Acoustic side-channel attacks (SCAs) use audio produced by a system to bypass traditional security measures to extract sensitive information. Human interface devices, such as keyboards, have been the focus of such attacks, however, computer mice are input devices that are currently in a research gap. This paper explores the security risks the emitted mouse sounds pose during usage. The methodology first establishes a proof of concept attack by classifying the mouse movement into up, down, left and right directions. The results lead to a 97% accuracy in distinguishing between the four categories in a controlled environment. This sets the stage by proving a leakage model useful for mouse acoustic SCAs. The research investigated the precision of tracking mouse movements by conducting experiments with ten unique movements on a large mouse pad. The study, using a dual-microphone setup, a smartphone in stereo recording, achieved 95% accuracy in discerning ten different movements. Furthermore, to place the research in a real-world context, the same experiment was repeated by adding two more directions (diagonal movement) and five other participants. The model was trained to become generalizable to six participants and 12 mouse pad movements, resulting in an accuracy of 94%. Given the same environment, this result shows the capability to extract sensitive information using a non-user-specific model. In addition, the paper experimented with a realistic attack scenario to infer a user action of closing a window on a laptop by clicking the red 'X' at the top right of the screen. The trained model could predict with 91% whether a mouse movement and click described the close window event. The experiments and findings within this research confirm audio leakage from a computer mouse in use. Moreover, the SCA poses a security risk in real-world scenarios, as it allows us to trace user activity in a realistic scenario. This work has explored the limits of single microphone use for SCAs and opened the door toward dual-microphone setup for future experiments.

Routing Protocol for Low Power and Lossy Networks (RPL) is a routing protocol for Internet of Things (IoT) devices with limited resources. As IoT is becoming prevalent, it is important to secure the underlying protocols that compose it such as RPL. This paper sought to avoid an RPL-specific routing attack by modifying the protocol's functionalities. As a result, STIR is a novel method improving memory efficiency of routing tables in RPL's storing mode of operation to prevent Routing Table Overload attacks. STIR assigns IPv6 addresses in a specific way to form clusters of addresses, thus resulting in routing tables with sizes proportional to the node's number of sub-DODAGs. The proposed contribution increases memory efficiency in RPL's storing mode, therefore preventing routing table overload attacks. STIR necessitates few protocol modifications and additional control messages making it an ideal preventive method.