The paper presents a survey and analysis on the current status and concerns of Internet of things (IoT) security. The IoT framework aspires to connect anyone with anything at anywhere. IoT typically has a three layers architecture consisting of Perception, Network, and Application layers. A number of security principles should be enforced at each layer to achieve a secure IoT realization. The future of IoT framework can only be ensured if the security issues associated with it are addressed and resolved. Many researchers have attempted to address the security concerns specific to IoT layers and devices by implementing corresponding countermeasures. This paper presents an overview of security principles, technological and security challenges, proposed countermeasures, and the future directions for securing the IoT.

As the Internet of Things (IoT) is gaining ground and becoming increasingly popular in smart city applications such as smart energy, smart buildings, smart factories, smart transportation, smart farming, and smart healthcare, the digital twin concept is evolving as complementary to its counter physical part. While an object is on the move, its operational and surrounding environmental parameters are collected by an edge computing device for local decision. A virtual replica of such object (digital twin) is based in the cloud computing platform and hosts the real-time physical object data, 2D and 3D models, historical data, and bill of materials (BOM) for further processing, analytics, and visualization. This paper proposes an end-to-end digital twin conceptual model that represents its complementary physical object from the ground to the cloud. The paper presents the proposed digital twin model’s multi-layers, namely, physical, communication, virtual space, data analytic and visualization, and application as well as the overlapping security layer. The hardware and software technologies that are used in building such a model will be explained in detail. A use case will be presented to show how the layers collect, exchange, and process the physical object data from the ground to the cloud.

The rapid growth of Internet of Things (IoT) is expected to add billions of IoT devices connected to the Internet. These devices represent a vast attack surface for cyberattacks. For example, these IoT devices can be infected with botnets to enable Distributed Denial of Service (DDoS) attacks. Signature-based intrusion detection systems are traditional countermeasures for such attacks. However, these methods rely on human experts and are time-consuming in terms of updates and may not exhaust all attack types especially zero-day attacks. Deep learning has shown some promise in intrusion detection. This paper shows that it is possible to use generative deep learning methods like Adversarial Autoencoders (AAE) and Bidirectional Generative Adversarial Networks (BiGAN) to detect intruders based on an analysis of the network data. The recently posted full IoT-23 dataset based on Somfy door lock, Philips Hue and Amazon Echo devices was used to train generative deep learning models to detect a variety of attacks like DDoS, and various botnets like Mirai, Okiruk and Torii. Over 1.8 million network flows were used to train the various models. The resulting generative models outperform traditional machine learning techniques like Random Forests. Both AAE and BiGAN-based models were able to achieve an F1-Score of 0.99. A BiGAN to detect unknown attacks was also trained to detect novel zero-day attacks with an F1-Score from 0.85 to 1.

Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each complication was as follows: 428—metabolic syndrome, 836—dyslipidemia, 223—neuropathy, 233—nephropathy, 240—diabetic foot, 586—hypertension, 498—obesity, 228—retinopathy. Repeated stratified k-fold cross-validation (with k = 10 and a total of 10 repetitions) was employed for a better estimation of the performance. Accuracy and F1-score were used to evaluate the models’ performance reaching a maximum of 97.8% and 97.7% for accuracy and F1-scores, respectively. Moreover, by comparing the performance achieved using different attributes’ sets, it was found that by using a selected number of features, we can still build adequate classifiers.

With the advancement of computers and technology, security threats are also evolving at a fast pace. Botnets are one such security threat which requires a high level of research and focus in order to be eliminated. In this paper, we use machine learning to detect Botnet attacks. Using the Bot-IoT and University of New South Wales (UNSW) datasets, four machine learning models based on four classifiers are built: Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Trees. Using 82,000 records from UNSW-NB15 dataset, the decision trees model has yielded the best overall results with 99.89% testing accuracy, 100% precision, 100% recall, and 100% $\Gamma-$score in detecting botnet attacks.