Internet Of Things Security Threats Research Articles

A Framework for Mitigating DDoS and DOS Attacks in IoT Environment Using Hybrid Approach

The Internet of Things (IoT) has gained remarkable acceptance from millions of individuals. This is evident in the extensive use of intelligent devices such as smartphones, smart television, speakers, air conditioning, lighting, and high-speed networks. The general application area of IoT includes industries, hospitals, schools, homes, sports, oil and gas, automobile, and entertainment, to mention a few. However, because of the unbounded connection of IoT devices and the lack of a specific method for overseeing communication, security concerns such as distributed denial of service (DDoS), denial of service (DoS), replay, botnet, social engineering, man-in-the-middle, and brute force attacks have posed enormous challenges in the IoT environment. Regarding these enormous challenges, this study focuses on DDoS and DoS attacks. These two attacks have the most severe consequences in the IoT environment. The solution proposed in this study can also help future researchers tackle the expansion of IoT security threats. Moreover, the study conducts rigorous experiments to assess the efficiency of the proposed approach. In summary, the experimental results show that the proposed hybrid approach mitigates data exfiltration caused by DDoS and DoS attacks by 95.4%, with average network lifetime, energy consumption, and throughput improvements of 15%, 25%, and 60%, respectively.

Using API Call Sequences for IoT Malware Classification Based on Convolutional Neural Networks

Internet of Things (IoT) devices built on different processor architectures have increasingly become targets of adversarial attacks. In this paper, we propose an algorithm for the malware classification problem of the IoT domain to deal with the increasingly severe IoT security threats. Application executions are represented by sequences of consecutive API calls. The time series of data is analyzed and filtered based on the improved information gains. It performs more effectively than chi-square statistics, in reducing the sequence lengths of input data meanwhile keeping the important information, according to the experimental results. We use a multi-layer convolutional neural network to classify various types of malwares, which is suitable for processing time series data. When the convolution window slides down the time sequence, it can obtain higher-level positions by collecting different sequence features, thereby understanding the characteristics of the corresponding sequence position. By comparing the iterative efficiency of different optimization algorithms in the model, we select an algorithm that can approximate the optimal solution to a small number of iterations to speed up the convergence of the model training. The experimental results from real world IoT malware sample show that the classification accuracy of this approach can reach more than 98%. Overall, our method has demonstrated practical suitability for IoT malware classification with high accuracies and low computational overheads by undergoing a comprehensive evaluation.

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: 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.

Threats and Corrective Measures for IoT Security with Observance of Cybercrime: A Survey

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boost their susceptibility to cyberattacks. Consequently, the cybercrime rate is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit researchers in identifying appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare. The role of advanced technologies like artificial intelligence, machine learning, cloud computing, edge computing, fog computing, and blockchain technology in cybercrime investigation is also discussed. Lastly, various open research challenges in IoT to assist cybercrime investigation are explained to provide a new direction for further research.

Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey

Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Quantum Cryptography for Internet of Things Security

Internet of things (IoT) is a developing technology with a lot of scope in the future. It can ease various different tasks for us. On one hand, IoT is useful for us, on the other hand, it has many serious security threats, like data breaches, side-channel attacks, and virus and data authentication. Classical cryptographic algorithms, like the Rivest-Shamir-Adleman (RSA) algorithm, work well under the classical computers. But the technology is slowly shifting towards quantum computing, which has immense processing power and is more than enough to break the current cryptographic algorithms easily. So it is required that we have to design quantum cryptographic algorithms to prevent our systems from security breaches even before quantum computers come in the market for commercial uses. IoT will also be one of the disciplines, which needs to be secured to prevent any malicious activities. In this paper, we review the common security threats in IoT and the presently available solutions with their drawbacks. Then quantum cryptography is introduced with some of its variations. And finally, the analysis has been carried out in terms of the pros and cons of implementing quantum cryptography for IoT security.

A survey on internet of things security: Requirements, challenges, and solutions

Internet of Things (IoT) is one of the most promising technologies that aims to enhance humans’ quality of life (QoL). IoT plays a significant role in several fields such as healthcare, automotive industries, agriculture, education, and many cross-cutting business applications. Addressing and analyzing IoT security issues is crucial because the working mechanisms of IoT applications vary due to the heterogeneity nature of IoT environments. Therefore, discussing the IoT security concerns in addition to available and potential solutions would assist developers and enterprises to find appropriate and timely solutions to tackle specific threats, providing the best possible IoT-based services. This paper provides a comprehensive study on IoT security issues, limitations, requirements, and current and potential solutions. The paper builds upon a taxonomy that taps into the three-layer IoT architecture as a reference to identify security properties and requirements for each layer. The main contribution of this survey is classifying the potential IoT security threat and challenges by an architectural view. From there, IoT security challenges and solutions are further grouped by the layered architecture for readers to get a better understanding on how to address and adopt best practices to avoid the current IoT security threats on each layer.

Network Intrusion Detection for IoT Security Based on Learning Techniques

Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.

A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems

The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.

Reassessing Android Malware Analysis: From Apps to IoT System Modelling

Applications based on the Internet of Things (IoT) are increasingly vulnerable to disruption from cyber attacks. Developers and researchers attempt to prevent the growth of such disruption models, mitigate and limit their impact. This requires the understanding and characterization of things and the technologies that empower the IoT. Futhermore, tools to evaluate, analyze and detect security threats in IoT devices are strongly required. This paper presents a web tool, named GARMDROID, aimed to help IoT software developers and integrators to evaluate IoT security threats based on the visualization of Android application hardware requests. This procedure is based on the static analysis of permissions requested by Android applications. A mapping from the malware analysis data obtained to a SysML block definition diagram is also briefly described. This mapping shows how data can be used to model IoT systems under different proposals such as Model Integrated Mechatronics (MIM) and UML4IoT.

Security Privacy in Internet of Things(IOT)

The Internet Of Things(IOT) is interconnection between identifiable embedded computing devices. Internet of Things is highly on the rise from smart cities, environment, health, energy, vehicle, transport, public safety to our daily essentials. Internet of Things has completely revitalized these areas. IOT expect the advanced connectivity with devices services. This paper highlighted the security and privacy aspect of IOT and various security threats which are given in each concept related to IOT. Various techniques are used for security and privacy of IOT devices and easy to adapt by users.

Distributed Danger Assessment Model for the Internet of Things Based on Immunology

The Internet of Things (IoT) confronts complicated and changeful security threats. It harms IoT and brings IoT potential danger. However, the research achievements of the danger assessment technology for IoT are rare. To calculate the danger value of IoT with many dispersive sense nodes, the theoretical model of distributed danger assessment for IoT is explored in this paper. The principles and mechanisms of Artificial Immune System (AIS) are introduced into the proposed model. Data packets in IoT are captured in each gateway and converted into antigens in the simulated immune environment. Detectors use self-learning and self-adaptation mechanisms in AIS to evolve themselves to adapt the local IoT environment and detect security threats. The mechanism of antibody density is simulated to reflect the intensity of security threats which are happening. Through the detected security threats and their intensity, the values of IoT property and security threats’ harm are combined to assess the quantitative value of danger for IoT. Theoretical analysis shows that the proposed model is significative of theory and practice.

Research on Intrusion Detection for the Internet of Things Based on Clone Selection Principle

The fast development of the Internet of Things (IoT) makes its security problems appear gradually. It is urgent to study the intrusion detection technology for IoT security threats. An intrusion detection method based on the clone selection principle is proposed in this paper. The key elements in the clone selection theory are simulated. The clone selection algorithm is realized to be applied in IoT. Detection elements for security threats evolve to adapt the real IoT environment. The proposed method is expected to improve the detection efficiency of IoT security threats.

An IoT Anomaly Detection Model Based on Artificial Immunity

To resolve the anomaly detection problem in the distributed environment of the Internet of Things (IoT), an artificial immunity-based anomaly detection model for the IoT is proposed in this paper. The proposed model adopts artificial immune mechanisms to recognize anomaly behavior of IoT security threats. It is consisted of Anomaly detection agents (ADA) and Central Service System (CSS). ADA is deployed by the IoT gateway. It collects the initial data of the sense layer of the IoT. It works independently and produces excellent detection elements. It shares its excellent detection elements with the other ones and uploads them to the CSS. Theory analysis shows that the proposed model is able to adapt the local network environment of IoT and improve the anomaly detection ability in the global IoT environment.