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      The current state and future of mobile security in the light of the recent mobile security threat reports

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          Abstract

          Smartphones have become small computers that meet many of our needs, from e-mail and banking transactions to communication and social media use. In line with these attractive functions, the use of smartphones has greatly increased over the years. One of the most important features of these mobile devices is that they offer users many mobile applications that they can install. However, hacker attacks and the spread of malware have also increased. Today, current mobile malware detection and defense technologies are still inadequate. Mobile security is not only directly related to the operating system and used device but also related with communication over the internet, data encryption, data summarization, and users’ privacy awareness. The main aim and contribution of this study are to collect the current state of mobile security and highlight the future of mobile security in light of the recent mobile security threat reports. The studies in the field of malware, attack types, and security vulnerabilities concerning the usage of smartphones were analyzed. The malware detection techniques were analyzed into two categories: signature-based and machine learning (behavior detection)-based techniques. Additionally, the current threats and prevention methods were described. Finally, a future direction is highlighted in the light of the current mobile security reports.

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          Is Open Access

          IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

          Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.
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            Machine learning based mobile malware detection using highly imbalanced network traffic

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              Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks

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                Author and article information

                Contributors
                accinar@selcuk.edu.tr , ahmetcevahircinar@gmail.com
                218273001009@lisansustu.selcuk.edu.tr
                Journal
                Multimed Tools Appl
                Multimed Tools Appl
                Multimedia Tools and Applications
                Springer US (New York )
                1380-7501
                1573-7721
                30 January 2023
                : 1-13
                Affiliations
                GRID grid.17242.32, ISNI 0000 0001 2308 7215, Department of Computer Engineering, Faculty of Technology, , Selçuk University, ; Konya, Turkey
                Author information
                https://orcid.org/http://orcid.org/0000-0001-5596-6767
                Article
                14400
                10.1007/s11042-023-14400-6
                9885923
                1337d48d-7e58-417a-8429-029fd8b4df77
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 30 January 2022
                : 6 May 2022
                : 22 January 2023
                Categories
                Article

                Graphics & Multimedia design
                mobile threat report,mobile security,smartphone security,security,mobile applications

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