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  • Open Access

    ARTICLE

    Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

    Mohammed Maray1, Hamed Alqahtani2, Saud S. Alotaibi3, Fatma S. Alrayes4, Nuha Alshuqayran5, Mrim M. Alnfiai6, Amal S. Mehanna7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3101-3115, 2023, DOI:10.32604/cmc.2023.032969

    Abstract Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computer-based systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyber-attacks and threaten the integrity, secrecy, and accessibility… More >

  • Open Access

    ARTICLE

    Android Malware Detection Using ResNet-50 Stacking

    Lojain Nahhas1, Marwan Albahar1,*, Abdullah Alammari2, Anca Jurcut3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3997-4014, 2023, DOI:10.32604/cmc.2023.028316

    Abstract There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious… More >

  • Open Access

    ARTICLE

    Impact of Portable Executable Header Features on Malware Detection Accuracy

    Hasan H. Al-Khshali1,*, Muhammad Ilyas2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 153-178, 2023, DOI:10.32604/cmc.2023.032182

    Abstract One aspect of cybersecurity, incorporates the study of Portable Executables (PE) files maleficence. Artificial Intelligence (AI) can be employed in such studies, since AI has the ability to discriminate benign from malicious files. In this study, an exclusive set of 29 features was collected from trusted implementations, this set was used as a baseline to analyze the presented work in this research. A Decision Tree (DT) and Neural Network Multi-Layer Perceptron (NN-MLPC) algorithms were utilized during this work. Both algorithms were chosen after testing a few diverse procedures. This work implements a method of subgrouping features to answer questions such… More >

  • Open Access

    ARTICLE

    Optimal Deep Belief Network Enabled Malware Detection and Classification Model

    P. Pandi Chandran1,*, N. Hema Rajini2, M. Jeyakarthic3

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3349-3364, 2023, DOI:10.32604/iasc.2023.029946

    Abstract Cybercrime has increased considerably in recent times by creating new methods of stealing, changing, and destroying data in daily lives. Portable Document Format (PDF) has been traditionally utilized as a popular way of spreading malware. The recent advances of machine learning (ML) and deep learning (DL) models are utilized to detect and classify malware. With this motivation, this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification (MFODBN-MDC) technique. The major intention of the MFODBN-MDC technique is for identifying and classifying the presence of malware exist in the PDFs. The… More >

  • Open Access

    ARTICLE

    Swarm Optimization and Machine Learning for Android Malware Detection

    K. Santosh Jhansi1,2,*, P. Ravi Kiran Varma2, Sujata Chakravarty3

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878

    Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms… More >

  • Open Access

    ARTICLE

    Optimal Unification of Static and Dynamic Features for Smartphone Security Analysis

    Sumit Kumar1,*, S. Indu2, Gurjit Singh Walia1

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1035-1051, 2023, DOI:10.32604/iasc.2023.024469

    Abstract Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification… More >

  • Open Access

    ARTICLE

    Crypto Hash Based Malware Detection in IoMT Framework

    R Punithavathi1, K Venkatachalam2, Mehedi Masud3, Mohammed A. AlZain4, Mohamed Abouhawwash5,6,*

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 559-574, 2022, DOI:10.32604/iasc.2022.024715

    Abstract The challenges in providing e-health services with the help of Internet of Medical Things (IoMT) is done by connecting to the smart medical devices. Through IoMT sensor devices/smart devices, physicians share the sensitive information of the patient. However, protecting the patient health care details from malware attack is necessary in this advanced digital scenario. Therefore, it is needed to implement cryptographic algorithm to enhance security, safety, reliability, preventing details from malware attacks and privacy of medical data. Nowadays blockchain has become a prominent technology for storing medical data securely and transmit through IoMT concept. The issues in the existing research… More >

  • Open Access

    ARTICLE

    Malware Detection Using Decision Tree Based SVM Classifier for IoT

    Anwer Mustafa Hilal1,*, Siwar Ben Haj Hassine2, Souad Larabi-Marie-Sainte3, Nadhem Nemri2, Mohamed K. Nour4, Abdelwahed Motwakel1, Abu Sarwar Zamani1, Mesfer Al Duhayyim5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 713-726, 2022, DOI:10.32604/cmc.2022.024501

    Abstract The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware… More >

  • Open Access

    ARTICLE

    A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

    Abdulbasit A. Darem*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 461-479, 2022, DOI:10.32604/cmc.2022.023566

    Abstract Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The deep learning-based approach is recently… More >

  • Open Access

    ARTICLE

    Android Malware Detection Based on Feature Selection and Weight Measurement

    Huizhong Sun1, Guosheng Xu1,*, Zhimin Wu2, Ruijie Quan3

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 585-600, 2022, DOI:10.32604/iasc.2022.023874

    Abstract With the rapid development of Android devices, Android is currently one of the most popular mobile operating systems. However, it is also believed to be an entry point of many attack vectors. The existing Android malware detection method does not fare well when dealing with complex and intelligent malware applications, especially those based on feature detection systems which have become increasingly elusive. Therefore, we propose a novel feature selection algorithm called frequency differential selection (FDS) and weight measurement for Android malware detection. The purpose is to solve the shortcomings of the existing feature selection algorithms in detection and to filter… More >

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