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  • Open Access

    ARTICLE

    Reverse Engineering of Mobile Banking Applications

    Syeda Warda Asher1, Sadeeq Jan1,*, George Tsaramirsis2, Fazal Qudus Khan3, Abdullah Khalil1, Muhammad Obaidullah4

    Computer Systems Science and Engineering, Vol.38, No.3, pp. 265-278, 2021, DOI:10.32604/csse.2021.016787

    Abstract Software reverse engineering is the process of analyzing a software system to extract the design and implementation details. Reverse engineering provides the source code of an application, the insight view of the architecture and the third-party dependencies. From a security perspective, it is mostly used for finding vulnerabilities and attacking or cracking an application. The process is carried out either by obtaining the code in plaintext or reading it through the binaries or mnemonics. Nowadays, reverse engineering is widely used for mobile applications and is considered a security risk. The Open Web Application Security Project (OWASP), a leading security research… More >

  • Open Access

    ARTICLE

    Analysis of Security Testing Techniques

    Omer Bin Tauqeer1, Sadeeq Jan1,*, Alaa Omar Khadidos2, Adil Omar Khadidos3, Fazal Qudus Khan3, Sana Khattak1

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 291-306, 2021, DOI:10.32604/iasc.2021.017260

    Abstract In the past decades, a significant increase has been observed in cyber-attacks on the web-based systems used for financial purposes. Such individual systems often contain security weaknesses, called vulnerabilities that can be exploited for malicious purposes. The exploitation of such vulnerabilities can result in disclosure and manipulation of sensitive data as well as have destructive effects. To protect such systems, security testing is required on a periodic basis. Various detection and assessment techniques have been suggested by developers and researchers to address these security issues. In this paper, we survey the contributions of academia in the field of security testing… More >

  • Open Access

    ARTICLE

    A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked Autoencoder

    Nojood O. Aljehane*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3915-3929, 2021, DOI:10.32604/cmc.2021.017905

    Abstract Cyber physical systems (CPSs) are a networked system of cyber (computation, communication) and physical (sensors, actuators) elements that interact in a feedback loop with the assistance of human interference. Generally, CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices. Increased utilization of CPSs, however, poses many threats, which may be of major significance for users. Such security issues in CPSs represent a global issue; therefore, developing a robust, secure, and effective CPS is currently a hot research topic. To resolve this issue, an intrusion… More >

  • Open Access

    ARTICLE

    Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

    Thavavel Vaiyapuri*, Adel Binbusayyis

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665

    Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More >

  • Open Access

    ARTICLE

    Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

    Sajid Habib Gill1, Noor Ahmed Sheikh1, Samina Rajpar1, Zain ul Abidin2, N. Z. Jhanjhi3,*, Muneer Ahmad4, Mirza Abdur Razzaq1, Sultan S. Alshamrani5, Yasir Malik6, Fehmi Jaafar7

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3773-3787, 2021, DOI:10.32604/cmc.2021.016001

    Abstract Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a… More >

  • Open Access

    ARTICLE

    A Survey on Security Threats and Solutions of Bitcoin

    Le Lai1,*,Tongqing Zhou1, Zhiping Cai1, Zhiyao Liang2, Hao Bai1

    Journal of Cyber Security, Vol.3, No.1, pp. 29-44, 2021, DOI:10.32604/jcs.2021.016349

    Abstract Bitcoin is known as the first decentralized digital currency around the world. It uses blockchain technology to store transaction data in a distributed public ledger, is a distributed ledger that removes third-party trust institutions. Since its invention, bitcoin has achieved great success, has a market value of about $200 billion. However, while bitcoin has brought a wide and far-reaching impact in the financial field, it has also exposed some security problems, such as selfish mining attacks, Sybil attack, eclipse attacks, routing attacks, EREBUS attacks, and so on. This paper gives a comprehensive overview of various attack scenarios that the bitcoin… More >

  • Open Access

    ARTICLE

    Food Insecurity and Depressive Symptoms in Adolescents Aged 12–15 Years from Low- and Middle-Income Countries

    Meng Wang*

    International Journal of Mental Health Promotion, Vol.23, No.2, pp. 177-187, 2021, DOI:10.32604/IJMHP.2021.016466

    Abstract Purpose: Little is known about the role of food insecurity (FIS) on depressive symptoms among adolescents. Thus, this study aimed to explore the association between FIS and depressive symptoms among adolescents aged 12–15 years from low- and middle-income countries across the world. Methods: Data from the Global school-based Student Health Survey were analyzed in 51,702 adolescents [mean (SD) age 13.8 (1.0) years; 49.3% girls). Self-reported measures assessed depressive symptoms during the past 12 months, and food insecurity. Participants reporting yes for depressive symptoms. FIS was categorized into five levels, including ‘never’, ‘rarely’, ‘sometimes’, ‘most of the time’ and ‘always’. Multivariable… More >

  • Open Access

    ARTICLE

    An Automated System to Predict Popular Cybersecurity News Using Document Embeddings

    Ramsha Saeed1, Saddaf Rubab1, Sara Asif1, Malik M. Khan1, Saeed Murtaza1, Seifedine Kadry2, Yunyoung Nam3,*, Muhammad Attique Khan4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 533-547, 2021, DOI:10.32604/cmes.2021.014355

    Abstract The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically enriched model is proposed which… More >

  • Open Access

    ARTICLE

    Decision Making in Internet of Vehicles Using Pervasive Trusted Computing Scheme

    Geetanjali Rathee1, Razi Iqbal2,*, Adel Khelifi3

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2755-2769, 2021, DOI:10.32604/cmc.2021.017000

    Abstract Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention. Recently Internet of Vehicles (IoVs) has been introduced as one of the applications of pervasive computing that addresses the road safety challenges. Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues. Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data. Due to the lack of existing transportation integration schemes, IoV has not been completely explored by business organizations.… More >

  • Open Access

    ARTICLE

    Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

    Jinxin Zuo1,2, Yueming Lu1,2,*, Hui Gao2,3, Tong Peng1,2, Ziyv Guo2,3, Tong An1,2, Enjie Liu4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2579-2595, 2021, DOI:10.32604/cmc.2021.016623

    Abstract With the skyrocketing development of technologies, there are many issues in information security quantitative evaluation (ISQE) of complex heterogeneous information systems (CHISs). The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently. In this paper, we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process. The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively. Firstly, with the support of asset identification and topology data, we… More >

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