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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.

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To support information security, organizations deploy Intrusion Detection Systems (IDS) that monitor information systems and networks, generating alerts for every suspicious behavior. However, the huge amount of alerts that an IDS triggers and their low-level representation make the alerts analysis a challenging task. In this paper, we propose a new approach based on hierarchical clustering that supports intrusion alert analysis in two main steps. First, it correlates historical alerts to identify the most typical strategies attackers have used. Then, it associates upcoming alerts in real time according to the strategies discovered in the first step. The experiments were performed using a real data set from the University of Maryland. The results show that the proposed approach can provide useful information for security administrators and may reduce the time between a security event and the response.

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Botnets have been part of some of the most aggressive cyberattacks reported in recent years. To make them even harder to be detected and mitigated, attackers have built C&C (Command and Control) infrastructures on top of popular Internet services such as Skype and Bitcoin. In this work, we propose an approach to detect botnets with C&C infrastructures based on the Bitcoin network. First, transactions are grouped according to the users that issued them. Next, features are extracted for each group of transactions, aiming to identify whether they behave systematically, which is a typical bot characteristic. To analyse this data, we employ the OSVM (One-class Support Vector Machine) algorithm, which requires only samples from legitimate behaviour to build a classification model. Tests were performed in a controlled environment using the ZombieCoin botnet and real data from the Bitcoin blockchain. Results showed that the proposed approach can detect most of the bots with a low false positive rate in multiple scenarios.