158 citations · 461 across the 37 of their papers we have counts for
8 papers · 1 filter
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection
Rahul Kale, Zhi Lu, Kar Wai Fok +1
Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily lif…
Data Privacy in Multi-Cloud: An Enhanced Data Fragmentation Framework
Randolph Loh, Vrizlynn L. L. Thing
Data splitting preserves privacy by partitioning data into various fragments to be stored remotely and shared. It supports most data operations because data can be stored in clear…
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning
Vrizlynn L. L. Thing
Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protec…
Intrusion Detection in Internet of Things using Convolutional Neural Networks
Martin Kodys, Zhi Lu, Kar Wai Fok +1
Internet of Things (IoT) has become a popular paradigm to fulfil needs of the industry such as asset tracking, resource monitoring and automation. As security mechanisms are often…
Clustering based opcode graph generation for malware variant detection
Kar Wai Fok, Vrizlynn L. L. Thing
Malwares are the key means leveraged by threat actors in the cyber space for their attacks. There is a large array of commercial solutions in the market and significant scientific…
Towards Effective Cybercrime Intervention
Jonathan W. Z. Lim, Vrizlynn L. L. Thing
Cybercrimes are on the rise, in part due to technological advancements, as well as increased avenues of exploitation. Sophisticated threat actors are leveraging on such advancement…
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection
Zhi Lu, Vrizlynn L. L. Thing
The explanation to an AI model's prediction used to support decision making in cyber security, is of critical importance. It is especially so when the model's incorrect prediction…
Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study
Zihao Wang, Kar-Wai Fok, Vrizlynn L. L. Thing
As people's demand for personal privacy and data security becomes a priority, encrypted traffic has become mainstream in the cyber world. However, traffic encryption is also shield…