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922 results on '"Ahmed, Kawsar"'

6. Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Author

Mehedi, Sk. Tanzir, Anwar, Adnan, Rahman, Ziaur, Ahmed, Kawsar, and Islam, Rafiqul

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, I.2, K.3.2
Abstract

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability., Comment: 12 pages, 13 Figures, 4 tables IEEE Transaction

Published
2022
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17. Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Author

Mehedi, Sk. Tanzir, Anwar, Adnan, Rahman, Ziaur, and Ahmed, Kawsar

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, I.2.6, K.3.2
Abstract

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security., Comment: 23 Pages, 14 Figures, 6 Tables with Appendix

Published
2021
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21. MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers

Author

Loba, Jannatul, Mia, Md. Rajib, Mahmud, Imran, Mahi, Md. Julkar Nayeen, Whaiduzzaman, Md., Ahmed, Kawsar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Younas, Muhammad, editor, Awan, Irfan, editor, Benbernou, Salima, editor, and Petcu, Dana, editor

Published
2023
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22. PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors

Author

Mia, Md. Rajib, Rahman, Md. Ashikur, Ali, Md. Mamun, Ahmed, Kawsar, Bui, Francis M., Mahmud, S M Hasan, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Satu, Md. Shahriare, editor, Moni, Mohammad Ali, editor, Kaiser, M. Shamim, editor, and Arefin, Mohammad Shamsul, editor

Published
2023
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23. Identification of Molecular Signatures and Pathways of Nasopharyngeal Carcinoma (NPC) Using Network-Based Approach

Author

Akhi, Alama Jannat, Ahmed, Kawsar, Habib, Md. Ahsan, Paul, Bikash Kumar, Akram, Mohammad Rubbyat, Saha, Sujay, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Hossain, Md. Sazzad, editor, Majumder, Satya Prasad, editor, Siddique, Nazmul, editor, and Hossain, Md. Shahadat, editor

Published
2023
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24. A Study on Graphene-Based Sensor Devices

Author

Tanvir, Jahid, Ahmed, Kawsar, Bui, Francis M., Patel, Shobhit K., Öchsner, Andreas, Series Editor, da Silva, Lucas F. M., Series Editor, Altenbach, Holm, Series Editor, Patel, Shobhit K., editor, Taya, Sofyan A., editor, Das, Sudipta, editor, and Vasu Babu, K., editor

Published
2023
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39. ML_SPS: Stroke Prediction System Employing Machine Learning Approach

Author

Hossain, Md. Sazzad, Shovo, Mehedi Hassan, Ali, Md. Mamun, Nayer, Md., Ahmed, Kawsar, Bui, Francis M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kumar, Ashwani, editor, Fister Jr., Iztok, editor, Gupta, P. K., editor, Debayle, Johan, editor, Zhang, Zuopeng Justin, editor, and Usman, Mohammed, editor

Published
2022
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40. DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep Learning and YOLOv5 Model

Author

Rahaman, Md. Abdur, Ali, Md. Mamun, Hossen, Md. Nazmul, Nayer, Md., Ahmed, Kawsar, Bui, Francis M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kumar, Ashwani, editor, Fister Jr., Iztok, editor, Gupta, P. K., editor, Debayle, Johan, editor, Zhang, Zuopeng Justin, editor, and Usman, Mohammed, editor

Published
2022
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45. Development of Photonic Crystal Fiber Based Gas/ Chemical Sensors

Author

Rifat, Ahmmed A., Ahmed, Kawsar, Asaduzzaman, Sayed, Paul, Bikash Kumar, and Ahmed, Rajib

Subjects
Physics - Applied Physics, Physics - Optics
Abstract

The development of highly-sensitive and miniaturized sensors that capable of real-time analytes detection is highly desirable. Nowadays, toxic or colorless gas detection, air pollution monitoring, harmful chemical, pressure, strain, humidity, and temperature sensors based on photonic crystal fiber (PCF) are increasing rapidly due to its compact structure, fast response and efficient light controlling capabilities. The propagating light through the PCF can be controlled by varying the structural parameters and core-cladding materials, as a result, evanescent field can be enhanced significantly which is the main component of the PCF based gas/chemical sensors. The aim of this chapter is to (1) describe the principle operation of PCF based gas/ chemical sensors, (2) discuss the important PCF properties for optical sensors, (3) extensively discuss the different types of microstructured optical fiber based gas/ chemical sensors, (4) study the effects of different core-cladding shapes, and fiber background materials on sensing performance, and (5) highlight the main challenges of PCF based gas/ chemical sensors and possible solutions.

Published
2018

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