Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device’s behaviour, the approach extracts features from the device’s CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device’s energy consumption, and CPU and memory utilisation.

One of the current challenges is to reduce collisions between vehicles and animals on roads, such accidents resulting in environmental imbalance and large expenditures in public coffers. This paper presents the components of a simple animal detection system and also a methodology for animals detection in images provided by cameras installed on the roads. This methodology allows the features extraction of regions of the image and the use of Machine Learning (ML) techniques to classify the areas into two classes: animal and non-animal. Two ML techniques were compared using synthetic images, traversing the pixels of the image using five distinctive approaches. Results show that the KNN learning model is more reliable than Random Forest to identify animals on roads accurately.

A key challenge for the use of unmanned aerial vehicles (UAVs) is the security of their information during navigation to accomplish its task. Information security is a known issue, but it seems to be overlooked from a research perspective, that tends to focus on more classical and well-formulated problems. This paper addresses an experimental evaluation of three Denial of Service (DoS) attack tools to analyze the UAV's behavior. These tools are executed in real-time on the robot while it navigates an indoor environment (inside the University building). We present experiments to demonstrate the impact of such attacks on a particular UAV model (AR.Drone 2.0) and also show a description of existing vulnerabilities. Our results indicate that DoS attacks might cause network availability issues influencing critical UAVs applications, such as the video streaming functionality.