{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T20:06:31Z","timestamp":1767211591967,"version":"3.41.0"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,9,30]]},"abstract":"<jats:p>\n            With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals\u2019 sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes\n            <jats:italic>BetaLogger<\/jats:italic>\n            , an Android-based application that highlights the issue of leaking smartphone users\u2019 privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope).\n            <jats:italic>BetaLogger<\/jats:italic>\n            efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN).\n            <jats:italic>BetaLogger<\/jats:italic>\n            is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that\n            <jats:italic>BetaLogger<\/jats:italic>\n            generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.\n          <\/jats:p>","DOI":"10.1145\/3460392","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T20:06:29Z","timestamp":1625083589000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network"],"prefix":"10.1145","volume":"20","author":[{"given":"Abdul Rehman","family":"Javed","sequence":"first","affiliation":[{"name":"Air University, Islamabad, Pakistan"}]},{"given":"Saif Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Air University, Islamabad, Pakistan"}]},{"given":"Mohib Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"University of Wah, Islamabad, Pakistan"}]},{"given":"Mamoun","family":"Alazab","sequence":"additional","affiliation":[{"name":"Charles Darwin University, Australia"}]},{"given":"Habib Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Qatar University, Qatar"}]}],"member":"320","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10922-021-09587-8"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2991067"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CTC.2013.16"},{"volume-title":"Complex Pattern Mining","author":"Andresini Giuseppina","key":"e_1_2_1_4_1","unstructured":"Giuseppina Andresini , Annalisa Appice , and Donato Malerba . 2020. Dealing with class imbalance in android malware detection by cascading clustering and classification . In Complex Pattern Mining . Springer , Switzerland , 173\u2013187. Giuseppina Andresini, Annalisa Appice, and Donato Malerba. 2020. Dealing with class imbalance in android malware detection by cascading clustering and classification. In Complex Pattern Mining. Springer, Switzerland, 173\u2013187."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2853"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.02.078"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01659-7"},{"key":"e_1_2_1_8_1","volume-title":"Praveen Kumar Reddy Maddikunta, Fang Fang, Pubudu N. Pathirana et\u00a0al.","author":"Deepa Natarajan","year":"2020","unstructured":"Natarajan Deepa , Quoc-Viet Pham , Dinh C. Nguyen , Sweta Bhattacharya , Thippa Reddy Gadekallu , Praveen Kumar Reddy Maddikunta, Fang Fang, Pubudu N. Pathirana et\u00a0al. 2020 . A survey on blockchain for big data: Approaches, opportunities, and future directions. Retrieved from https:\/\/arXiv:2009.00858. Natarajan Deepa, Quoc-Viet Pham, Dinh C. Nguyen, Sweta Bhattacharya, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Fang Fang, Pubudu N. Pathirana et\u00a0al. 2020. A survey on blockchain for big data: Approaches, opportunities, and future directions. Retrieved from https:\/\/arXiv:2009.00858."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1976.5408784"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_1_11_1","first-page":"1","article-title":"A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU","volume":"0","author":"Gadekallu Thippa Reddy","year":"2020","unstructured":"Thippa Reddy Gadekallu , Dharmendra Singh Rajput , M. Praveen Kumar Reddy , Kuruva Lakshmanna , Sweta Bhattacharya , Saurabh Singh , Alireza Jolfaei , and Mamoun Alazab . 2020 . A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU . J. Real-Time Image Process. 0 , 0 (2020), 1 \u2013 14 . Thippa Reddy Gadekallu, Dharmendra Singh Rajput, M. Praveen Kumar Reddy, Kuruva Lakshmanna, Sweta Bhattacharya, Saurabh Singh, Alireza Jolfaei, and Mamoun Alazab. 2020. A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J. Real-Time Image Process. 0, 0 (2020), 1\u201314.","journal-title":"J. Real-Time Image Process."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2015.12.001"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.008"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2988160"},{"key":"e_1_2_1_15_1","first-page":"1","article-title":"AlphaLogger: Detecting motion-based side-channel attack using smartphone keystrokes","volume":"0","author":"Javed Abdul Rehman","year":"2020","unstructured":"Abdul Rehman Javed , Mirza Omer Beg , Muhammad Asim , Thar Baker , and Ali Hilal Al-Bayatti . 2020 . AlphaLogger: Detecting motion-based side-channel attack using smartphone keystrokes . J. Ambient Intell. Human. Comput. 0 , 0 (2020), 1 \u2013 14 . Abdul Rehman Javed, Mirza Omer Beg, Muhammad Asim, Thar Baker, and Ali Hilal Al-Bayatti. 2020. AlphaLogger: Detecting motion-based side-channel attack using smartphone keystrokes. J. Ambient Intell. Human. 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