Secure Internet Of Things Systems Research Articles

Deep Learning for Anomaly Detection in IoT Systems: Techniques, Applications, and Future Directions

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time data collection and processing, leading to unprecedented levels of efficiency and convenience. However, this increased connectivity and data flow also introduce significant security risks, particularly in the realm of anomaly detection. This study delves into the use of deep learning techniques for identifying abnormalities in IoT systems, providing a thorough analysis of state-of-the-art methods, assessing their effectiveness across different IoT scenarios, and exploring future research directions. We review current deep learning-based anomaly detection techniques, examine their applications in real-world settings, and discuss potential improvements and innovations. By synthesizing the latest research and developments, this paper aims to offer a comprehensive understanding of how deep learning can bolster the security and performance of IoT systems. Our findings highlight the importance of robust anomaly detection mechanisms in safeguarding IoT networks and underscore the need for continued advancements in this area. Through this study, we seek to contribute valuable insights into the application of deep learning for enhancing IoT system security and reliability, ultimately supporting the sustainable growth and integration of IoT technologies across various domains.

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. Quantum computing can enhance encryption algorithms to make IoT data more secure for intelligent IoT applications. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks

The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Systematic Literature Review on the Role of Big Data in IoT Security

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed.

Ensuring the security of the Internet of Things: A deep dive into current network weaknesses and approaches for strengthening

As the Internet of Things (IoT) rapidly takes center stage in today's digital revolution, its integration across various sectors is becoming remarkably ubiquitous. Nevertheless, with the accelerated adoption of IoT, a surge of security concerns has concurrently surfaced. Acknowledging this paradigm, this article delves deep into the underlying architecture of IoT systems, spotlighting their constituent elements and the complex interplay between them. Through meticulous literature reviews and scrutiny of real-world incidents, the most prevalent security vulnerabilities inherent to these systems are pinpointed. For each identified vulnerability, a suite of protective countermeasures is meticulously crafted, underscoring the imperative to preemptively tackle these security gaps. It's paramount to recognize that IoT system security isn't merely a technical requisite but a cornerstone ensuring the system's integrity and resilience. As businesses and individuals increasingly rely on IoT for their day-to-day operations, prioritizing security isn't just a luxury; it's an absolute necessity. Developers and users alike must remain vigilant, consistently updating and fortifying their systems. In doing so, the full potential of IoT can be harnessed while minimizing risks, ensuring that these systems remain both robust and trustworthy in an ever-evolving digital landscape.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

A survey of security challenges, attacks in IoT

A network of linked devices that can gather, process, and exchange data is known as the Internet of Things (IoT). It delivers several benefits such as convenience, efficiency and automation. However, IoT also poses significant security concerns that necessity to be describe like the heterogeneity and diversity of IoT devices with different hardware, software and communication protocols. Now a days the large scale and complexity of IoT systems, which may involve multiple stakeholders, domains and layers. The limited resources and capabilities of IoT devices, which may affect their ability to perform security functions such as encryption, authentication and update. The vital and evolving features of IoT environments, which may introduce new threats and vulnerabilities over time. These challenges require novel and holistic approaches to ensure that privacy and security of IoT systems and its users. With the develop of security standards and best practices for IoT devices and systems it should include Implementation of security by design and security by default principles for IoT development and deployment by adopting risk-based and adaptive security mechanisms for IoT management, operation and enhanced security awareness and education among IoT users and stakeholders.

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized internet-based technologies and have led to an increase in information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS have recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve IDS model.

Smart Building Security using ESP32 based AES One Bio-key and Owner's Biometrics Encryption Technology

Internet of Things (IoT) connects smart devices to the internet, but it poses significant security challenges. ‘Security measures like access control, authentication, and encryption must be implemented during IoT system development to safeguard sensitive information and ensure secure transmission. Regular security updates, ongoing assessment, and improved measures are necessary to address evolving threats. Data security is crucial for overall IoT system security, given its unique features like perception, transmission, and processing. In this paper, it is suggested using encryption for data sent over Wi-Fi to improve Internet of Things (IoT) security. Advanced Encryption Standard (AES) commonly used encryption standard, and it is the encryption technique employed. AES algorithm uses one bio-key (variable key every minute) to maximize security and the capacity to fend off attacks. It is added the owner's biometric to the bio-key for authentication and security in order to authenticate the system. Our technology stands out with robust encryption and authentication against numerous assaults because the owner's biometrics are encrypted and it is added to the bio-key made up of the minute-by-minute changes to the year, month, day, hour, and minute. The data is encoded in ESP32 and sent to the local server and then stored in a Python language database using the HTTP protocol. The database is hosted on XAMPP, which is a web server package that allows easy database management.

AKILLI EV SİSTEMLERİNDE KUANTUM ANAHTAR DAĞITIMI

In recent years, with the increase in the widespread use of the internet, the usage areas and the number of users have increased rapidly. Technological advances and efforts to make life easier bring innovation and transformation in many sectors. One of these innovative approaches is the use of Internet of Things (IoT) in home systems. The concept of IoT refers to the ability of objects to communicate with each other over the internet. Smart home systems stand out as systems where different devices come together work in interaction and can be controlled remotely. While this communication and integration offers various advantages, it also brings security risks. Ensuring security in IoT systems basically includes objectives such as confidentiality, integrity, and authentication. In this study, how the key used in the encryption of data transmitted between the gateway and the cloud in smart home systems can be strengthened with the B92 Quantum Key Distribution protocol is implemented through a scenario. The effect of the Quantum Key Distribution protocol in providing privacy and its advantages against man-in-the-middle attacks are emphasized in this study.

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Security is an important field in the Internet of Things (IoT) systems. The IoT and security are topical domains. Because it was obtained 35,077 document results from the Scopus database. Hence, the AI (Artificial Intelligence) has proven its efficiency in several domains including security, digital marketing, healthcare, big data, industry, education, robotic, and entertainment. Thus, the contribution of AI to the security of IoT systems has become a huge breakthrough. This contribution adopts the artificial intelligence (AI) as a base solution for the IoT security systems. Two different subsets of AI algorithms were considered: Machine Learning (ML) and Deep Learning (DL) methods. Nevertheless, it is difficult to determine which AI method and IoT dataset are best (more suitable) for classifying and/or detecting intrusions and attacks in the IoT domain. The large number of existing publications on this phenomenon explains the need for the current state of research that covers publications on IoT security using AI methods. Thus, this study compares the results regarding AI algorithms that have been mentioned in the related works. The goal of this paper is to compare the performance assessment of the existing AI algorithms in order to choose the best algorithm as well as whether the chosen algorithm can be used for classifying or/and detecting intrusions and attacks in order to improve security in the IoT domain. This study compares these methods in term of accuracy rate. Evaluating the current state of IoT security, AI and IoT datasets is the main aim for considering our future work. After that, this paper proposes, as result, a new and general taxonomy of AI techniques for IoT security (classification and detection techniques). Finally, the obtained results from this assessment survey that was dedicated to research conducted between 2018 and 2023 were satisfactory. This paper provides a good reference for researchers and readers in the IoT domain.

Designing an internet of things laboratory to improve student understanding of secure IoT systems

In response to an alarming shortage of workers in cybersecurity and a growing skills gap, the U.S. Department of Defense is taking steps to build cybersecurity capacity through workforce training and education. In this paper, we present an approach to address this shortage and skills gap through the development of cybersecurity education courseware for internet of things (IoT) applications.To attract students and workers into the field of cybersecurity, it is important to design courseware that is exciting and tied to real-world problems. We describe our design for an embedded systems course taught at the graduate level for engineering and computer science students. The innovation in our approach is to select the fast-growing domain of healthcare and feature different IoT sensors that are seeing increased usage. These include barcode scanners, cameras, fingerprint sensors, and pulse sensors. These devices cover important functions such as patient identification, monitoring, and creating electronic health records. We use a password protected MySQL database as a model for electronic health records. We also demonstrate potential vulnerabilities of these databases to SQL injection attacks.We administered these labs and collected survey data from the students. We found a significant increase in student understanding of cybersecurity issues. The mean confidence level of the students in cybersecurity issues increased from 2.5 to 4.1 on a 5-point scale after taking this course, which represents a 65% increase. The instructional lab material has been uploaded to the web portal https://clark.center designated by the National Security Agency for dissemination. Our approach, design, and experimental validation methodology will be useful for educators, researchers, students, and organizations interested in re-skilling their workforce.

IoT Security Implementation using Machine Learning

This paper focuses on the implementation of machine learning algorithms to improve security in the Internet of Things (IoT) environment. IoT is becoming an essential part of our daily lives, and security is a significant concern in this domain. Traditional security measures are not enough to protect IoT systems from the increasing number of cyber-attacks. Machine learning algorithms can provide a better and more effective approach to detecting and mitigating security threats in IoT systems. This paper discusses various machine learning techniques such as supervised learning, unsupervised learning, and deep learning, and how they can be applied to improve security in IoT systems. The paper also explores the challenges and opportunities of using machine learning in IoT security and provides recommendations for future research. Overall, this paper provides a comprehensive overview of the role of machine learning in IoT security implementation and highlights the need for further research in this area.

An innovative approach for dynamic key dependent S-Box to enhance security of IoT systems

The Internet of Things (IoT) foresees pervasive and connected smart nodes that interact independently while offering varied services. Today, IoT nodes are a treasure house of data and hence security has become a major concern in IoT networks. Conventional cryptography faces many challenges during implementation in IoT devices. These challenges can be tackled by lightweight cryptography which is highly secure with less resource requirements. The vital characteristic of security is fulfilled by one out of 6 inner structures such as Substitution-Permutation Network, Feistel Network, General Feistel Network, Add-Rotate-XOR, Non Linear-Feedback Shift Register and Hybrid. PRESENT, which is a NIST approved lightweight cryptographic algorithm with SPN structure uses fixed or static S-Box. This permits attackers to explore S-Box and identify weaknesses of the algorithm. Key dependent approaches are better because it impedes an attacker from any offline analyses of attacks on the S-Box. Additionally, this is susceptible to linear and differential attacks. The static S Box does not possess superior diffusion and does not have a good rate of avalanche effect. So, the solution is to create a robust dynamic key dependent algorithm with nonlinear S-Boxes to secure the communication between IoT devices and platform. Security analysis is done on Non-linearity, Differential and Linear Cryptanalysis and Avalanche characteristics, besides, encryption of IoT data to demonstrate its aptness for cold chain security applications.© 2017 Elsevier Inc. All rights reserved.

The Advance of Internet of Things Security Threats and Possible Measures

The Internet of Things (IoT) is a new stage of human informatization development after the Internet. With the IoT, physical devices can seamlessly exchange and process data with each other, further improving the human ability to process information. However, Internet of Things security research is still preliminary despite frequent data breaches and security incidents. This paper starts with the introduction of the IoT and introduces its definition, technical characteristics, and hierarchical architecture of the IoT. After that, the security threats that may be encountered at each layer are discussed, and finally, the ways to enhance the security of IoT. This paper aims to examine the security issues that arise in the IoT system and investigate the security measures that can be employed to serve as security technology guidelines for constructing secure IoT systems.

Internet of Things: Security and Privacy standards

Ensuring security in the interaction of the Internet of Things (IoT) has focused the attention of many researchers. At present, the issue of standards is a very complex and important one that directly affects both the development and implementation of the Internet of Things in everyday life. There are many standards and protocols that may not be consistent across different layers of the architecture. This paper provides an overview of the current International Internet of Things Security Standards, which are discussed in various aspects such as terminology, architecture models, security and integration scenarios, classification and protocols. A comparative analysis of standards and related research is conducted to help inform decisions about the security of IoT systems during their development and production. This research aims to overcome the difficulties encountered and develop practical recommendations for the selection of controls and security of connected IoT devices. The research methodology included identification of gaps in IoT standards, analysis of existing problems and proposed solutions.

Joint IoT/ML Platforms for Smart Societies and Environments: A Review on Multimodal Information-Based Learning for Safety and Security

The application of the Internet of Things (IoT) is highly expected to have comprehensive economic, business, and societal implications for our smart lives; indeed, IoT technologies play an essential role in creating a variety of smart applications that improve the nature and well-being of life in the real world. Consequently, the interconnected nature of IoT systems and the variety of components of their implementation have given rise to new security concerns. Cyber-attacks and threats in the IoT ecosystem significantly impact the development of new intelligent applications. Moreover, the IoT ecosystem suffers from inheriting vulnerabilities that make its devices inoperable to benefit from instigating security techniques such as authentication, access control, encryption, and network security. Recently, great advances have been achieved in the field of Machine Intelligence (MI), Deep Learning (DL), and Machine Learning (ML), which have been applied to many important applications. ML and DL are regarded as efficient data exploration techniques for discovering “normal” and “abnormal” IoT component and device behavior inside the IoT ecosystem. Therefore, ML/DL approaches are required to convert the security of IoT systems from providing safe Device-to-Device (D2D) communication to providing security-based intelligence systems. The proposed work examines ML/DL technologies that may be utilized to provide superior security solutions for IoT devices. The potential security risks associated with the IoT are discussed, including pre-existing and newly emerging threats. Furthermore, the benefits and challenges of DL and ML techniques are examined to enhance IoT security.

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

Juliet-PUF: Enhancing the Security of IoT-Based SRAM-PUFs Using the Remanence Decay Effect

The cloud-based Internet of Things (IoT) enables the creation of innovative computer applications based on sensing, analyzing, and controlling the physical world. IoT deployments, however, are at a particular risk of counterfeiting, through which an adversary can corrupt the entire ecosystem. Therefore, entity authentication of edge devices is considered an essential part of the security of IoT systems. This research addresses the challenge of generating a unique ID in IoT devices. Unique IDs allow the IoT system maker to identify each edge device, and to ensure that only genuine devices can upload data to the cloud. Traditional ID mechanisms are not feasible in IoT, due to the edge device’s constrained runtime environment, or the additional costs and the deployment difficulties that they introduce. In this work, we present JULIET-PUF, a novel PUF-based method for IoT identification, which relies on SRAM content retrieval after power glitches with time differences. Our scheme comes with no added hardware cost on the edge device. We evaluate JULIET-PUF using a dataset of 24 units of a popular commercial IoT device, and show that it is on average 95.58 times more secure than the common use of SRAM-PUF.