Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples
Conference Proceeding
Babaagba, K., Tan, Z., & Hart, E. (2020)
Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. https://doi.org/10.1109/CEC48606.2020.9185668
Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this,...
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites
Conference Proceeding
Babaagba, K. O., Tan, Z., & Hart, E. (2020)
Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites. In Applications of Evolutionary Computation. EvoApplications 2020. , (117-132). https://doi.org/10.1007/978-3-030-43722-0_8
In the field of metamorphic malware detection, training a detection model with malware samples that reflect potential mutants of the malware is crucial in developing a model r...
An AI approach to Collecting and Analyzing Human Interactions with Urban Environments
Journal Article
Ferrara, E., Fragale, L., Fortino, G., Song, W., Perra, C., di Mauro, M., & Liotta, A. (2019)
An AI approach to Collecting and Analyzing Human Interactions with Urban Environments. IEEE Access, 7, 141476-141486. https://doi.org/10.1109/access.2019.2943845
Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, i...
Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach
Journal Article
Di Mauro, M., & Liotta, A. (2019)
Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach. IEEE Transactions on Network and Service Management, 16(4), 1493-1506. https://doi.org/10.1109/tnsm.2019.2943776
The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provision...
Reviving legacy enterprise systems with microservice-based architecture within cloud environments
Conference Proceeding
Habibullah, S., Liu, X., Tan, Z., Zhang, Y., & Liu, Q. (2019)
Reviving legacy enterprise systems with microservice-based architecture within cloud environments. In Computer Science Conference Proceedingshttps://doi.org/10.5121/csit.2019.90713
Evolution has always been a challenge for enterprise computing systems. The microservice based architecture is a new design model which is rapidly becoming one of the most eff...
Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019)
Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371
High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning,...
Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities
Journal Article
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., …Liotta, A. (2019)
Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217
The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data ...
Interference graphs to monitor and control schedules in low-power WPAN
Journal Article
van der Lee, T., Liotta, A., & Exarchakos, G. (2019)
Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014
Highlights
• This study presents the complete and slotted interference graph model.
• The service uses the complete interference graph to evaluate the network.
• Slotted int...
Cross-modality interactive attention network for multispectral pedestrian detection
Journal Article
Zhang, L., Liu, Z., Zhang, S., Yang, X., Qiao, H., Huang, K., & Hussain, A. (2019)
Cross-modality interactive attention network for multispectral pedestrian detection. Information Fusion, 50, 20-29. https://doi.org/10.1016/j.inffus.2018.09.015
Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To e...
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Journal Article
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018)
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-04316-3
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from ...