The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

Nowadays, IoT technology has become an essential part of many aspects of life and business. Nevertheless, such widespread application has come at the cost of many security concerns that threaten data privacy and diminish IoT utilization momentum in critical applications such as the smart grid and intelligent transportation systems. To address this challenge, several approaches have been proposed to detect and prevent IoT cyberthreats from materializing. Anomaly detection is one of these approaches that defines the boundaries of legitimate (normal) behavior. Any behavior that falls outside these boundaries is considered anomalous. However, these solutions should have the capability to adapt and adjust to environmental changes that prompt IoT nodal behavioral aberrations, except they only assume that these nodes show the same behavior. This assumption does not hold due to the heterogeneity of IoT nodes and the dynamic nature of an IoT network topology. Furthermore, existing adaptive solutions rely on static (pre-defined) thresholds to control the moment for retraining updates. The cost is heavy for highly dynamic environments like IoT as it leads to an unnecessary higher frequency of retraining. Consequently, the model becomes unstable and adversely affects its accuracy and robustness. This paper addresses these problems by offering an improved Adaptive Anomaly Detection (AAD) methodology that resolves the heterogeneity issues by building local profiles that define normal behavior at each IoT node. The One Class Support Vector Machines (OC-SVM) was used to build these profiles. Then, K-Means clustering was used to build a global profile that represents all network nodes. A Local-Global Ratio-Based (LGR) Anomaly Detection scheme is advanced and was enlisted to control the adaptation process by adjusting the threshold of adaptive functionality dynamically based on the “current” situation to prevent unnecessary retraining. An Ensemble of Deep Belief Networks (EDBN) is developed and used to train the anomaly detection model. Additionally, this study's proposes a new Minimized Redundancy Discriminative Feature Selection (MRD-FS) technique to resolve the issue of redundant features. The MRD-FS experimental evaluation shows detection accuracy higher than those of the related solutions including lower false alarm rates. This validates the efficacy of the proposed model for various IoT applications such as smart grids, smart homes, smart cities and intelligent transportation systems.

Access control is one of the imperative defense frontlines for digital computing environments especially for pervasive computing environments such as cloud computing and the internet of things (IoT) characterized by heterogeneous and distributed services and resources. Although, numerous access control models (ACM) have been implemented and used to prevent unauthorized access to cloud services and resources, these models may not prove efficacious to prevent overexposure and underexposure of data. More granular access control models or fine-grained access control therefore are developed to enable more itemized authorization policies. This paper presents a comparative analysis of different ACM used in the cloud with a focus on fine-grained access control models. First, we discuss the existing ACMs then and present the state-of-the-art research efforts carried out in the domain. Finally, we provide our analysis of the existing cloud-based access control methods and identify the gaps in the field for future work. This survey aims to improve the understanding and application of ACMs in the cloud and pave the road for future works in this area.

Public availability of electronic health records raises major privacy concerns, as that data contains confidential personal information of individuals. Publishing such data must be accompanied by appropriate privacy-preserving techniques to avoid or at least minimize privacy breaches. The task of privacy preservation becomes even more challenging when the data have multiple sensitive attributes (SAs). Privacy risks increase even further when an individual has multiple records (1:M) in a dataset, a rather typical situation with electronic health records (EHRs). To overcome these privacy issues, the methodologies known as 1:M generalization and l-anatomy have been proposed by the research community. However, these models fail to provide optimal privacy protection, data utility and security against certain types of attacks, such as gender-specific SA attacks. In this paper, we propose a generic 1:M data privacy model, called G-model, which provides guaranteed data privacy with high data utility and no information loss. Our G-model maintains separate groups and caches of male and female SAs, thus protecting privacy against gender-specific SA attacks. Furthermore, G-model avoids generalization, thus providing high data utility with no information loss. Experiments performed on three real-world datasets (Adult, Informs, and YouTube datasets) have shown that the proposed model is more efficient and better at privacy protection than the existing models from the literature.