Internet Of Things Security Problems Research Articles

Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network

In the face of growing Internet of Things (IoT) security challenges, traditional Intrusion Detection Systems (IDSs) fall short due to IoT devices’ unique characteristics and constraints. This paper presents an effective, lightweight detection model that strengthens IoT security by addressing the high dimensionality of IoT data. This model merges an asymmetric stacked autoencoder with a Deep Neural Network (DNN), applying one-class learning. It achieves a high detection rate with minimal false positives in a short time. Compared with state-of-the-art approaches based on the BoT-IoT dataset, it shows a higher detection rate of up to 96.27% in 0.27 s. Also, the model achieves an accuracy of 99.99%, precision of 99.21%, and f1 score of 97.69%. These results demonstrate the effectiveness and significance of the proposed model, confirming its potential for reliable deployment in real IoT security problems.

Blockchain for Enhancing IoT Privacy and Security

The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues

Blockchain for Cybersecurity in Edge Networks

The Blockchain is one of the most promising and artistic cybersecurity solutions. It has been practiced in a variety of reinforcements, including healthcare, transportation, and Internet of Things (IoT) applications. However, Blockchain has a colossal scalability challenge, limiting its ability to control services with high transaction volumes. Edge computing, on the other hand, was designed to allow cloud services and resources to be deployed at the network's edge, although it now faces issues in terms of decentralized security and management. The unification of edge computing and Blockchain within one solution jar provides a vast scale of storage systems, database servers, and authenticity computation towards the end in a safe fashion. Despite the potential of interconnected edge computing and Blockchain environments, difficulties such as scalability, resource management, function integration, self-organization, and new security concerns must be addressed before broad adoption. This article provides an overview of the secure IoT framework, paradigms, enablers, and security problems of combining Blockchain and intelligent edge computing. Finally, broader viewpoints for future research directions are investigated.

Dynamic coefficient symmetric polynomial-based secure key management scheme for Internet of Things (IoT) networks.

With the extensive application and continuous expansion of the Internet of Things (IoT), the access of a large number of resource-limited nodes makes the IoT application face a variety of security vulnerabilities and efficiency limitations, and the operating efficiency and security of IoT are greatly challenged. Key management is the core element of network security and one of the most challenging security problems faced by wireless sensor networks. A suitable key management scheme can effectively defend against network security threats. However, among the key management schemes that have been proposed so far, most of them do not take into account the efficiency in terms of connectivity rate and resource overhead, and some of them even have security risks. In this article, based on the symmetric polynomial algorithm, a dynamic coefficient symmetric polynomial key management scheme is proposed to better solve the IoT security problem. In this scheme, the nodes' IDs are mapped into the elements of the shared matrix M by the identity mapping algorithm, and these elements are used to construct polynomials P(x,y) to generate pairwise keys. The communicating nodes have their own coefficients of P(x,y) and thus have higher connectivity. The overall performance evaluation shows that the scheme significantly improves the resilience against node capture and effectively reduces the communication and storage overheads compared to the previous schemes. Moreover, the scheme overcomes the λ-security of symmetric polynomial key management scheme, and is able to provide a large pool of polynomials for wireless sensor networks, facilitating large-scale application of nodes.

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data.

The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.

Managing security in IoT by applying the deep neural network-based security framework

Security issues and Internet of Things (IoT) risks in several areas are growing steadily with the increased usage of IoT. The systems have developed weaknesses in computer and memory constraints in most IoT operating systems. IoT devices typically cannot operate complicated defense measures because of their poor processing capabilities. A shortage of IoT ecosystems is the most critical impediment to developing a secured IoT device. In addition, security issues create several problems, such as data access control, attacks, vulnerabilities, and privacy protection issues. These security issues lead to affect the originality of the data that cause to affects the data analysis. This research proposes an AI-based security method for the IoT environment (AI-SM-IoT) system to overcome security problems in IoT. This design was based on the edge of the network of AI-enabled security components for IoT emergency preparedness. The modules presented detect, identify and continue to identify the phase of an assault life span based on the concept of the cyberspace killing chain. It outlines each long-term security in the proposed framework and proves its effectiveness in practical applications across diverse threats. In addition, each risk in the borders layer is dealt with by integrating artificial intelligence (AI) safety modules into a separate layer of AI-SM-IoT delivered by services. It contrasted the system framework with the previous designs. It described the architectural freedom from the base areas of the project and its relatively low latency, which provides safety as a service rather than an embedded network edge on the internet-of-things design. It assessed the proposed design based on the administration score of the IoT platform, throughput, security, and working time

DOES SMART HOME HAVE WIDE OPEN DOORS? MQTT COMMUNICATION PROTOCOL STANDARDIZATION - POTENTIAL MISSING RING OF THE IOT NETWORKS SECURITY CHAIN

This paper pointed out the benefits as well as the security risk that is the result of inconsistencies of software applications from different manufacturers and the lack of standards in IoT devices networks. Message Queue Telemetry Transport (MQTT) is now one of the most used open protocols in the Internet of Things. The term IoT is often followed by a great enthusiasm of researchers. End-users also share the same sentiment, ignoring the security risks that arise from dropping and reconnecting IoT devices that very often send passwords to server in plain text, while manufacturers often remain silently indifferent. They primary put focus on profitability, but not on safety. In other words, the door on smart homes remains wide open for hackers. This paper gives an overview of one potential solution of the Internet of Things security problem.

A Classification-Based Blockchain Architecture for Smart Home with Hierarchical PoW Mechanism

Smart home, as a typical Internet of Things (IoT) application, provides people with a variety of conveniences. Unfortunately, it may suffer from security and privacy issues. Currently, blockchain theory is considered as one of the potential solutions to the IoT security problem. However, according to the rules of blockchain, it requires large storage to store distributed ledgers and undertakes long latency caused by proof of work (PoW), which cannot be performed by resource-constrained IoT devices. To address the issue, we propose a classification-based blockchain architecture with a hierarchical PoW mechanism, which can reduce the storage consumption and decrease the latency. In our architecture, we divide IoT devices into several child nodes by data classification and convert the data storage into partial network storage. Furthermore, we try to set the moderate-cost security grades (SG) to adjust the difficulty of PoW for reduction of latency. Finally, comparing the performance of our scheme with the traditional method and current technology, the proposed architecture not only takes up less storage (i.e., almost 90% reduction) but also increases efficiency (i.e., almost 50% running time saving) while ensuring safety.

A Review on the IOT System

The advancement of computer science and communication technologies is being enabled by something called the Internet of Things (IoT). The IoT is being used in many fields, so maintaining its Security is becoming an increasingly important concern. The IoT industry as a whole is going to be significantly impacted as a result of this. This study examines the IoT security problem at the IoT system's perception, network, and application levels suggests a secure way to build IoT and explains how to make it safe based on the problems that already exist in the IoT framework. The study begins with the concept of IoT, its characteristics, and how it functions as a system. Finally, the theoretical foundations necessary to construct a trustworthy IoT security system will be presented. The IoT will enable inanimate things to participate in human activities. IoT security is notoriously difficult to handle because items are often integrated into the control procedures of more complicated systems. In practice, the IoT is a complicated paradigm in which people interact with a technological ecosystem based on intelligent devices via various complicated procedures. Interactions between a person, an intelligent object, a technical environment, and a process show IoT security's cognitive and systemic components.

A survey on boosting IoT security and privacy through blockchain

The constant development of interrelated computing devices and the emergence of new network technologies have caused a dramatic growth in the number of Internet of Things (IoT) devices. It has brought great convenience to people’s lives where its applications have been leveraged to revolutionize everyday objects connected in different life aspects such as smart home, healthcare, transportation, environment, agriculture, and military. This interconnectivity of IoT objects takes place through networks on centralized cloud infrastructure that is not constrained to national or jurisdictional boundaries. It is crucial to maintain security, robustness, and trustless authentication to guarantee secure exchange of critical user data among IoT objects. Consequently, blockchain technology has recently emerged as a tenable solution to offer such prominent features. Blockchain’s secure decentralization can overcome security, authentication, and maintenance limitations of current IoT ecosystem. In this paper we conduct a comprehensive literature review to address recent security and privacy challenges related to IoT where they are categorized according to IoT layered architecture: perception, network, and application layer. Further, we investigate blockchain technology as a key pillar to overcome many of IoT security and privacy problems. Additionally, we explore the blockchain technology and its added values when combined with other new technologies as machine learning especially in intrusion detection systems. Moreover, we highlight challenges and privacy issues resulted due to integration of blockchain in IoT applications. Finally, we propose a framework of IoT security and privacy requirements via blockchain technology. Our main contribution is to exhaust the literature to highlight the recent IoT security and privacy issues and how blockchain can be utilized to overcome these issues, nevertheless; we address challenges and open security issues that blockchain may impose on the current IoT systems. Research findings formulate a rigid foundation upon which an efficient and secure adoption of IoT and blockchain is highlighted accordingly.

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.

An efficient method for group key management in Internet of Things using machine learning approach

Internet of Things (IoT) represent a network of pervasive devices with different characteristics and features. It is important to provide security in IoT as Internet plays a prominent role in the communication process. In this paper, a novel group and hierarchical group key management scheme is proposed. The devices are found into groups and placed into various levels of hierarchy. The group leader shares a key to the members in the group and the key needs to be updated based on the entry and exit of the members into and out of the groups. Also, the machine learning techniques are used to make the system adapt to the provision of the keys to the devices which enter into the group. Incremental Gaussian Mixture Model is used to determine whether the particular device belong to the group or not. The proposed algorithm, machine learning based group and hierarchical key management is simulated and compared with the group and hierarchical key management scheme for solving security problems in IoT in terms of throughput and delay with varying no. of groups and overall cost with respect to no. of nodes and mobility speed.

ONS resolution prediction based on Rasch model.

IoT (Internet of Things) involves a wide range of fields, and its application scenarios are complex and diverse. Failure of security defense in any link of IoT may lead to huge information leakage and immeasurable losses. IoT security problem is affecting and restricting its application prospect, and has become one of the hotspots in the field of IoT. ONS (Object Naming Service) is responsible for mapping function from EPC code information to URI (Uniform Resource Identifier). The security mechanism of ONS has been extensively studied by more and more scholars in recent years. The purpose of this paper is to apply Rasch, a famous psychological model, to ONS resolution security technology. Through observing the past resolution result, the ability of ONS resolution and the difficulty of EPC code can be calculated. With the difference between the ability of ONS resolution and the difficulty of EPC code, this model can predict the probability of the ONS future resolution to achieve the purpose of privacy protection in IoT addressing. Through simulation and Ministep software, the feasibility of the model is verified.

A Hypergraph-Based Blockchain Model and Application in Internet of Things-Enabled Smart Homes

With the fast development and expansion of the Internet of Things (IoT), billions of smart devices are being continuously connected, and smart homes, as a typical IoT application, are providing people with various convenient applications, but face security and privacy issues. The idea of Blockchain (BC) theory has brought about a potential solution to the IoT security problem. The emergence of blockchain technology has brought about a change of decentralized management, providing an effective solution for the protection of network security and privacy. On the other hand, the smart devices in IoT are always lightweight and have less energy and memory. This makes the application of blockchain difficult. Against this background, this paper proposes a blockchain model based on hypergraphs. The aims of this model are to reduce the storage consumption and to solve the additional security issues. In the model, we use the hyperedge as the organization of storage nodes and convert the entire networked data storage into part network storage. We discuss the design of the model and security strategy in detail, introducing some use cases in a smart home network and evaluating the storage performance of the model through simulation experiments and an evaluation of the network.

IoT security: Review, blockchain solutions, and open challenges

With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth, with Cisco Inc. predicting to have 50 billion connected devices by 2020. However, most of these IoT devices are easy to hack and compromise. Typically, these IoT devices are limited in compute, storage, and network capacity, and therefore they are more vulnerable to attacks than other endpoint devices such as smartphones, tablets, or computers.In this paper, we present and survey major security issues for IoT. We review and categorize popular security issues with regard to the IoT layered architecture, in addition to protocols used for networking, communication, and management. We outline security requirements for IoT along with the existing attacks, threats, and state-of-the-art solutions. Furthermore, we tabulate and map IoT security problems against existing solutions found in the literature. More importantly, we discuss, how blockchain, which is the underlying technology for bitcoin, can be a key enabler to solve many IoT security problems. The paper also identifies open research problems and challenges for IoT security.

Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?

The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into this new computing paradigm is a major technical challenge. What IoT security problems can we solve using existing security principles? And, what new problems and challenges in this space will require new security mechanisms?

A Flexible Security Architecture for the Internet of Things

Security architecture is the key problem of each network construction, Internet of things (IoT) is no exception. Firstly, existing research results in relation to security architecture of IoT are summarized. Then, on the basis of the analysis of the IoT security problem, this paper discusses the network security service requirements of IoT. Finally, a novel hierarchical and flexible secure architecture for IoT is proposed, which includes the security perception layer, security network layer and security application layer. Meanwhile, it involves security services entity and security management entity. The security architecture can provide strong technical support for security construction of IoT.