Table of Content

Machine Learning Empowered Secure Computing for Intelligent Systems

Submission Deadline: 28 February 2022 (closed)

Guest Editors

Dr. Muhammad Adnan Khan, Gachon University, Korea.
Dr. Muhammad Aamer Saleem, Hamdard University, Pakistan.
Dr. Rizwan Ali Naqvi, Sejong University, Korea.

Summary

Artificial intelligence (AI) and machine learning (ML) have been put thoroughly into practice, with more promotion being given, to enhance continuity, cybersecurity in cloud computing, Internet services, and the Internet-of-Things. Machine learning algorithms, such as AI, are used to track complex cyber threats that cannot be readily identified by conventional detection methods. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost.

By far, the most difficult challenge is how to exploit AI and machine learning algorithms for improved safe service computation while maintaining the user's privacy. The robustness of AI and deep learning as well as the reliability and privacy of data is an important part of today's modern computing. This topic aims to determine the security issues of using AI to protect systems. To be able to enforce them in reality, privacy would have to be held throughout the implementation process.

In this special issue of the journal, we are finding groundbreaking applications and undisclosed work related to artificial intelligence and machine learning for more stable and privacy cleaning computing. By reflecting on the role of machine learning in information security, we are looking to discuss recent developments in the area of machine learning and privacy-preserving strategies. To make our computation more secure and confidential, we aim to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and privacy of information.


Keywords

Suggested topics include, but are not limited to, the following:
• Cybersecurity
• Spam Detection
• Secure online social networks
• Anomaly and intrusion detection in the network
• Malware analysis and detection
• Security models based AI for protecting IoT networks
• Intrusion Detection for IoT systems
• Distributed AI Systems and Architectures
• eBusiness, eCommerce, eHealth, eLearning
• Finance and AI
• Extreme Machine Learning
• Applications of neural networks in data analytics
• CNN, LSTM
• Automation and control system
• Smart mobility and transportation
• Signal and Image Processing

Published Papers


  • Open Access

    ARTICLE

    Machine Learning Empowered Electricity Consumption Prediction

    Maissa A. Al Metrik, Dhiaa A. Musleh
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1427-1444, 2022, DOI:10.32604/cmc.2022.025722
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes… More >

  • Open Access

    ARTICLE

    An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

    Mutasem K. Alsmadi, Ghaith M. Jaradat, Malek Alzaqebah, Ibrahim ALmarashdeh, Fahad A. Alghamdi, Rami Mustafa A. Mohammad, Nahier Aldhafferi, Abdullah Alqahtani
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1995-2014, 2022, DOI:10.32604/cmc.2022.025077
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity, capability, and capacity. Such tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or decision-making. Swarm intelligence techniques including Particle Swarm Optimization (PSO) have proved to be effective examples. Different recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling, machine scheduling, etc. However, having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is guaranteed. This research paper seeks… More >

  • Open Access

    ARTICLE

    A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

    Farman Ali, Sadia Khan, Arbab Waseem Abbas, Babar Shah, Tariq Hussain, Dongho Song, Shaker EI-Sappagh, Jaiteg Singh
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 73-92, 2022, DOI:10.32604/cmc.2022.024103
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning… More >

  • Open Access

    ARTICLE

    Windows 10's Browser Forensic Analysis for Tracing P2P Networks’ Anonymous Attacks

    Saima Kauser, Tauqeer Safdar Malik, Mohd Hilmi Hasan, Emelia Akashah P. Akhir, Syed Muhammad Husnain Kazmi
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1251-1273, 2022, DOI:10.32604/cmc.2022.022475
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract A web browser is the most basic tool for accessing the internet from any of the machines/equipment. Recently, data breaches have been reported frequently from users who are concerned about their personal information, as well as threats from criminal actors. Giving loss of data and information to an innocent user comes under the jurisdiction of cyber-attack. These kinds of cyber-attacks are far more dangerous when it comes to the many types of devices employed in an internet of things (IoT) environment. Continuous surveillance of IoT devices and forensic tools are required to overcome the issues pertaining to secure data and… More >

  • Open Access

    ARTICLE

    Man Overboard Detection System Using IoT for Navigation Model

    Hüseyin Gürüler, Murat Altun, Faheem Khan, Taegkeun Whangbo
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4955-4969, 2022, DOI:10.32604/cmc.2022.023556
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry, ro-ro, catamaran, frigate, yacht that are the vehicles services for the purpose of logistic and passenger transport. In this paper, all processes in the event of Man overboard (MOB)are initiated for smart transportation. In MOB the falling person is totally dependent on the person who first saw the falling person. The main objective of this paper is to develop a solution to this significant problem. If a staff member or a passenger does not see the fall into the… More >

  • Open Access

    ARTICLE

    Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

    Uğur Ayvaz, Hüseyin Gürüler, Faheem Khan, Naveed Ahmed, Taegkeun Whangbo, Abdusalomov Akmalbek Bobomirzaevich
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5511-5521, 2022, DOI:10.32604/cmc.2022.023278
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals. One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs are successful in processing the voice signal with high accuracies. MFCCs represents a sequence of voice signal-specific features. This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings. Since the human perception of sound is not linear, after the filterbank step in the MFCC… More >

  • Open Access

    ARTICLE

    Interpretable and Adaptable Early Warning Learning Analytics Model

    Shaleeza Sohail, Atif Alvi, Aasia Khanum
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3211-3225, 2022, DOI:10.32604/cmc.2022.023560
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain. Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions. Recently, some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified. However, adaptability is not specifically considered in this domain. This paper presents a new framework based on hybrid statistical fuzzy theory to overcome these… More >

  • Open Access

    ARTICLE

    An Enhanced Privacy Preserving, Secure and Efficient Authentication Protocol for VANET

    Safiullah Khan, Ali Raza, Seong Oun Hwang
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3703-3719, 2022, DOI:10.32604/cmc.2022.023476
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Vehicular ad hoc networks (VANETs) have attracted growing interest in both academia and industry because they can provide a viable solution that improves road safety and comfort for travelers on roads. However, wireless communications over open-access environments face many security and privacy issues that may affect deployment of large-scale VANETs. Researchers have proposed different protocols to address security and privacy issues in a VANET, and in this study we cryptanalyze some of the privacy preserving protocols to show that all existing protocols are vulnerable to the Sybil attack. The Sybil attack can be used by malicious actors to create fake… More >

  • Open Access

    ARTICLE

    Performance Evaluation of Topological Infrastructure in Internet-of-Things-Enabled Serious Games

    Shabir Ahmad, Faheem Khan, Taeg Keun Whangbo
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2653-2666, 2022, DOI:10.32604/cmc.2022.022821
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Serious games have recently enticed many researchers due to their wide range of capabilities. A serious game is a mean of gaming for a serious job such as healthcare, education, and entertainment purposes. With the advancement in the Internet of Things, new research directions are paving the way in serious games. However, the internet connectivity of players in Internet-of-things-enabled serious games is a matter of concern and has been worth investigating. Different studies on topologies, frameworks, and architecture of communication technologies are conducted to integrate them with serious games on machine and network levels. However, the Internet of things, whose… More >

  • Open Access

    ARTICLE

    Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

    Dhiaa A. Musleh, Taef A. Alkhales, Reem A. Almakki, Shahad E. Alnajim, Shaden K. Almarshad, Rana S. Alhasaniah, Sumayh S. Aljameel, Abdullah A. Almuqhim
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3463-3477, 2022, DOI:10.32604/cmc.2022.022508
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Depression has been a major global concern for a long time, with the disease affecting aspects of many people's daily lives, such as their moods, eating habits, and social interactions. In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases. However, people all over the world, including Arab citizens, tend to express their feelings openly on social media, especially Twitter, as it is a platform designed to enable the expression of emotions through short texts, pictures, or videos. Users are inclined to treat their Twitter accounts as diaries because the platform… More >

  • Open Access

    ARTICLE

    Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

    Usman Ali, Muhammad Tariq Mahmood
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1597-1611, 2022, DOI:10.32604/cmc.2022.022219
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operator is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on local binary pattern (LBP) with adaptive threshold for blur detection. The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur, that may… More >

  • Open Access

    ARTICLE

    DDoS Detection in SDN using Machine Learning Techniques

    Muhammad Waqas Nadeem, Hock Guan Goh, Vasaki Ponnusamy, Yichiet Aun
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 771-789, 2022, DOI:10.32604/cmc.2022.021669
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a… More >

  • Open Access

    ARTICLE

    Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning

    Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6189-6204, 2022, DOI:10.32604/cmc.2022.021657
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm… More >

  • Open Access

    ARTICLE

    A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

    Muhammad Aasim Qureshi, Muhammad Asif, Mohd Fadzil Hassan, Ghulam Mustafa, Muhammad Khurram Ehsan, Aasim Ali, Unaza Sajid
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544
    (This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
    Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53… More >

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