Where's your data going? It was a huge pleasure to welcome Anna Maria Mandalari at TII last week for a new session of our AI Seminar regarding prioritising security and privacy in IoT networks. These are my key takeaways: - IoT devices can easily use their many sensors to spy on us in our privacy and share information with third parties without us knowing. - Smart TVs come with ACR (Automated Content Recognition) that shares screenshots of what they are displaying, even if you are connecting say your corporate laptop via HDMI—oops! You can disable ACR in your smart TV configuration. - Most IoT safeguards that claim to detect attacks on IoT devices do not actually work as expected. - IoT trimmers can help filter only the required traffic to and from IoT devices to ensure only the essential functionalities are active. - We might be able to train ML models to detect attacks on an IoT device based on its real-time activity. - Regulation on IoT devices is very hard to enforce, as manufacturers can setup configurations to achieve the appropriate certifications yet update them from the distance once installed in your network. - Very few vendors have responded positively when security concerns about their devices have been raised. Cyber security is definitely worth looking into, but even more so when considering IoT networks. You are probably more exposed than you think. #IoTSecurity #IoT #CyberSecurity #TII #RaisingAwareness
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It is a great pleasure to share that the following interesting work has been published in Alexandria Engineering Journal (Elsevier) with 2023 SCI Impact Factor: 6.2 [Category: Q1]: Uma Sankararao Varri, Debjani Mallick, Ashok Kumar Das, M. Shamim Hossain, Youngho Park, and Joel J. P. C. Rodrigues. "TL-ABKS: Traceable and lightweight attribute-based keyword search in edge-cloud assisted IoT environment," in Alexandria Engineering Journal (Elsevier), Vol. 107, pp. 757-769, November 2024, DOI: 10.1016/j.aej.2024.09.030. (2023 SCI Impact Factor: 6.2) The summary of this work is explained below: Edge-cloud coordination offers the chance to mitigate the enormous storage and processing load brought on by a massive increase in traffic at the network's edge. Though this paradigm has benefits on a large scale, outsourcing the sensitive data from the smart devices deployed in an Internet of Things (IoT) application may lead to privacy leakage. With an attribute-based keyword search (ABKS), the search over ciphertext can be achieved; this reduces the risk of sensitive data explosion. However, ABKS has several issues, like huge computational overhead to perform multi-keyword searches and tracing malicious users. To address these issues and enhance the performance of ABKS, we propose a novel traceable and lightweight attribute-based keyword search technique in an Edge-cloud-assisted IoT, named TL-ABKS, using edge-cloud coordination. With TL-ABKS, it is possible to do effective multi-keyword searches and implement fine-grained access control. Further, TL-ABKS outsources the encryption and decryption computation to edge nodes to enable its usage to resource-limited IoT smart devices. In addition, TL-ABKS achieves tracing user identity who misuse their secret keys. TL-ABKS is secure against modified secret keys, chosen plaintext, and chosen keyword attacks. By comparing the proposed TL-ABKS with the current state-of-the-art schemes, and conducting a theoretical and experimental evaluation of its performance and credibility, TL-ABKS is efficient. This work has been collaborated with Dr. Uma Sankararao Varri (Department of Computer Science and Engineering, SRM University-AP, Amaravathi, Andhra Pradesh, India), Ms. Debjani Mallick (PhD student at Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad 500 032, India), Prof. M. Shamim Hossain (Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 13273, Saudi Arabia), Prof. Youngho Park (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, South Korea), and Prof. Joel J. P. C. Rodrigues (Amazonas State University, Manaus - AM, Brazil). The full work can be downloaded from the following link:
TL-ABKS: Traceable and lightweight attribute-based keyword search in edge–cloud assisted IoT environment
sciencedirect.com
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The Internet of Things (IoT) consists of interconnected devices that enhance real-time data collection and analysis across various sectors, which creates new privacy and security challenges that demand a new approach. We've seen and heard about the concept of Federated Learning (FL) many times before. FL addresses issues of accuracy, efficiency, and privacy in traditional machine learning but faces challenges in IoT, including resource limitations, data heterogeneity, communication overheads, and privacy concerns. A new paper emphasizes the need to protect FL data privacy on IoT devices, with robust measures against threats like inference attacks and data leakage. Key privacy threats in FL for IoT include inference attacks, poisoning attacks, eavesdropping, Sybil attacks, backdoor attacks, gradient leakage, and reconstruction. For instance, Zhang et al. highlight the significant privacy leakages from membership inference attacks using Generative Adversarial Networks (GANs). Similarly, some propose systems-aware optimization methods to counter data poisoning attacks in IoT systems. Defensive measures include differential privacy techniques, robust federated learning methods, and lightweight privacy protection protocols for edge computing. "Using FL with differential privacy techniques can safeguard against eavesdropping in IIoT environments," a study suggests. This new paper explains the need for comprehensive understanding of privacy concerns in FL within resource-constrained IoT environments. It's more of a review article, so a quick skim for anyone working in this space. #federatedlearning #privacy #iot
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Here are some of the latest positive updates and innovations in the field of Information Technology: 1. **AI Advancements**: Major strides in artificial intelligence continue to revolutionize various sectors. For instance, AI is now being used more effectively in healthcare for early diagnosis of diseases and personalized treatment plans. 2. **Quantum Computing**: Significant progress in quantum computing is paving the way for solving complex problems faster than ever before. Companies like IBM and Google are making headway in developing practical quantum computers. 3. **Cybersecurity Improvements**: Innovations in cybersecurity are enhancing data protection and privacy. Advances include the development of more sophisticated encryption techniques and AI-driven threat detection systems. 4. **5G Rollout**: The deployment of 5G networks is expanding, providing faster and more reliable internet connectivity. This technology is expected to enable new applications in IoT, smart cities, and autonomous vehicles. 5. **Green IT**: There is a growing focus on sustainable IT practices. Companies are increasingly adopting energy-efficient technologies and renewable energy sources to power data centers, reducing their carbon footprint. These developments highlight the ongoing evolution and positive impact of IT on various aspects of our lives.
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Our upcoming Live Event is in about 2hrs on the paper entitled "AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices": https://lnkd.in/erZn4cD8 Date: Saturday 14th September 2024, Time: 19h (GMT+1) Host: KmerAI Guest: Volviane Saphir MFOGO, PhD student at the University of Dschang. Event Details: About the Guest: Volviane Saphir MFOGO is currently pursuing a PhD degree with the Faculty of Science, Department of Mathematics and Computer Science, University of Dschang. She is working on Machine learning and Cybersecurity under the Game theory and Machine learning for Cyber Deception, Resilience and Agility (GMC-DRA) project, sponsored by the US Army research lab. She is an African Master in Machine Intelligence (AMMI), alumnus where she obtained an MSc. In Machine Learning, which is sponsored by Facebook and Google, Before joining AMMI, she obtained an MSc. In Mathematics from the African Institute for Mathematical Sciences (AIMS) (AIMS Cameroon). Topic: AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices Saphir Volviane Mfogo will be presenting her work on honeypots, a popular deception technique to simulate real device interactions to attract attackers. Due to the vast number and diversity of IoT devices, manually creating honeypots is impractical. Her paper proposes a machine learning-based honeypot that automates interactions with attackers, enhancing security. The system's evaluation shows it can extend interactions with attackers and capture more attack data on IoT networks. Don't miss this insightful discussion! Checkout the paper: https://lnkd.in/exFQDYjf
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🌟 Excited to share our latest research presented at the Applications and Techniques in Information Security (ATIS 2024) conference! 🎉 📄 Our paper, "HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control Systems Using MLPNN and Attention Mechanisms," introduces a novel method for enhancing cyber-attack detection in ICS environments. By integrating hypergraph-based attention mechanisms into a Multilayer Perceptron Neural Network (MLPNN), our approach significantly improves the detection of complex feature interactions in ICS datasets. 🔑 Key highlights: - 📊 Evaluated on iTrust’s Secure Water Treatment (SWaT) and Mississippi’s Gas Pipeline dataset. - 🚀 Faster training on labeled data. - 🏆 Superior performance with high recall and F1-scores. 🙏 A special thanks to Dr. Priyanga S for the opportunity to work on this exciting research! 📖 Read more about our work here: https://lnkd.in/gbrs25Bh #CyberSecurity #ICS #MachineLearning #AI #Research #IoT #ATIS
HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control Systems Using MLPNN and Attention Mechanisms
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I am thrilled to share that my book chapter titled "Security Modules for Biomedical Signal Processing using Internet of Things" is now available online on ScienceDirect! This chapter is part of the book "Machine Learning Models and Architectures for Biomedical Signal Processing" and explores the integration of IoT with secure biomedical signal processing—an essential step forward in protecting patient data and enhancing healthcare technology. For anyone interested in the fields of IoT, security, and biomedical science, this chapter dives into how robust security frameworks can be applied in real-world healthcare scenarios to ensure safe and reliable signal processing. #IoT #Biomedical #SignalProcessing #MachineLearning #HealthcareInnovation #CyberSecurity
Security modules for biomedical signal processing using Internet of Things
sciencedirect.com
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Hey connections I am elated to share that my research paper titled 'VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks' has finally been published by Springer as part of the proceedings for 'International Conference on Recent Trends in Image Processing and Pattern Recognition 2023'. This research employs hybrid deep learning algorithms to detect intrusions in MQTT protocol enabled IoT networks. The proposed model showcases high accuracies and classifies a given network flow into one of five attack classes. This research highlights the application of deep learning and machine learning techonologies to tackle real life problems faced in the domain of cyber security. I would like extend my warmest gratitude towards Dr. Arjun Choudhary and Jaspreet Kaur for their valuable inputs and guidance. A special thanks to Vaidehi Gupta for aiding in the ideation of this research. Lastly, kudos to my fellow researcher Prashant Mathur for this publication. The published research can be found here - https://lnkd.in/g8UVZZxd
VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks
link.springer.com
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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based ...: During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these ... #iot #data #internetofthings
Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model
link.springer.com
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Exciting Opportunity for Researchers and Academicians! I’m thrilled to share that the International Conference on Emerging Technologies for Intelligent Systems (ETIS2025) is coming up from 7-9 February 2025, organized by APJ Abdul Kalam Technological University, in collaboration with IEEE Kerala Section and IEEE Industry Applications Society. This multidisciplinary forum is a fantastic platform to exchange insights on the latest innovations, trends, and challenges in Artificial Intelligence, IoT, Cybersecurity, Healthcare Technologies, and more. 📍 Venue: Mar Baselios College of Engineering and Technology, Thiruvananthapuram 📅 Important Dates: Draft Paper Submission Deadline: September 30, 2024 Notification of Acceptance: November 1, 2024 Final Camera-ready Paper: November 15, 2024 If you're working on cutting-edge research in any of the following topics, we encourage you to submit your paper: Artificial Intelligence and Machine Learning IoT Innovations Robotics and Automation Data Science & Analytics Cloud and Edge Computing, and more! All accepted papers will be submitted to IEEE Xplore for publication, with select extended papers considered for special issues in journals like IJSWIS and JADC. For more details, visit: etis-2025.org #AI #MachineLearning #IoT #Cybersecurity #ETIS2025 #ResearchOpportunities #Conference2025 #EmergingTechnologies
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