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  • 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 More >

  • Open Access

    ARTICLE

    Investigation of Android Malware Using Deep Learning Approach

    V. Joseph Raymond1,2,*, R. Jeberson Retna Raj1

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2413-2429, 2023, DOI:10.32604/iasc.2023.030527

    Abstract In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits… More >

  • Open Access

    ARTICLE

    Randomized MILP framework for Securing Virtual Machines from Malware Attacks

    R. Mangalagowri1,*, Revathi Venkataraman2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1565-1580, 2023, DOI:10.32604/iasc.2023.026360

    Abstract Cloud computing involves remote server deployments with public network infrastructures that allow clients to access computational resources. Virtual Machines (VMs) are supplied on requests and launched without interactions from service providers. Intruders can target these servers and establish malicious connections on VMs for carrying out attacks on other clustered VMs. The existing system has issues with execution time and false-positive rates. Hence, the overall system performance is degraded considerably. The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target… 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 More >

  • Open Access

    ARTICLE

    A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

    Attia Qammar1, Ahmad Karim1,*, Yasser Alharbi2, Mohammad Alsaffar2, Abdullah Alharbi2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 915-930, 2022, DOI:10.32604/csse.2022.023652

    Abstract Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination More >

  • Open Access

    ARTICLE

    Detecting IoT Botnet in 5G Core Network Using Machine Learning

    Ye-Eun Kim1, Min-Gyu Kim2, Hwankuk Kim2,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4467-4488, 2022, DOI:10.32604/cmc.2022.026581

    Abstract As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type More >

  • Open Access

    ARTICLE

    Deobfuscating Mobile Malware for Identifying Concealed Behaviors

    Dongho Lee, Geochang Jeon, Sunjun Lee, Haehyun Cho*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5909-5923, 2022, DOI:10.32604/cmc.2022.026395

    Abstract The smart phone market is continuously increasing and there are more than 6 billion of smart phone users worldwide with the aid of the 5G technology. Among them Android occupies 87% of the market share. Naturally, the widespread Android smartphones has drawn the attention of the attackers who implement and spread malware. Consequently, currently the number of malware targeting Android mobile phones is ever increasing. Therefore, it is a critical task to find and detect malicious behaviors of malware in a timely manner. However, unfortunately, attackers use a variety of obfuscation techniques for malware to… 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… More >

  • Open Access

    ARTICLE

    Ransomware Classification Framework Using the Behavioral Performance Visualization of Execution Objects

    Jun-Seob Kim, Ki-Woong Park*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3401-3424, 2022, DOI:10.32604/cmc.2022.026621

    Abstract A ransomware attack that interrupted the operation of Colonial Pipeline (a large U.S. oil pipeline company), showed that security threats by malware have become serious enough to affect industries and social infrastructure rather than individuals alone. The agents and characteristics of attacks should be identified, and appropriate strategies should be established accordingly in order to respond to such attacks. For this purpose, the first task that must be performed is malware classification. Malware creators are well aware of this and apply various concealment and avoidance techniques, making it difficult to classify malware. This study focuses… More >

  • Open Access

    ARTICLE

    Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

    Mohammad Hafiz Mohd Yusof1,*, Abdullah Mohd Zin2, Nurhizam Safie Mohd Satar2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571

    Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had… More >

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