Website Fingerprinting (WF) attacks significantly endanger user privacy in Anonymous Communication Networks, such as Tor, by allowing adversaries to infer the user’s browsing activity. Contemporary research has demonstrated that WF attacks using Deep Learning techniques transcend conventional rule-based defenses. Deep Learning models, in particular, have achieved remarkable accuracy in identifying websites visited through Tor, exhibiting the elevated threat posed by advanced WF attacks. This paper presents a novel WF defense mechanism, ADEFTOR, incorporating incremental distance reduction and universal distortion for individual sites. This approach involves selecting random target traces and progressively decreasing the distance between the modified examples and these targets. It also generates a universal distortion applicable across different user sessions and traffic types, allowing perturbations to blend into real-time network traffic seamlessly. ADEFTOR is evaluated against DL-based attacks using a public Tor traffic dataset. Experimental results demonstrate that ADEFTOR reduces the accuracy of the top-1 attack from 98% to between 29% and 39% and the accuracy of the top-2 to 47%. ADEFTOR reduces the bandwidth overhead of Full-Duplex and Half-Duplex to 45% and 60%, respectively. ADEFTOR presents a promising solution for improving privacy in the Tor networks by addressing WF attacks, effectively degrading the accuracy of sophisticated classifiers.

Internet of Things (IoT) connects billions of devices and tiny sensors enabled with Low-Power and Lossy Networks (LLNs) to provide real time data transfer. These LLNs work as s backbone of complete IoT ecosystem which has limited power, memory and processing capability. The routing protocol for such LLNs enabled devices is standardized by IETF in RFC 6550 which is known has Routing Protocol for Low-Power and Lossy Networks (RPL). Due to the constraints of the RPL protocol, it is vulnerable to various new security attacks which needs effective defense solution. Machine Intelligence (including Machine Learning and Deep Learning) plays a great role to deal with detection of recent attacks based on the pattern and behaviour analysis of device presented in the network. This paper introduces a novel hyperparameter-based approach to detect and classify the RPL-based routing attacks. The proposed approach uses a Hyperband tuner search, that finds the optimal hyperparameter such as learning rate and the number of neurons for deep learning model to improve the detection performance. Experiments have been conducted on the ROUT-4-2023 dataset that contains the attack sample of Flooding, Blackhole, DODAG Version number and Rank attack. The results are evaluated against various performance metrics on dataset balance and imbalance scenario which provide the promising findings for attack classification.