The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

Considerable progress has been made in improving the estimation accuracy of cognitive workload using various sensor technologies. However, the overall performance of different algorithms and methods remain suboptimal in real-world applications. Some studies in the literature demonstrate that a single modality is sufficient to estimate cognitive workload. These studies are limited to controlled settings, a scenario that is significantly different from the real world where data gets corrupted, interrupted, and delayed. In such situations, the use of multiple modalities is needed. Multimodal fusion approaches have been successful in other domains, such as wireless-sensor networks, in addressing single-sensor weaknesses and improving information quality/accuracy. These approaches are inherently more reliable when a data source is lost. In the cognitive workload literature, sensors, such as electroencephalography (EEG), electrocardiography (ECG), and eye tracking, have shown success in estimating the aspects of cognitive workload. Multimodal approaches that combine data from several sensors together can be more robust for real-time measurement of cognitive workload. In this article, we review the published studies related to multimodal data fusion to estimate the cognitive workload and synthesize their main findings. We identify the opportunities for designing better multimodal fusion systems for cognitive workload modeling.

The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature from the perspective of multi-tasking: we pay particular attention to concepts that contribute to the progress of swarm robotics for multi-tasking applications. To do this, we first examine the different studies in multi-tasking swarm robotics, covering platforms, multi-tasking scenarios, sub-task allocation methodologies, and performance metrics. We then highlight several swarm robotics related disciplines that have significant effect on the development of swarm robotics for multi-tasking problems. We propose two taxonomies: the first categorises works based on the characteristics of the scenarios being handled, whereas the second taxonomy categorises works based on the swarming strategies utilised to achieve multi-tasking capabilities. We finish with a discussion of swarm robots’ existing limitations for real-world multi-tasking applications, as well as recommendations for future research directions.

Brain signals are potential biometric markers in user authentication, complementing existing biometric authentication techniques (such as those based on fingerprint, iris and facial recognition). This article proposes a novel EEG fusion method to examine the reliability and durability of EEG biometric markers across recording sessions. Our hypothesis is that models trained using EEG signals collected during various elicitation protocols can capture generalised brain patterns that pertain personalised information which can improve the durability of biometric systems. Different protocols are likely to produce different responses across brain regions, which can result in more identifiable responses from EEG. In our approach, an end-to-end convolutional neural network (CNN) model is adopted for feature extraction and classification of raw EEG data. The proposed method is evaluated on two EEG datasets which were collected over two separate sessions on different days using multiple different EEG elicitation protocols. Within-session and across-session experiments were conducted. Results for within session experiments showed that CNN models with protocol fusion can achieve similar if not better results than models trained with single protocol. In across-session scenarios, models trained with the proposed protocol fusion approach significantly outperformed single protocol based models. The obtained results illustrate the durability and reliability capabilities of the proposed fusion approach.

Rough set theory has proved efficient for many applications, including finding hidden patterns in data, data reduction, evaluating significance of data, and generating sets of decision rules from data. Recently, it has shown to be effective approach for constructing ensemble learning systems as well. Learning classifier systems are genetics-based machine learning techniques that have recently shown a high degree of competence on a variety of data mining problems. Attempts to improve its generalization capabilities in the literature using ensemble learning lack a systematic and robust techniques for partitioning the problem at hand. It is well known that ensemble performance depends on the problem decomposition technique being used. Rough set based learning classifier system ensemble is proposed in this paper. In this approach, rough set attribute reduction is used to generate a set of reducts, and then a diverse subset of these reducts is selected to train an ensemble of base classifiers. The experiments show that classification accuracy of reduct-based ensemble systems outperforms a single learning classifier system model. It has also shown better performance than either an ensemble of classifiers with all attributes being used or a single classifier trained by a single reduct. It has also shown competitive performance to the random subspace ensemble strategy on the set of real data sets used in the experiments.