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IoT, Volume 2, Issue 3 (September 2021) – 4 articles

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Article
Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks
IoT 2021, 2(3), 428-448; https://doi.org/10.3390/iot2030022 - 27 Jul 2021
Viewed by 212
Abstract
The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing [...] Read more.
The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing for the launch of multiple attacks via large-scale botnets through the IoT. These attacks have been successful in achieving their heinous objectives. A strong identification strategy is essential to keep devices secured. This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data. The proposed model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Our proposed binary and multiclass classification model achieved an exceptionally high level of accuracy, precision, recall, and F1 score. Full article
(This article belongs to the Special Issue Industrial IoT as IT and OT Convergence: Challenges and Opportunities)
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Article
Achieving Ethical Algorithmic Behaviour in the Internet of Things: A Review
IoT 2021, 2(3), 401-427; https://doi.org/10.3390/iot2030021 - 04 Jul 2021
Viewed by 502
Abstract
The Internet of Things is emerging as a vast, inter-connected space of devices and things surrounding people, many of which are increasingly capable of autonomous action, from automatically sending data to cloud servers for analysis, changing the behaviour of smart objects, to changing [...] Read more.
The Internet of Things is emerging as a vast, inter-connected space of devices and things surrounding people, many of which are increasingly capable of autonomous action, from automatically sending data to cloud servers for analysis, changing the behaviour of smart objects, to changing the physical environment. A wide range of ethical concerns has arisen in their usage and development in recent years. Such concerns are exacerbated by the increasing autonomy given to connected things. This paper reviews, via examples, the landscape of ethical issues, and some recent approaches to address these issues concerning connected things behaving autonomously as part of the Internet of Things. We consider ethical issues in relation to device operations and accompanying algorithms. Examples of concerns include unsecured consumer devices, data collection with health-related Internet of Things, hackable vehicles, behaviour of autonomous vehicles in dilemma situations, accountability with Internet of Things systems, algorithmic bias, uncontrolled cooperation among things, and automation affecting user choice and control. Current ideas towards addressing a range of ethical concerns are reviewed and compared, including programming ethical behaviour, white-box algorithms, black-box validation, algorithmic social contracts, enveloping IoT systems, and guidelines and code of ethics for IoT developers; a suggestion from the analysis is that a multi-pronged approach could be useful based on the context of operation and deployment. Full article
Article
Attacks and Defenses for Single-Stage Residue Number System PRNGs
IoT 2021, 2(3), 375-400; https://doi.org/10.3390/iot2030020 - 25 Jun 2021
Viewed by 247
Abstract
This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. [...] Read more.
This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses. Full article
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Article
A Client/Server Malware Detection Model Based on Machine Learning for Android Devices
IoT 2021, 2(3), 355-374; https://doi.org/10.3390/iot2030019 - 24 Jun 2021
Viewed by 392
Abstract
The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for malware detection on [...] Read more.
The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for malware detection on Android devices to protect private and financial information, for the mobile applications of the ATISCOM project. This model is based on client/server architecture, to reduce the heavy computations on a mobile device by sending data from the mobile device to the server for remote processing (i.e., offloading) of the predictions. We then gradually optimized our proposed model for better classification of the newly installed applications on Android devices. We at first adopted Naive Bayes to build the model with 92.4486% accuracy, then the classification method that gave the best accuracy of 93.85% for stochastic gradient descent (SGD) with binary class (i.e., malware and benign), and finally the regression method with numerical values ranging from −100 to 100 to manage the uncertainty predictions. Therefore, our proposed model with random forest regression gives a good accuracy in terms of performance, with a good correlation coefficient, minimum computation time and the smallest number of errors for malware detection. Full article
(This article belongs to the Special Issue Cyber Security and Privacy in IoT)
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