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Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors [article]

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Timo Schenk, Adrian Lars Benjamin Iten, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller
2022
aggregation functions acting as anti-adversarial mechanisms to increase the models robustness.  ...  However, FL is vulnerable to malicious participants, and the impact of adversarial attacks on federated models detecting spectrum sensing data falsification (SSDF) attacks on spectrum sensors has not been  ...  As can be seen in TABLE I , none of the related work studies the detection performance and robustness of federated models detecting SSDF attacks.  ... 
doi:10.48550/arxiv.2202.00137 fatcat:gx5kaj375reftjsxhfuts7qgd4

Paper Titles

2019 MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)  
Layer Security in 5G Heterogeneous Networks On the Use of Cyber Threat Intelligence (CTI) in Support of Developing the Commander's Understanding of the Adversary Optimal Power Control with Channel Uncertainty  ...  Generative Adversarial NetworksT 5 A C D E F G H I J L M N O P Q R S T U V WThe Business and Technology of Quantum Computing, Quantum Algorithms for the Warfighter The Case for Robust Adaptation: Autonomic  ... 
doi:10.1109/milcom47813.2019.9020754 fatcat:xqj3wymtanex3kyio6fc6pzqnm

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security [article]

Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Al-Ali, Xiaojiang Du, Mohsen Guizani
2018 arXiv   pre-print
On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges.  ...  It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020.  ...  In the related security literature, a previous study [244] . showed the effective performance of DL in accurately detecting Android malware, and the authors of the study constructed a DL model to learn  ... 
arXiv:1807.11023v1 fatcat:a3onbmcf65acpkrtlywcv42tm4

Consumer, Commercial and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Christos Xenofontos, Ioannis Zografopoulos, Charalambos Konstantinou, Alireza Jolfaei, Muhammad Khurram Khan, Kim-Kwang Raymond Choo
2021 IEEE Internet of Things Journal  
The steep growth and vast adoption of IoT devices reinforce the importance of sound and robust cybersecurity practices during the device development life-cycles.  ...  A 2020 study conducted by Nokia's threat intelligence labs, for example, indicated that IoT devices account for almost 30% of the attacks encountered in mobile and wireless networks (e.g., WiFi, Bluetooth  ...  This could allow researchers to develop learning-based techniques by fusing domain-aware knowledge of the underlying IoT system nature into the learning model.  ... 
doi:10.1109/jiot.2021.3079916 fatcat:rfmkc6wrk5co3i7bl44ar432bi

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Eva Rodriguez, Beatriz Otero, Norma Gutierrez, Ramon Canal
2021 IEEE Communications Surveys and Tutorials  
Consequently, cybersecurity systems have embraced Deep Learning (DL) models as they provide efficient detection of novel attacks and better accuracy.  ...  In the last years, the number of cyberattacks has grown dramatically, as well as their complexity.  ...  ACKNOWLEDGMENTS This work is supported by the Generalitat de Catalunya under grant 2017SGR962 and the DRAC project (001-P-001723).  ... 
doi:10.1109/comst.2021.3086296 fatcat:2svylj3y7vfijnynpnoksdl6oa

A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports

Nickolaos Koroniotis, Nickolaos Koroniotis, Nour Moustafa, Nour Moustafa, Francesco Schiliro, Francesco Schiliro, Praveen Gauravaram, Praveen Gauravaram, Helge Janicke
2020 IEEE Access  
An anomaly-based IDS is a ML or DL model that has been trained and evaluated on data instances that are considered normal, allowing the model to "learn" to detect the normal behaviour of the users in a  ...  He enrolled in UNSW Canberra to initiate his PhD studies in February 2017 in the field of Cyber security with a particular interest in Network Forensics and the IoT.  ... 
doi:10.1109/access.2020.3036728 fatcat:7kbdcvfwmvextpmrgo4mc2qgyq

Cyberphysical Security of Grid Battery Energy Storage Systems

Rodrigo D. Trevizan, James Obert, Valerio De Angelis, Tu A. Nguyen, Vittal S. Rao, Babu R. Chalamala
2022 IEEE Access  
Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.  ...  ACKNOWLEDGMENT The authors would like to thank Dr. Imre Gyuk, Director of Energy Storage Research, Office of Electricity Delivery and Energy Reliability for his funding and guidance on this research.  ...  Cost-effective gas sensors are typically sensitive to a narrow spectrum of molecules, therefore the choice of gas sensors must be informed by understanding the failure mechanisms of each battery technology  ... 
doi:10.1109/access.2022.3178987 fatcat:z3erpvu6pbh65hk6w25bqb3x7m

Technical Program

2022 2022 IEEE International Conference on Consumer Electronics (ICCE)  
Simulation results show that the proposed method can adapt to both ambient light levels and the scene content to keep the creative intent in different ambient conditions.  ...  The paper proposes a high dynamic range (HDR) tone mastering system which dynamically corrects the picture quality based on creative intent metadata to preserve content providers' creative intent in different  ...  10:07 3D LiDAR Automatic Driving Environment Detection System Based on  ... 
doi:10.1109/icce53296.2022.9730380 fatcat:csqu3xqbczgdhpp3hbmvjpt26a

XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions [article]

Gautam Srivastava, Rutvij H Jhaveri, Sweta Bhattacharya, Sharnil Pandya, Rajeswari, Praveen Kumar Reddy Maddikunta, Gokul Yenduri, Jon G. Hall, Mamoun Alazab, Thippa Reddy Gadekallu
2022 arXiv   pre-print
The present study provides and extensive review of the use of XAI in cybersecurity. Cybersecurity enables protection of systems, networks and programs from different types of attacks.  ...  In the past few years, artificial intelligence (AI) techniques have been implemented in almost all verticals of human life. However, the results generated from the AI models often lag explainability.  ...  In [22] , a deep learning based intrusion detection framework was proposed. The explainable AI techniques in the study, helped to achieve transparency at each level of the ML model.  ... 
arXiv:2206.03585v1 fatcat:qe2d33ujdzbapczhefs2whbnwe

Mentor's Musings on Security Standardization Challenges and Imperatives for Artificial Intelligence of Things

N. Kishor Narang
2022 IEEE Internet of Things Magazine  
Using approaches like federated learning, AIoT devices can learn from user preferences and improve their decisions.  ...  So-called adversarial attacks try to exploit this by manipulating input data in a way that confuses the AI model.  ... 
doi:10.1109/miot.2022.9773094 fatcat:ds7mdlqhijfbrcff3q6vmj6oee

Cybersecurity: Past, Present and Future [article]

Shahid Alam
2022 arXiv   pre-print
Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.  ...  We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity.  ...  ., 2020] presents a study and systematic review of the application of adversarial machine learning to intrusion and malware detection.  ... 
arXiv:2207.01227v1 fatcat:vfx54hq3ejc7dlfestj6dkstpa

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
The recent success of deep learning underpins new and powerful tools that tackle problems in this space.  ...  In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  The applicability of deep learning for trajectory prediction is studied in [485] .  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
The recent success of deep learning underpins new and powerful tools that tackle problems in this space.  ...  In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  The applicability of deep learning for trajectory prediction is studied in [482] .  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges [article]

Kashif Ahmad, Majdi Maabreh, Mohamed Ghaly, Khalil Khan, Junaid Qadir, Ala Al-Fuqaha
2021 arXiv   pre-print
In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications  ...  As the globally increasing population drives rapid urbanisation in various parts of the world, there is a great need to deliberate on the future of the cities worth living.  ...  learning rate of the model.  ... 
arXiv:2012.09110v4 fatcat:yxh5tvpehbgldcblweoovbvdsq

A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions [article]

Thulitha Senevirathna, Zujany Salazar, Vinh Hoa La, Samuel Marchal, Bartlomiej Siniarski, Madhusanka Liyanage, Shen Wang
2022 arXiv   pre-print
However, most of the existing surveys in B5G security focus on the performance of AI/ML models and their accuracy, but they often overlook the accountability and trustworthiness of the models' decisions  ...  Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks  ...  ACKNOWLEDGEMENT This work is partly supported by European Union in SPA-TIAL Project (Grant No: 101021808), Academy of Finland in 6Genesis (grant no. 318927) and and Science Foundation Ireland under CONNECT  ... 
arXiv:2204.12822v1 fatcat:uv74yh772jag3nkuil2zlx5uki
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