Security Of Internet Of Things Devices Research Articles

IoT device security audit tools: a comprehensive analysis and a layered architecture approach for addressing expanded security requirements

IoT device security audit tools: a comprehensive analysis and a layered architecture approach for addressing expanded security requirements

TaPaFuzz: Hardware-accelerated RISC-V bare-metal firmware fuzzing using rapid job launches

Fuzz testing serves as a key technique in software security aimed at identifying unexpected program behaviors by repeatedly executing the target program with auto-generated random inputs. Testing is integral to IoT device security but is hampered by the minimal observability features of typical in-market IoT devices. Moreover, the slow nature of a RISC-V software emulation on x86 host CPUs and the inaccuracies introduced by compiling IoT applications to a different ISA for execution on host systems pose significant challenges. Our software-hardware co-design surmounts these hurdles. Fuzzing jobs are prepared and evaluated on a host computer, while the actual execution with high-throughput tracing is performed on an FPGA. Advances in the host-to-FPGA interface together with an accelerated reset procedure between Fuzzer jobs effectively hide the costly host-FPGA communication, increasing the single-thread fuzzing performance by up to factor 11.7x that of the leading QEMU-based fuzzer AFL++ running on a very fast x86 CPU. We demonstrate practical usability by evaluating our framework on a collection of bare-metal applications.

An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

Implementation of Chaotic Neural Key Generation Algorithm For IoT Devices

This paper presents a new method for generating encryption keys for Internet of Things (IoT) devices. This method combines chaos theory with neural networks to create secure and efficient keys. We use the Lorenz chaotic system to generate complex patterns and a Convolutional Neural Network (CNN) to learn and predict these patterns. This approach is designed to address the unique security challenges of IoT devices, which often have limited computing power. We tested our method with different key sizes and evaluated its performance using accuracy, loss, entropy, and correlation metrics. The results show that key sizes between 256 and 512 bits offer the best balance between model performance and security for IoT devices. We also conducted Diehard statistical tests, which our key generation method passed successfully, demonstrating its ability to produce high-quality random keys.

Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment

The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.

End User Security using Smart Devices with Ability to Access IoT services

This paper explores the security issues of smart devices in IoT environments and proposes solutions to enhance end-user protection. A qualitative approach, including a comprehensive literature review, was used to identify key security issues and best practices. Key vulnerabilities in IoT device security include insecure communication channels, weak authentication mechanisms, outdated firmware and software, and a lack of standardized security protocols. Current security practices among end-users show limited awareness and inconsistent implementation. Recommendations include adopting universal security standards, enhancing user education through regular programs, promoting advanced security tools like multi-factor authentication, and simplifying device management with user-friendly interfaces. This paper offers a comprehensive analysis of IoT security issues and practical recommendations to create a safer IoT ecosystem, ensuring technological advancements do not compromise user security.

Leveraging Digital Twins and Intrusion Detection Systems for Enhanced Security in IoT-Based Smart City Infrastructures

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems.

5G Security Features, Vulnerabilities, Threats, and Data Protection in IoT and Mobile Devices: A Systematic Review

The evolution of wireless communications, from the first to the fifth generation, has driven Internet of Things (IoT) advancements. IoT is transforming sectors like agriculture, healthcare, and transportation, but also presents challenges like spectrum bandwidth demand, speed requirements, and security issues. IoT environments, with embedded sensors and actuators, connect to other devices to transmit and receive data over the internet. These data are processed locally or in the cloud, enabling decision-making and automation. Various wireless technologies, including Bluetooth, Zigbee, LoRa, and WiFi, cater to specific IoT applications. 5G, with its high speeds, low latency, and efficient bandwidth sharing, is ideal for high-speed data transmission, and thus it provides robust infrastructure for low-latency and bandwidth-sensitive applications. This latest mobile network technology is being rapidly implemented worldwide, providing unprecedented throughput and low latency, which are crucial for the IoT. This paper presents a comprehensive systematic review on 5G security features, vulnerabilities, threats to mobile devices, security in IoT devices, common attacks to IoT devices and protection of data in this environment. Despite enhanced security, 5G faces challenges like Distributed Denial of Service (DDoS), Man In The Middle (MITM), cross-slice disruptions, and side-channel attacks.

Improved ECC cryptosystem-based lightweight authentication for SDN-based edge computing in IoT-enabled healthcare systems

The Internet of Things (IoT) consists of smart devices with limited resources that can identify and analyze data. In IoT-enabled healthcare systems, the security of IoT devices and the data they contain is complex. These devices in the healthcare industry, edge computing can provide low-latency information services at a reasonable cost. This work proposes a security infrastructure for Software Defined Network (SDN)-based edge computing in IoT-enabled healthcare systems consisting of three steps: Lightweight authentication, collaborative edge computing and job migration. The lightweight authentication step involves both Improved Lightweight Key Management (ILKM) and Improved Elliptic Curve Cryptography (IECC) schemes to ensure authentication among the devices and edge servers. Moreover, the patient’s data in IoT devices are scheduled to the appropriate edge server by examining the load balancing in the collaborative edge computing phase. This is done optimally using the adopted hybrid optimization model, Osprey Assisted Coati Optimization Algorithm (OACOA). Further, job migration takes place, in which the data is allocated to the edge server by comparing the capacity of edge servers and the data gets migrated to other servers by considering migration cost when the capacity of the edge server is overloaded. Finally, the efficiency of the suggested OACOA scheme is evaluated over traditional models with regard to several metrics. When considering the edge-server 30, the OACOA scheme achieves a makespan of 385, while conventional methods acquired fewer makespan ratings. Also, the OACOA approach obtained the highest security ratings (0.7143) on edge-server 20 when compared to existing schemes.

Choosing the machine learning algorithm for detecting intrusions into IoT

Aim. The paper aims to improve the security of IoT devices by applying machine learning algorithms to detect attacks against IoT networks. The relevance of the goal is defined by the ever-growing number of such attacks around the world and the widespread use of IoT systems. The paper provides relevant statistical data. An analysis of the available papers showed that various methods were examined individually and were not compared to each other, so the aim of this paper that consists in identifying the most promising machine learning algorithm for detecting attacks against IoT networks is of relevance. Methods. The paper used the following machine learning methods to detect attacks against IoT networks: logistic regression, SVC, random forest, K-nearest neighbour method, k-means method, naive Bayes classifier, and variants of gradient boosting (XGBoost, AdaBoost, and CatBoost). The novelty consists in the comparison of the outputs of the supervised algorithms with the unsupervised K-means in the context of detection of attacks against IoT networks. The attack detection systems under development were trained using the UNSWNB15 dataset that contains data on nine types of attacks. The number of entries is more than 80 thousand. More than half of the entries deal with attacks. The methods were compared using a number of metrics. Results. An intrusion detection system was structurally defined and implemented. The stages of its operation include the analysis of input data and the output of final statistical data. The results show that the random forest algorithm is the best one out of those examined. The method also performs well in terms of learning speed. That means that the algorithm can be deployed and applied with the greatest success. Conclusions. This paper presents the results of comparing various machine learning algorithms in the context of IoT device intrusion detection. The accuracy and the ROC-AUC curve are used to evaluate the efficiency of the employed models. Having compared the models of the employed algorithms we found that the RandomForestClassifier model has the highest accuracy and a high AUC, which means that this algorithm is the most efficient in terms of IoT network intrusion detection. Further research will be dedicated to distinguishing between the types of attack.

Enhancing Security in IoT Devices with a Comprehensive Analysis of Lightweight Cryptographic Algorithms

A lot of gadgets connected to the Internet of Things (IoT) have changed many fields, from healthcare and smart houses to smart cities and commercial robotics. But this fast growth has also brought about big security problems, mostly because IoT devices don't have a lot of resources, which makes it hard to use standard cryptographic methods. The reason of this ponder is to grant a intensive examination of lightweight cryptographic strategies that can make IoT gadgets more secure without abating them down. Since IoT gadgets have uncommon security needs, lightweight cryptography has ended up one of the foremost critical ways to secure them. These methods are made to supply solid assurance whereas utilizing as few computer assets, power, and memory as conceivable. We see at well-known illustrations from each gather, like Display and Driven (square ciphers), Trivium and Grain (stream ciphers), and PHOTON and SPONGENT (hash capacities), to see how well they work in IoT settings. To donate a full picture of the contrasts, we carefully see at a few speed measures, such as running time, vitality utilize, memory utilization, and security against cryptographic dangers. The appraisal is based on both a common see at the subject and real-world tests on common IoT frameworks. We moreover conversation almost the trade-offs that come with choosing the correct cryptographic strategies to meet security needs whereas moreover taking under consideration the limits of IoT gadgets. Our inquire about appears that lightweight cryptographic calculations have a part of benefits, but the correct calculation must be chosen based on the IoT deployment's one of a kind utilize case and threat demonstrate. For example, applications that require a part of speed might advantage from lightweight stream ciphers, while applications that require to form beyond any doubt the security of the data might select lightweight hash capacities. The think about moreover talks approximately unused thoughts and bearings for lightweight cryptography for IoT, such as utilizing post-quantum cryptographic primitives to create IoT security future-proof. We see into the plausibility of blended strategies that use more than one lightweight procedure to progress security without utilizing as well numerous assets. Within the conclusion, this in-depth ponder appears how vital lightweight cryptographic strategies are for progressing IoT security. This paper aims to help researchers and practitioners set up good security measures for IoT devices by giving a thorough look at how well they work and what kinds of uses they are best for. This will make the IoT environment safer and more reliable.

Enhancing IoT device security in Kubernetes: An approach adopted for network policies and the SARIK framework

The Internet of Things (IoT) has ushered in an era of connected devices that, while facilitating real-time data collection and sharing, also exposes these devices to significant security risks. This study addresses the challenges of security risks and vulnerabilities by employing the Network Policy in Kubernetes and focusing on the SARIK framework. SARIK is designed to automate the creation and implementation of network policies, with the aim of enhancing the efficiency and strengthening the protection of IoT devices. Experiments conducted in a controlled environment with Minikube in Kubernetes showed that the implementation of SARIK notably improved the security of IoT devices. Key observations included a noticeable reduction in vulnerability to cyberattacks and a significant increase in the overall resilience of the system. In particular, the study revealed improvements in the performance metrics analyzed, which is evidence of SARIK’s effectiveness in real-world scenarios. Compared with existing frameworks - e.g., those of Sysdig -, SARIK is notable for its integration with Kubernetes network policies and its emphasis on automated security management. Although automation is a key factor in related works, SARIK’s unique approach to leveraging the inherent capabilities of Kubernetes offers a distinct advantage in ensuring the security of IoT environments. This aspect, along with its performance benefits, underlines the value of SARIK’s contribution to IoT security. The application of SARIK in protecting IoT devices in Kubernetes environments meets the need for automated and cohesive strategies to tackle current security threats. This study not only highlights the efficiency of SARIK but also emphasizes the need for evolving security strategies, that can be adapted to dynamic threat modeling in complex and interconnected IT environments.

IoT Forensics: Current Perspectives and Future Directions.

The Internet of Things forensics is a specialised field within digital forensics that focuses on the identification of security incidents, as well as the collection and analysis of evidence with the aim of preventing future attacks on IoT networks. IoT forensics differs from other digital forensic fields due to the unique characteristics of IoT devices, such as limited processing power and connectivity. Although numerous studies are available on IoT forensics, the field is rapidly evolving, and comprehensive surveys are needed to keep up with new developments, emerging threats, and evolving best practices. In this respect, this paper aims to review the state of the art in IoT forensics and discuss the challenges in current investigation techniques. A qualitative analysis of related reviews in the field of IoT forensics has been conducted, identifying key issues and assessing primary obstacles. Despite the variety of topics and approaches, common issues emerge. The majority of these issues are related to the collection and pre-processing of evidence because of the counter-analysis techniques and challenges associated with gathering data from devices and the cloud. Our analysis extends beyond technological problems; it further identifies the procedural problems with preparedness, reporting, and presentation as well as ethical issues. In particular, it provides insights into emerging threats and challenges in IoT forensics, increases awareness and understanding of the importance of IoT forensics in preventing cybercrimes, and ensures the security and privacy of IoT devices and networks. Our findings make a substantial contribution to the field of IoT forensics, as they not only involve a critical analysis of the challenges presented in existing works but also identify numerous problems. These insights will greatly assist researchers in identifying appropriate directions for their future research.

A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

The convergence of cybersecurity, Internet of Things (IoT), and data analytics: Safeguarding smart ecosystems

The Internet of Things (IoT) has profoundly impacted various industries by enabling enhanced connectivity and automation, thereby transforming our interactions with technology and our environment. However, this increased interconnectivity introduces substantial cybersecurity challenges, which threaten the integrity and security of IoT devices. This paper investigates the intersection of cybersecurity, IoT, and data analytics, providing an in-depth analysis of the vulnerabilities inherent in IoT devices and the innovative security solutions developed to address these issues through data-driven approaches. By leveraging advanced data analytics, we can bolster the security measures within IoT ecosystems, ensuring their resilience against cyber threats. Additionally, this study explores emerging trends and future directions in safeguarding smart ecosystems, offering valuable insights into how the integration of these domains can create a more secure and robust technological infrastructure for the future.

A Generalized Lightweight Intrusion Detection Model With Unified Feature Selection for Internet of Things Networks

ABSTRACTThe applicability of the Internet of Things (IoT) cutting across different domains has resulted in newer “things” acquiring IP connectivity. These things, technically known as IoT devices, are vulnerable to diverse security threats. Consequently, there has been an exponential increase in IoT malware over the past 5 years, and securing IoT devices from such attacks is a pressing concern in the current era. However, the traditional peripheral security measures do not comply with the lightweight security requirements of the IoT ecosystem. Considering this, we propose a lightweight intrusion detection model for IoT networks (LIDM‐IoT) that demonstrates similar efficiency in exposing malicious activities compared with the existing computationally expensive methods. The crux of the proposed model is that it provides efficient attack detection with lower computational requirements in IoT networks. LIDM‐IoT achieves the feat through a novel unified feature selection strategy that unifies filter‐based and embedded feature selection methods. The proposed feature selection strategy reduces the feature space by 94%. Also, we use only the records of a single attack type to build the model using the XGBoost algorithm. We have tested LIDM‐IoT with unseen attack types to ensure its generalized behavior. The results indicate that the proposed model exhibits efficient attack detection, with a reduced feature set, in IoT networks compared with the state‐of‐the‐art models.

Data-dependent half-select free GSRAM cell with word line write-assist and built-in read buffer schemes for use in PUFs-based IoT devices

In this study, a static-RAM cell using the Gnr-GDI method is proposed as a weak-type physical unclonable function (PUF) circuit to generate a unique and stable binary output for secure IoT devices. Regarding the memory level, a suitable combination of dynamic body bias, stacked networks, and multi-Vth techniques has been used in the architecture of asymmetric cell-structure inverters as a latch section to reduce power consumption and improve hardware efficiency. In addition, the logic styles of virtual ground and power gating based on word and BL data lines and a tri-state buffer structure have been used to extend the write VTC and improve read stability, respectively. From the perspective of PUF performance, the body of the latch section can form skewed VTCs based on the setting of critical parameters in GnrFET technology to achieve an efficient PUF circuit design.At the memory performance level, the Monte Carlo (MC) method-based results confirm the reasonable performance of the proposed structure in terms of static noise margin (SNM) and hardware efficiency, such as 53 % delay and 72 % energy-delay product (EDP) parameters, compared with the 6 T SRAM structure in a similar 16 nm GnrFET technology. In addition, in terms of the performance as a PUF circuit, the simulation results demonstrate the superiority of the proposed cell in terms of energy consumption, BER, response time, uniqueness, and stability under non-technological variation conditions of temperature and supply voltage. The outstanding performance results of the figure of merits (FoMs), CEQM, and UR2, which are composed of variability, energy, reliability, and layout-level factors, indicate the suitability of the proposed memory architecture for use in both the memory and PUF modes.Furthermore, to investigate the application level, memory structure has been used to store fingerprint images as PUF data using a hardware algorithm. The results of the proposed comprehensive FoM, which is based on the simultaneous consideration of circuit level and quality parameters, indicate that the proposed memory scheme in a bit-interleaved architecture-compatible design can be introduced as a high-performance candidate for generating and storing unique binary data in PUF-based IoT platforms.

Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview

Machine learning techniques are essential for processing the vast volume of IoT data efficiently, improving performance, and managing IoT applications effectively. Machine learning algorithms play a crucial role in detecting malicious attacks and anomalies in real-time IoT data analysis, thereby enhancing the security of IoT devices. The integration of big data analytics methods with machine learning techniques can further enhance IoT data analysis, improving the performance of IoT applications and overcoming related challenges. Real-time data collection using sensors like DHT11 and Gas level sensors, coupled with machine learning algorithms, enables efficient analysis of IoT data, aiding in the identification of anomalies and attacks. The comprehensive overview of enhancing IoT data analysis with machine learning provides insights for future research, including exploring advanced machine learning algorithms and optimizing data preprocessing techniques to enhance IoT data analysis capabilities.

Improved temporal IoT device identification using robust statistical features.

The Internet of Things (IoT) is becoming more prevalent in our daily lives. A recent industry report projected the global IoT market to be worth more than USD 4 trillion by 2032. To cope with the ever-increasing IoT devices in use, identifying and securing IoT devices has become highly crucial for network administrators. In that regard, network traffic classification offers a promising solution by precisely identifying IoT devices to enhance network visibility, allowing better network security. Currently, most IoT device identification solutions revolve around machine learning, outperforming prior solutions like port and behavioural-based. Although performant, these solutions often experience performance degradation over time due to statistical changes in the data. As a result, they require frequent retraining, which is computationally expensive. Therefore, this article aims to improve the model performance through a robust alternative feature set. The improved feature set leverages payload lengths to model the unique characteristics of IoT devices and remains stable over time. Besides that, this article utilizes the proposed feature set with Random Forest and OneVSRest to optimize the learning process, particularly concerning the easier addition of new IoT devices. On the other hand, this article introduces weekly dataset segmentation to ensure fair evaluation over different time frames. Evaluation on two datasets, a public dataset, IoT Traffic Traces, and a self-collected dataset, IoT-FSCIT, show that the proposed feature set maintained above 80% accuracy throughout all weeks on the IoT Traffic Traces dataset, outperforming selected benchmark studies while improving accuracy over time by +10.13% on the IoT-FSCIT dataset.