With the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such attacks can vary significantly, from affecting the service of the device itself to putting their owners and their personal information at risk. As a response to improving their security, the focus has been on attacks, specifically on the network layer. However, the importance and impact of other vulnerabilities, such as low-level Radio Frequency (RF) attacks, have been neglected. Such attacks are challenging to detect, and they can be deployed using non-expensive equipment and can cause significant damage. This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again. This finding highlights the lack of protection against both intentional and unintentional jamming. In addition, all devices demonstrated that they were susceptible to replay attacks, which highlights the need for more hardened security measures. Finally, this paper discusses proposals for defence mechanisms for enhancing the security of IoT devices against the aforementioned attacks.

The rapid expansion of Internet of Things (IoT) devices has heightened the need for effective intrusion detection systems (IDSs) that operate under strict resource constraints. Conventional IDS implementations require substantial computational resources, making them unsuitable for low-power microcontroller-based devices. This article proposes a novel collaborative IDS (CIDS) architecture that separates centralized model training from distributed edge inference. The system employs an autoencoder-based labeling mechanism trained on regular traffic to identify anomalies. Each ESP32 device performs local inference and exchanges predictions via UDP multicast, whilst MD5 hashing ensures model consistency across the network. Collaborative verification enables devices to identify and isolate compromised nodes without central coordination. Experimental evaluation demonstrates 98.5% $F$ -score with 3 ms average inference latency and 12.7 KB memory footprint, consuming only 2.4% of available SRAM. Our approach achieves superior detection accuracy compared to existing cloud-edge systems whilst operating on severely resource-constrained hardware, making it a practical solution for large-scale IoT security deployments.

The digitalisation of the modern schooling system has led to multiple schools and organisations buying similar hardware. Electronic equipment like wireless microphones, projectors, touchscreen displays etc., have been almost standardised with a few well-known brands leading the market. This has led to the adoption of common frequency ranges between brands with many sticking between 600-670 MHz. The popularity of low-cost computing devices like the Raspberry Pi which has been used in a plethora of applications has also taken the path of being used as low-cost transmitters. There have been many implementations where the Raspberry Pi has been used as the target device but few cases where the PI is the actual threat. In this paper, we explore the use of the Raspberry Pi as a stealth radio frequency jamming device to disable wireless conference microphones. Harmonics were used to achieve frequencies outside the Pi's transmission frequency by taking advantage of its unfiltered transmission.