Outputs (293)

Experimental Review of Neural-Based Approaches for Network Intrusion Management (2020)
Journal Article
Mauro, M. D., Galatro, G., & Liotta, A. (2020). Experimental Review of Neural-Based Approaches for Network Intrusion Management. IEEE Transactions on Network and Service Management, 17(4), 2480-2495. https://doi.org/10.1109/tnsm.2020.3024225

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic ID... Read More about Experimental Review of Neural-Based Approaches for Network Intrusion Management.

A Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
Presentation / Conference Contribution
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020, September). A Distributed Trust Framework for Privacy-Preserving Machine Learning. Presented at The 17th International Conference on Trust, Privacy and Security in Digit

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.

Microtargeting or Microphishing? Phishing Unveiled (2020)
Presentation / Conference Contribution
Khursheed, B., Pitropakis, N., McKeown, S., & Lambrinoudakis, C. (2020). Microtargeting or Microphishing? Phishing Unveiled. In Trust, Privacy and Security in Digital Business (89-105). https://doi.org/10.1007/978-3-030-58986-8_7

Online advertisements delivered via social media platforms function in a similar way to phishing emails. In recent years there has been a growing awareness that political advertisements are being microtargeted and tailored to specific demographics, w... Read More about Microtargeting or Microphishing? Phishing Unveiled.

An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples (2020)
Journal Article
Verdi, M., Sami, A., Akhondali, J., Khomh, F., Uddin, G., & Karami Motlagh, A. (2022). An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples. IEEE Transactions on Software Engineering, 48(5), 1497-1514. https://doi.org/10.1109/tse.2020.

Software developers share programming solutions in Q&A sites like Stack Overflow, Stack Exchange, Android forum, and so on. The reuse of crowd-sourced code snippets can facilitate rapid prototyping. However, recent research shows that the shared code... Read More about An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples.

FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures (2020)
Journal Article
Popov, S., & Buchanan, W. J. (2021). FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures. Journal of Parallel and Distributed Computing, 147, 77-86. https://doi.org/10.1016/j.jpdc.2020.09.002

This paper presents a novel leaderless protocol (FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures) with a low communicational complexity and which allows a set of nodes to come to a consensus on a value of a single bit. The paper... Read More about FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures.

A distributed sensor-fault detection and diagnosis framework using machine learning (2020)
Journal Article
Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777-796. https://doi.org/10.1016/j.ins.2020.08.068

The objective of this work is to design a sensor-fault detection and diagnosis system for the Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this objective within the limited computation, memory, and energy resour... Read More about A distributed sensor-fault detection and diagnosis framework using machine learning.

Artificial neural networks training acceleration through network science strategies (2020)
Journal Article
Cavallaro, L., Bagdasar, O., De Meo, P., Fiumara, G., & Liotta, A. (2020). Artificial neural networks training acceleration through network science strategies. Soft Computing, 24, https://doi.org/10.1007/s00500-020-05302-y

The development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs)... Read More about Artificial neural networks training acceleration through network science strategies.

Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks (2020)
Journal Article
Alsarhan, A., Al-Ghuwairi, A., Almalkaw, I., Alauthman, M., & Al-Dubai, A. (2021). Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks. Wireless Personal Communications, 117, 3129-3152 (2021). https://doi.org/10.1007/s

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road inf... Read More about Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks.

Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound (2020)
Presentation / Conference Contribution
Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Cochran, S., & Nathke, I. (2020). Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound. In 2020 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/ius46

With histological information on inflammation status as the ground truth, deep learning methods can be used as a classifier to distinguish different stages of bowel inflammation based on microultrasound (μUS) B-scan images. However, it is extremely t... Read More about Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound.

Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps (2020)
Journal Article
Shah, S. A., Ahmad, J., Masood, F., Shah, S. Y., Pervaiz, H., Taylor, W., …Abbasi, Q. H. (2021). Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps. IEEE Sensors Journal, 21(3), 366

The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the ’unstable incapacity’. This health status is essentially determined by the apparent decline of independ... Read More about Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps.