup Kim, Ganggyu Lee, Dong Hoon Lee
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These… More >

  • Open Access


    Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

    Youngeun An, Jimin Lee, EunSang Bak, Sungbum Pan
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1817-1832, 2023, DOI:10.32604/cmc.2023.039460
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces. Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model. In contrast, this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions, especially around the eyes, eyebrows, nose, and mouth. Then, we apply a new classifier using an ensemble network to increase emotion recognition accuracy. The emotion recognition performance was More >

  • Open Access


    Blockchain-Based Secure and Fair IoT Data Trading System with Bilateral Authorization

    Youngho Park, Mi Hyeon Jeon, Sang Uk Shin
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1871-1890, 2023, DOI:10.32604/cmc.2023.039462
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract These days, data is regarded as a valuable asset in the era of the data economy, which demands a trading platform for buying and selling data. However, online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other. Therefore, in this paper, a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption. The proposed system enables bilateral authorization, where data trading between a seller and a buyer is accomplished only if their policies,… More >

  • Open Access


    CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

    Hyeon Park, SeoYeon Kim, Seok Min Ko, TaeGuen Kim
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. More >

  • Open Access


    An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

    Nouf Saeed Alotaibi
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2061-2078, 2023, DOI:10.32604/cmc.2023.039446
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the… More >

  • Open Access


    XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

    Yuna Han, Hangbae Chang
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous… More >

  • Open Access


    Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

    Semi Park, Kyungho Lee
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 813-826, 2023, DOI:10.32604/cmc.2023.035313
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study,… More >

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