Website Fingerprinting attacks aim to track the visited websites in browsers and infer confidential information about users. Several studies showed that recent advancements in Machine Learning (ML) and Deep Learning (DL) algorithms made it possible to implement website fingerprinting attacks even though various defense techniques are present in the network. Nevertheless, trained models for website detection are not analyzed deeply to identify the leakage sources which are not always visible to both attackers and Cyber Threat Intelligence engineers. This study focuses on explaining ML and DL models in the context of microarchitecture-based website fingerprinting attacks. In the attack model, performance counters and cache occupancy side-channels are implemented on Google Chrome and Tor browsers. After ML and DL models are trained, LIME and saliency map XAI methods are applied to examine the leakage points in the side-channel data. In order to match the leakage samples in the measurements to the network traces, a novel dataset is collected by utilizing Google Chrome and Firefox browser developer tools. Next, the efficiency of explainable methods are analyzed with XAI metrics. Finally, an XAI-based obfuscation defense technique is proposed as a countermeasure against microarchitecture-based website fingerprinting attacks

The arm race between hardware security engineers and side-channel researchers has become more competitive with more sophisticated attacks and defenses in the last decade. While modern hardware features improve the system performance significantly, they may create new attack surfaces for malicious people to extract sensitive information about users without physical access to the victim device. Although many previously exploited hardware and OS features were patched by OS developers and chip vendors, any feature that is accessible from userspace applications can be exploited to perform software-based side-channel attacks. In this paper, we present DF-SCA, which is a software-based dynamic frequency side-channel attack on Linux and Android OS devices. We exploit unprivileged access to cpufreq interface that exposes real-time CPU core frequency values directly correlated with the system utilization, creating a reliable side-channel for attackers. We show that Dynamic Voltage and Frequency Scaling (DVFS) feature in modern systems can be utilized to perform website fingerprinting attacks for Google Chrome and Tor browsers on modern Intel, AMD, and ARM architectures. We further extend our analysis to a wide selection of scaling governors on Intel and AMD CPUs, verifying that all scaling governors provide enough information on the visited web page. Moreover, we extract properties of keystroke patterns on frequency readings, that leads to 95% accuracy to distinguish the keystrokes from other activities on Android phones. We leverage inter-keystroke timings of a user by training a k-th nearest neighbor model, which achieves 88% password recovery rate in the first guess on Bank of America application. Finally, we propose several countermeasures to mask the user activity to mitigate DF-SCA on Linux-based systems.

With the rising number of devices connected to the internet, the number of cyber-attacks on these devices increases in parallel. There are several strategies that an attacker can pursue, like stealing intellectual property of a victim or encrypting data to demand ransom for the decryption. In this work, we are focusing on the detection of so called cryptojacking attacks, in which an attacker that gained access to a system, then introduces programs that use the processing power of the victim device to mine cryptocurrencies. The presence of such an attack is not obvious right away and the longer an attacker manages to remain undetected, the longer they can profit having the victim foot the power bill. In this study, we combine previous approaches to demonstrate that cryptojacking attacks can be detected with an accuracy of 96% by leveraging hardware performance counters on the Windows operating system. Further, we present a method to determine which performance events result in the best detection rates, thus allowing the selection of a few performance events that can be monitored simultaneously by modern consumer CPUs. In a next step, we show that the CPU counters-based detection mechanism fails when an attacker switches from using the CPU resources to GPUs for the mining tasks. Based on these findings we then improve the previous detection approaches by extending the CPU performance counters with GPU-specific metrics resulting in 99.86% accuracy for the GPU-based cryptojacking attack class. In addition to a high detection rate the presented approach only causes a negligible performance loss while monitoring the whole system, which allows for continuous monitoring of live systems.

Cache-based website fingerprinting (WF) attacks violate user privacy where the attacker leverages the shared last-level cache in CPUs and analyzes the fingerprints through machine learning and deep learning models. WF attacks are even applicable in Incognito and anonymized browser platforms, leading to a serious threat to the public. Several defense techniques inject random noise during website rendering to degrade the attack success rate, while these techniques either create large performance overhead or cannot obfuscate the WF dataset entirely when the attacker retrains a new learning model with noisy fingerprints. In this work, we develop a dynamic generative learning-based defense technique, DefWeb, to protect user privacy against cache-based WF attacks by injecting precise noise into the WFs. For this purpose, (i) we train generative neural networks to represent high-dimensional fingerprints in a low-dimension space while creating distinct clusters for each website. (ii) Minimal noise templates are extracted in the low-dimension space to obfuscate the fingerprints efficiently. (iii) We create practical noise templates that can be added to WFs during website rendering by leveraging self-modifying code (SMC). We implement DefWeb in both simulation and real-world setups to degrade the attacker’s model accuracy. DefWeb can decrease the model accuracy to 1.1% and 28.8% in simulation and real-world setups, respectively, including Mozilla Firefox, Google Chrome, and Tor browsers. Finally, the performance overhead introduced by DefWeb is only 9.5%, which is considerably lower than previous defense techniques.