Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions. 

A survey on security in internet of things with a focus on the impact of emerging technologies

Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.

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Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

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SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Providing security to the healthcare data stored in an IoT-cloud environment is one of the most challenging and demanding tasks in recent days. Because the IoT-cloud framework is constructed with an enormous number of sensors that are used to generate a massive amount of data, however, it is more susceptible to vulnerabilities and attacks, which degrades the security level of the network by performing malicious activities. Hence, Artificial Intelligence (AI) technology is the most suitable option for healthcare applications because it provides the best solution for improving the security and reliability of data. Due to this fact, various AI-based security mechanisms are implemented in the conventional works for the IoT-cloud framework. However, it faces significant problems of increased complexity in algorithm design, inefficient data handling, not being suitable for processing the unstructured data, increased cost of IoT sensors, and more time consumption. Therefore, this paper proposed an AI-based intelligent feature learning mechanism named Probabilistic Super Learning- (PSL-) Random Hashing (RH) for improving the security of healthcare data stored in IoT-cloud. Also, this paper is aimed at reducing the cost of IoT sensors by implementing the proposed learning model. Here, the training model has been maintained for detecting the attacks at the initial stage, where the properties of the reported attack are updated for learning the characteristics of attacks. In addition to that, the random key is generated based on the hash value of the data matrix, which is incorporated with the standard Elliptic Curve Cryptography (ECC) technique for data security. Then, the enhanced ECC-RH mechanism performs the data encryption and decryption processes with the generated random hash key. During performance evaluation, the results of both existing and proposed techniques are validated and compared using different performance indicators.

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Healthcare Data Security Using IoT Sensors Based on Random Hashing Mechanism

Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application needs in IoT. In this article, we highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT. Specifically, we first give an introduction of sensing technologies widely employed in IoT applications. Then, we systematically expatiate on more design issues for collaborative sensing, i.e., sensing models, deployment methods, sensing scheduling strategies and metrics for sensing quality. Next, the network-wide sensing collaboration is expatiated through three fundamental types of coverage problems as well as their solutions and insightful observations. Furthermore, we discuss promising sensing applications and elaborate how the sensing problem is established from the realistic application requirements and how the sensing quality impacts the application performance. Finally, we discuss several inspiring future research directions.

Collaborative Sensing in Internet of Things: A Comprehensive Survey

The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms– branches of Artificial Intelligence (AI)– can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Security vulnerabilities have become significant concerns due to growing demand of Internet of Things (IoT) not only for home automation systems, but also for various industrial applications. Central security technologies are vulnerable to single-point failure that could reduce attack handling technology efficiency. Distributed security frameworks are used in resource-constrained heterogeneous IoT networks. Conventional security strategies are insufficient to protect the distributed and dynamic existence of IoT networks. Conversely, Software Define Network (SDN) and Blockchain are two emerging techniques expected to solve heterogeneous IoT network security. This analysis work therefore mainly seeks to examine IoT network features, security specifications, and challenges. Thereafter, well-known threats or attacks are analyzed in IoT. SDN and blockchain-based countermeasures are addressed for IoT network security with a case study. Finally, to enhance security and privacy , numerous open issues are discussed.

Shakila Zaman

Papers

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Towards SDN and Blockchain based IoT Countermeasures: A Survey