Security Challenges and Opportunities in IoT-Driven Cloud Computing: An In-Depth Review |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-4 |
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| Year of Publication : 2023 | ||
| Authors : Dilip Narayanan Ravindran | ||
| DOI : 10.14445/22312803/IJCTT-V71I4P106 | ||
How to Cite?
Dilip Narayanan Ravindran, "Security Challenges and Opportunities in IoT-Driven Cloud Computing: An In-Depth Review ," International Journal of Computer Trends and Technology, vol. 71, no. 4, pp. 37-52, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I4P106
Abstract
The most economical, commercial, cultural, social, and governmental activities occur in cyberspace in today's digitally connected world. Yet, increased reliance on electronic technology exposes businesses to cyber threats that could harm their finances, military capabilities, or political standing. Despite numerous solutions being put forth to stop or lessen these attacks, researchers are still looking at the problems, benefits, and limitations of current techniques. The extensive use of cloud computing in industrial settings has brought about several positive effects. However, it has also generated security problems that conventional solutions might not be able to address adequately. Recent studies have concentrated on using artificial intelligence's deep learning to enhance security controls for cloud-based IoT devices to overcome these problems. This in-depth study examines the most recent developments in cloud-based IoT frameworks, applications, configurations, and security architectures and the division of cloud security problems into four groups. The paper outlines potential areas for future research for integrating cybersecurity in the cloud by outlining research gaps in IoT-based cloud infrastructure, discussing major security vulnerabilities in each domain, and presenting their limitations from a fundamental, artificial intelligence (AI), and deep learning (DL) point of view.
Keywords
Cyber security, Cloud computing, Internet of things, Attack prevention.
Reference
[1] Mouna Jouini, and Latifa Ben Arfa Rabai, “A Security Framework for Secure Cloud Computing Environments:,” International Journal of Cloud Applications and Computing, vol. 6, no. 3, pp. 32–44, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Aqsa Mohiyuddin et al., “Secure Cloud Storage for Medical IoT Data using Adaptive Neuro-Fuzzy Inference System,” International Journal of Fuzzy Systems Volume, vol. 24, no. 2, pp. 1203–1215, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Rabia Abid et al., “An Optimised Homomorphic CRT-RSA Algorithm for Secure and Efficient Communication,” Personal and Ubiquitous Computing, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Arfa Arslaan Ikram et al., “Mobile Cloud Computing Framework for Securing Data,” 2021 44th International Conference on Telecommunications and Signal Processing (TSP), Brno, Czech Republic, IEEE, pp. 309–315, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Gartner Top 10 Strategic Technology Trends, 2020. [Online]. Available: https://www.gartner.com/smarterwithgartner/gartner-top-10- strategic-technology-trends-for-2020
[6] Peter Mell, and Timothy Grance, “The NIST Definition of Cloud Computing,” National Institute of Standards and Technology, Gaithersburg, MD, NIST SP 800-145, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Aakriti Sharma, Bright Keshwani, and Pankaj Dadheech, “Authentication Issues and Techniques in Cloud Computing Security: A Review,” SSRN Journal, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mostafa Ghobaei-Arani et al., “ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment,” IEEE Access, vol. 7, pp. 106912–106924, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] M. Shabbir et al., “Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing,” IEEE Access, vol. 9, pp. 8820–8834, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Thar Baker et al., “Intention-Oriented Programming Support for Runtime Adaptive Autonomic Cloud-Based Applications,” Computers & Electrical Engineering, vol. 39, no. 7, pp. 2400–2412, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Mohammed Al-khafajiy et al., “COMITMENT: A Fog Computing Trust Management Approach,” Journal of Parallel and Distributed Computing, vol. 137, pp. 1–16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tian Xia et al., “CSPM: Metamodel for Handling Security and Privacy Knowledge in Cloud Service Development,” International Journal of Systems and Software Security and Protection, vol. 12, no. 2, pp. 68–85, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] John R. Vacca, Cloud Computing Security: Foundations and Challenges, CRC Press, 2016.
[Google Scholar] [Publisher Link]
[14] 4 Trends Impacting Cloud Adoption in 2020, Gartner. [Online]. Available: https://www.gartner.com/smarterwithgartner/4-trends-impacting-cloud-adoption-in-2020
[15] Jeb Su, Why Cloud Computing Cyber Security Risks are on the Rise: Report, Forbes. [Online]. Available: https://www.forbes.com/sites/jeanbaptiste/2019/07/25/why-cloud-computing-cyber-security-risks-are-on-the-rise-report/
[16] Preeti Mishra et al., “VMProtector: Malign Process Detection for Protecting Virtual Machines in Cloud Environment,” Advances in Computing and Data Sciences, Communications in Computer and Information Science, Singapore: Springer Singapore, vol. 1045, pp. 360–369, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Md. Tanzim Khorshed, A.B.M. Shawkat Ali, and Saleh A. Wasimi, “A Survey on Gaps, Threat Remediation Challenges and Some Thoughts for Proactive Attack Detection in Cloud Computing,” Future Generation Computer Systems, vol. 28, no. 6, pp. 833–851, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[18] David Teneyuca, “Internet Cloud Security: The Illusion of Inclusion,” Information Security Technical Report, vol. 16, no. 3–4, pp. 102– 107, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Noriswadi Ismail, “Cursing the Cloud (or) Controlling the Cloud?,” Computer Law & Security Review, vol. 27, no. 3, pp. 250–257, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Nancy J. King, and V.T. Raja, “Protecting the Privacy and Security of Sensitive Customer Data in the Cloud,” Computer Law & Security Review, vol. 28, no. 3, pp. 308–319, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Patrick Ryan, and Sarah Falvey, “Trust in the Clouds,” Computer Law & Security Review, vol. 28, no. 5, pp. 513–521, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Rashmi, Dr.G.Sahoom, and Dr.S.Mehfuz, “Securing Software as a Service Model of Cloud Computing: Issues and Solutions,” International Journal on Cloud Computing: Services and Architecture, vol. 3, no. 4, pp. 1–11, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Fei Chen et al., “IoT Cloud Security Review: A Case Study Approach Using Emerging Consumer-oriented Applications,” ACM Computing Surveys, vol. 54, no. 4, pp. 1–36, 2022.
[CrossRef] [Publisher Link]
[24] Patruni Muralidhara Rao, and Pedada Saraswathi, “Evolving Cloud Security Technologies for Social Networks,” Security in IoT Social Networks, Elsevier, pp. 179–203, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Reza Montasari et al., “Cloud Computing Security: Hardware-Based Attacks and Countermeasures,” Digital Forensic Investigation of Internet of Things (IoT) Devices, Advanced Sciences and Technologies for Security Applications, Cham: Springer International Publishing, pp. 155–167, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] M. A. Khan, “A Survey of Security Issues for Cloud Computing,” Journal of Network and Computer Applications, vol. 71, pp. 11–29, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Deepak Puthal et al., “Cloud Computing Features, Issues, and Challenges: A Big Picture,” 2015 International Conference on Computational Intelligence and Networks, IEEE, pp. 116–123, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Faisal Shahzad, Waheed Iqbal, and Fawaz S. Bokhari, “On the use of Cryptdb for Securing Electronic Health Data in the Cloud: A Performance Study,” 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), Boston, MA, USA: IEEE, pp. 120–125, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[29] M. Anuradha et al., “IoT Enabled Cancer Prediction System to Enhance the Authentication and Security Using Cloud Computing,” Microprocessors and Microsystems, vol. 80, p. 103301, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Marco Antonio Sotelo Monge, Jorge Maestre Vidal, and Gregorio Martínez Pérez, “Detection of Economic Denial of Sustainability (Edos) Threats in Self-Organizing Networks,” Computer Communications, vol. 145, pp. 284–308, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Madarapu Naresh Kumar et al., “Mitigating Economic Denial of Sustainability (EDoS) in Cloud Computing Using in Cloud Scrubber Service,” 2012 Fourth International Conference on Computational Intelligence and Communication Networks, IEEE, pp. 535–539, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Yulei Wu, “Cloud-Edge Orchestration for the Internet of Things: Architecture and AI-Powered Data Processing,” IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12792–12805, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Jun-Bo Wang et al., “A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing,” IEEE Network, vol. 32, no. 2, pp. 144–151, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Afia Naeem et al., “DARE-SEP: A Hybrid Approach of Distance Aware Residual Energy-Efficient SEP for WSN,” IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 611–621, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Abdul Rehman Javed et al., “Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication,” Computers, Materials & Continua, vol. 67, no. 2, pp. 1933–1950, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Swarna Priya R.M et al., “Load Balancing of Energy Cloud Using Wind Driven and Firefly Algorithms in Internet of Everything,” Journal of Parallel and Distributed Computing, vol. 142, pp. 16–26, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Rana Zeeshan Ahamad et al., “Interference Mitigation in D2D Communication Underlying Cellular Networks: Towards Green Energy,” Computers, Materials & Continua, vol. 68, no. 1, pp. 45–58, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[38] B. Kezia Rani, B. Padmaja Rani Dr., and A. Vinaya Babu Dr, “Cloud Computing and Inter-Clouds – Types, Topologies and Research Issues,” Procedia Computer Science, vol. 50, pp. 24–29, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Tinankoria Diaby, and Babak Bashari Rad, “Cloud Computing: A Review of the Concepts and Deployment Models,” International Journal of Information Technology and Computer Science, vol. 9, no. 6, pp. 50–58, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Mohammad Babar et al., “Cloudlet Computing: Recent Advances, Taxonomy, and Challenges,” IEEE Access, vol. 9, pp. 29609–29622, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[41] M. A. Khan and K. Salah, “IoT Security: Review, Blockchain Solutions, and Open Challenges,” Future Generation Computer Systems, vol. 82, pp. 395–411, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Lu Shen et al., “CloudS: A Multi-cloud Storage System with Multi-level Security,” Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science, Cham: Springer International Publishing, vol. 9530, pp. 703–716, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[43] S. Karthikeyan, T. Poongodi, "Secure and Optimized Communication in the Internet of Things using DNA Cryptography with X.509 Digital Attributes," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 1-8, 2023.
[CrossRef] [Publisher Link]
[44] Jayavardhana Gubbi et al., “Internet of Things (Iot): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Alessio Botta et al., “Integration of Cloud Computing and Internet of Things: A Survey,” Future Generation Computer Systems, vol. 56, pp. 684–700, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Iman Azimi et al., “Internet of Things for Remote Elderly Monitoring: A Study From User-Centered Perspective,” Journal of Ambient Intelligence and Humanized Computing, vol. 8, no. 2, pp. 273–289, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[47] S. M. Riazul Islam et al., “The Internet of Things for Health Care: A Comprehensive Survey,” IEEE Access, vol. 3, pp. 678–708, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[48] A. R. Al-Ali et al., “A Smart Home Energy Management System Using IoT and Big Data Analytics Approach,” IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 426–434, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Faraz Doja et al., “A Comprehensive Framework for the IoT-Based Smart Home Automation Using Blynk,” Information and Communication Technology for Competitive Strategies, Lecture Notes in Networks and Systems, Singapore: Springer Nature Singapore, vol. 401, pp. 49-58, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Mohammad Asadul Hoque, and Chad Davidson, “Design and Implementation of an IoT-Based Smart Home Security System,” International Journal of Networked and Distributed Computing, vol. 7, no. 2, pp. 85-92, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Mostafa Yalpanian et al., “BIOT: A Blockchain-Based IoT Platform for Distributed Energy Resource Management,” Silicon Valley Cybersecurity Conference, Communications in Computer and Information Science, Cham: Springer International Publishing, vol. 1383, pp. 134–147, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Fahad Khan et al., “IoT Based Power Monitoring System for Smart Grid Applications,” 2020 International Conference on Engineering and Emerging Technologies, IEEE, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Jacques Jansen, and Alta van der Merwe, “A Framework for Industrial Internet of Things,” Responsible Design, Implementation and Use of Information and Communication Technology, Lecture Notes in Computer Science, Cham: Springer International Publishing, pp. 138– 150, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[54] S. Venkata Lakshmi et al., “Role and Applications of IoT in Materials and Manufacturing Industries – Review,” Materials Today: Proceedings, vol. 45, pp. 2925–2928, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Abdul Rehman Javed et al., “Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4291–4300, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Waqas Ahmed et al., “Security in Next Generation Mobile Payment Systems: A Comprehensive Survey,” IEEE Access, vol. 9, pp. 115932– 115950, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Ala Al-Fuqaha et al., “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015. [CrossRef] [Google Scholar] [Publisher Link]
[58] Asad Abbas et al., “Blockchain-Assisted Secured Data Management Framework for Health Information Analysis Based on Internet of Medical Things,” Personal and Ubiquitous Computing, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Sherif El-Gendy, and Marianne. A. Azer, “Security Framework for Internet of Things (IoT),” 2020 15th International Conference on Computer Engineering and Systems, IEEE, pp. 1–6, 2020. [CrossRef] [Google Scholar] [Publisher Link]
[60] Naqash Azeem Khan, Azlan Awang, and Samsul Ariffin Abdul Karim, “Security in Internet of Things: A Review,” IEEE Access, vol. 10, pp. 104649–104670, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Er. Garima Agrawal, and Dr. Samta Jain Goyal, “Survey on Data Leakage Prevention through Machine Learning Algorithms,” 2022 International Mobile and Embedded Technology Conference, IEEE, pp. 121–123, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Mohammad Equebal Hussain, and Rashid Hussain, “Cloud Security as a Service Using Data Loss Prevention: Challenges and Solution,” Internet of Things and Connected Technologies, Lecture Notes in Networks and Systems, Cham: Springer International Publishing, vol. 340, pp. 98–106, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Yao Zhao, Zhenjiang Zhang, and Jian Li, “A Secure Edge-Cloud Computing Framework for IoT Applications,” Smart Grid and Internet of Things, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Cham: Springer International Publishing, vol. 354, pp. 70-78, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Abbas Jamalipour, and Sarumathi Murali, “A Taxonomy of Machine-Learning-Based Intrusion Detection Systems for the Internet of Things: A Survey,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9444–9466, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Baraka William Nyamtiga et al., “Blockchain-Based Secure Storage Management with Edge Computing for IoT,” Electronics, vol. 8, no. 8, p. 828, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Yanqi Zhao et al., “Blockchain-Based Auditable Privacy-Preserving Data Classification for Internet of Things,” IEEE Internet Things Journal, vol. 9, no. 4, pp. 2468–2484, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Hyunjoo Kim et al., “Design of Network Threat Detection and Classification Based on Machine Learning on Cloud Computing,” Cluster Computing, vol. 22, no. S1, pp. 2341–2350, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Fatima Salahdine, Zakaria El Mrabet, and Naima Kaabouch, “Phishing Attacks Detection A Machine Learning-Based Approach,” 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE, pp. 250–255, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[69] P. Velmurugan et al., “An Advanced and Effective Encryption Methodology Used for Modern Iot Security,” Materials Today: Proceedings, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[70] Sara Afzal et al., “URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models,” Journal of Network and Systems Management, vol. 29, no. 3, p. 21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[71] Suman Sansanwal, and Nitin Jain, “Security Attacks in Cloud Computing: A Systematic Review,” 2021 Third International Conference on Inventive Research in Computing Applications, IEEE, pp. 501–508, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[72] Ahmed Sallam, Ahmed Refaey, and Abdallah Shami, “On the Security of SDN: A Completed Secure and Scalable Framework Using the Software-Defined Perimeter,” IEEE Access, vol. 7, pp. 146577–146587, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[73] Azidine Guezzaz et al., “A Distributed Intrusion Detection Approach Based on Machine Leaning Techniques for a Cloud Security,” Intelligent Systems in Big Data, Semantic Web and Machine Learning, Advances in Intelligent Systems and Computing, Cham: Springer International Publishing, vol. 1344, pp. 85–94, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[74] Ali Muhammad, Muhammad Asad, and Abdul Rehman Javed, “Robust Early Stage Botnet Detection using Machine Learning,” 2020 International Conference on Cyber Warfare and Security (ICCWS), IEEE, pp. 1–6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[75] Abdul Rehman Javed et al., “Ensemble Adaboost Classifier for Accurate and Fast Detection of Botnet Attacks in Connected Vehicles,” Emerging Telecommunications Technologies, vol. 33, no. 10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[76] Neha Agrawal, and Shashikala Tapaswi, “Defense Mechanisms Against DDoS Attacks in a Cloud Computing Environment: State-of-theArt and Research Challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3769–3795, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[77] Satish Pokhrel, Robert Abbas, and Bhulok Aryal, “IoT Security: Botnet detection in IoT using Machine Learning,” 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[78] Abdul Rehman Javed et al., “Alphalogger: Detecting Motion-Based Side-Channel Attack Using Smartphone Keystrokes,” Journal of Ambient Intelligence and Humanized Computing, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[79] Abdul Rehman Javed et al., “Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 20, no. 5, pp. 1–17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[80] Md. Ashfakul Islam, and Susan V. Vrbsky, “Transaction Management with Tree-Based Consistency in Cloud Databases,” International Journal of Cloud Computing, vol. 6, no. 1, pp. 58-78, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[81] Jun Tang et al., “Ensuring Security and Privacy Preservation for Cloud Data Services,” ACM Computing Surveys, vol. 49, no. 1, pp. 1– 39, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[82] Seungwon Shin et al., “Rosemary: A Robust, Secure, and High-performance Network Operating System,” Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, ACM, pp. 78–89, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[83] Sudakshina Mandal, and Danish Ali Khan, “A Study of Security Threats in Cloud: Passive Impact of COVID-19 Pandemic,” 2020 International Conference on Smart Electronics and Communication, IEEE, pp. 837–842, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[84] Abdul Rehman Javed, and Zunera Jalil, “Byte-Level Object Identification for Forensic Investigation of Digital Images,” in 2020 International Conference on Cyber Warfare and Security (ICCWS), IEEE, pp. 1–4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[85] Maryam Hina et al., “SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning,” IEEE Access, vol. 9, pp. 98398–98411, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[86] Abdul Rehman Javed et al., “CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network Using CNN and Attention-Based GRU,” IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1456–1466, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[87] Mohit Mittal et al., “Analysis of Security and Energy Efficiency for Shortest Route Discovery in Low-Energy Adaptive Clustering Hierarchy Protocol Using Levenberg-Marquardt Neural Network and Gated Recurrent Unit for Intrusion Detection System,” Emerging Telecommunications Technologies, vol. 32, no. 6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[88] Abdul Rauf, Riaz Ahmed Shaikh, and Asadullah Shah, “Security and Privacy for IoT and Fog Computing Paradigm,” 2018 15th Learning and Technology Conference (L&T), IEEE, pp. 96–101, 2018.
[CrossRef] [Google Scholar] [Publisher Link]