Search Results for author:
Found 7 papers, 3 papers with code
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially.
Privacy and Trust Redefined in Federated Machine Learning
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures.
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms.
Forensic Science International: Digital Investigation
The resulting generated timelines provided a excellent way to visualise the behaviour of the ransomware and the encryption key management practices it employed, and from a forensic investigation and possible mitigation point of view, when the encryption keys are in memory.
Cryptography and Security E.3; K.6.5
A Distributed Trust Framework for Privacy-Preserving Machine Learning
Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place.
Fast Probabilistic Consensus with Weighted Votes
The fast probabilistic consensus (FPC) is a voting consensus protocol that is robust and efficient in Byzantine infrastructure.
Distributed, Parallel, and Cluster Computing
Phishing URL Detection Through Top-level Domain Analysis: A Descriptive Approach
Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate.
