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The Internet of Things (IoT) has shown rapid growth and wide adoption in recent years. However, IoT devices are not designed to address modern security challenges. The weak security of these devices has been exploited by malicious actors and has led to several serious cyber-attacks. In this context, anomaly detection approaches are considered very effective owing to their ability to detect existing and novel attacks while requiring data only from normal execution. Because of the limited resources of IoT devices, conventional security solutions are not feasible. This emphasizes the need to develop new approaches that are specifically tailored to IoT devices. In this study, we propose a host-based anomaly detection approach that uses system call data and a Markov chain to represent normal behavior. This approach addresses the challenges that existing approaches face in this area, mainly the segmentation of the syscall trace into suitable smaller units and the use of a fixed threshold to differentiate between normal and malicious syscall sequences. Our proposed approach provides a mechanism for segmenting syscall traces into the program's execution paths and dynamically determines the threshold for anomaly detection. The proposed approach was evaluated against various attacks using two well-known public datasets provided by the University of New South Mexico (UNM) and one custom dataset (PiData) developed in the laboratory. We also compared the performance and characteristics of our proposed approach with those of recently published related work. The proposed approach has a very low false positive rate (0.86%), high accuracy (100%), and a high F1 score (100%) that is, a combined performance measure of precision and recall.

The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The proposed technique is compared and evaluated with state-of-the-art and is found to outperform them. It detects more attacks while maintaining the packet loss ratio in the range of 15-25%. The average energy consumption of the nodes also remains in the ratio of 60-80 mj. There is approximately 40% more energy conservation at node level with an overall 50% increase in network lifetime. THC-RPL has 10% less message exchange and 0% storage costs., Competing Interests: The authors have declared that no competing interests exist.

Recently, the Internet of Things (IoT) has emerged as an important way to connect diverse physical devices to the internet. The IoT paves the way for a slew of new cutting-edge applications. Despite the prospective benefits and many security solutions offered in the literature, the security of IoT networks remains a critical concern, considering the massive amount of data generated and transmitted. The resource-constrained, mobile, and heterogeneous nature of the IoT makes it increasingly challenging to preserve security in routing protocols, such as the routing protocol for low-power and lossy networks (RPL). RPL does not offer good protection against routing attacks, such as rank, Sybil, and sinkhole attacks. Therefore, to augment the security of RPL, this article proposes the energy-efficient multi-mobile agent-based trust framework for RPL (MMTM-RPL). The goal of MMTM-RPL is to mitigate internal attacks in IoT-based wireless sensor networks using fog layer capabilities. MMTM-RPL mitigates rank, Sybil, and sinkhole attacks while minimizing energy and message overheads by 25-30% due to the use of mobile agents and dynamic itineraries. MMTM-RPL enhances the security of RPL and improves network lifetime (by 25-30% or more) and the detection rate (by 10% or more) compared to state-of-the-art approaches, namely, DCTM-RPL, RBAM-IoT, RPL-MRC, and DSH-RPL.

This research aims to explore Internet of Things (IoT) applications in medical libraries of Pakistan. Employing an explanatory sequential mixed-methods approach, the study unfolds in two phases. Initially, quantitative data was gathered from 63 medical librarians throughout Pakistan. Subsequently, in the second phase, 10 medical librarians were purposefully selected for in-depth interviews. The findings reveal that medical libraries in Pakistan utilize a limited range of IoT-based appliances and services, including smart air conditioners, automatic fire alarms, intelligent hand sanitizer dispensers, automated notifications for circulation desk activities, and smart gates to track people. Pakistani medical librarians acknowledge several advantages of IoT applications, such as cost savings, remote access to library resources, and enhanced monitoring and security of library premises. Moreover, the study sheds light on challenges faced by medical libraries in Pakistan, encompassing the expenses associated with IoT applications, data security and privacy issues, the necessity for a highly integrated environment, and a shortage of skilled personnel. The study promises to be the first comprehensive survey of IoT applications in Pakistani medical libraries. The research findings will assist medical libraries and administrators in integrating IoT applications in medical libraries not just in Pakistan, but also in other developing nations. [ABSTRACT FROM AUTHOR]

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency. [ABSTRACT FROM AUTHOR]

Security and efficiency remain a serious concern for Internet of Things (IoT) environments due to the resource-constrained nature and wireless communication. Traditional schemes are based on the main mathematical operations, including pairing, pairing-based scalar multiplication, bilinear pairing, exponential operations, elliptic curve scalar multiplication, and point multiplication operations. These traditional operands are cost-intensive and require high computing power and bandwidth overload, thus affecting efficiency. Due to the cost-intensive nature and high resource requirements, traditional approaches are not feasible and are unsuitable for resource-limited IoT devices. Furthermore, the lack of essential security attributes in traditional schemes, such as unforgeability, public verifiability, non-repudiation, forward secrecy, and resistance to denial-of-service attacks, puts data security at high risk. To overcome these challenges, we have introduced a novel signcryption algorithm based on hyperelliptic curve divisor multiplication, which is much faster than other traditional mathematical operations. Hence, the proposed methodology is based on a hyperelliptic curve, due to which it has enhanced security with smaller key sizes that reduce computational complexity by 38.16% and communication complexity by 62.5%, providing a well-balanced solution by utilizing few resources while meeting the security and efficiency requirements of resource-constrained devices. The proposed strategy also involves formal security validation, which provides confidence for the proposed methodology in practical implementations. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server's aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively. [ABSTRACT FROM AUTHOR]

This article investigates a new autonomous mobile fog computing (MFC) system empowered by multiple unmanned aerial vehicles (UAVs) in order to serve medical Internet of Things devices (MIoTDs) efficiently. The aim of this article is to reduce the energy consumption of the UAVs-empowered MFC system by designing UAVs' trajectories. To construct the trajectories of UAVs, we need to consider not only the order of SPs but also the association among UAVs, SPs, and MIoTDs. The above-mentioned problem is very complicated and is difficult to be handled via applying traditional techniques, as it is NP-hard, nonlinear, non-convex, and mixed-integer. To handle this problem, we propose a novel simulated annealing trajectory optimization algorithm (SATOA), which handles the problem in three phases. First, the deployment (i.e., number and locations) of stop points (SPs) is updated and produced randomly using variable population sizes. Accordingly, MIoTDs are associated with SPs and extra SPs are removed. Finally, a novel simulated annealing algorithm is proposed to optimize UAVs' association with SPs as well as their trajectories. The performance of SATOA is demonstrated by performing various experiments on nine instances with 40 to 200 MIoTDs. The simulation results show that the proposed SATOA outperforms other compared state-of-the-art algorithms in terms of saving energy consumption. [ABSTRACT FROM AUTHOR]

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs. [ABSTRACT FROM AUTHOR]

The purpose of this article is to understand and elaborate on the impact of energy management through the use of solar energy systems in the industrial sector over the economic growth of developing countries. This study is based on Pakistan where the concept is in the infancy stage. Therefore, the significance of the study has many folds as the study will not only reflect the contemporary use of solar panels in the industrial sector but also reflect vividly upon the implications of the concept regarding the eastern and developing markets. The study collected data from employees of the energy sector to test the claim with a non-probability sampling technique and analysis has been made through incorporating SMART-PLS. The findings of the study reflect that there is a definite association between the use of solar panels in the production sector on energy efficiency as well as the sustainable economic growth of the country. Hence the study is also beneficial for fostering research work as well as to optimize the policy formulation for the use of solar energy systems in the production sector of developing countries of the world. [ABSTRACT FROM AUTHOR]

Devices belonging to the realm of edge Internet of Things (IoT) are becoming highly susceptible to intrusion attacks. The large-scale development in edge IoT, ease of availability, and affordability have drastically increased its usage in the real world. The business market revolves around producing better, innovative, and appealing products every day. However, security is often left unchecked to achieve these standards. Therefore, vulnerabilities present in these devices make them susceptible to various intrusion attacks. We devised a model named DF-IDS for detecting intrusions in IoT traffic. DF-IDS consists of two main phases: In the 1 st phase, it comparatively selects the best features from the feature matrix using SpiderMonkey (SM), principle component analysis (PCA), information gain (IG), and correlation attribute evaluation (CAE). In the 2 nd phase, these features along with assigned labels are used to train a deep neural network for intrusion detection. DF-IDS achieves an accuracy of 99.23% with an F1-score of 99.27%. It shows improvement not only in accuracy but also in F1 score as compared to the other comparative models and existing studies. [ABSTRACT FROM AUTHOR]

This study aims to investigate the applications of the Internet of Things (IoTs) and highlights the challenges faced to implement IoTs in Pakistani university libraries. This study opted exploratory sequential mixed-methods approach and was completed in two phases. In the first phase, purposefully selected university librarians were interviewed. In the second phase, quantitative data were collected from 122 university librarians across Pakistan. This study found that Pakistani university libraries implemented IoTs-based appliances including smart air conditioners, automatic fire alarms, smart hand sanitizer machines, and smart security doors. Moreover, the university libraries were using limited IoT-based services that are auto notification of check-out, check-in the reading materials, self-check-out and self-check-in system, recognition of user's card, and the use of Radio Frequency Identification (RFID) tags for security perspective. The major challenges to implementing IoTs applications were found lack of a highly networked and integrated environment, budget problems, unavailability of policy and strategic plan, and the lack of technical personnel. This novel study explains the extent of IoTs applications and the challenges to adopt and implement IoTs in Pakistani university libraries. The research findings would be useful for library and university administrators who are interested to implement IoTs applications in their libraries. [ABSTRACT FROM AUTHOR]

Smart homes, smart cities, smart grids, and smart vehicles are a few examples of the Internet of Things (IoT) applications. These applications possess distributed data delivery infrastructures for remote access, storage, distribution, and processing. As a result, the communication amongst such smart objects offers new horizons for applications to improve human well being. However, massive communication within such cyber-physical systems poses a wide variety of security challenges. These security malfunctions may disturb the entire applications/systems and lead to lethal consequences. Therefore, trust and security are major prerequisites in advanced IoT applications. To this end, there is a dire need to understand the key security challenges within smart IoT applications, such as e-health, agriculture, and energy sectors. Therefore, this paper provides and discusses major security challenges and requirements of smart IoT applications in order to enhance their security. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) fostered a new epoch of innovation by interconnecting digital devices to make human life more convenient and attractive. These smart objects are largely deployed as low power and lossy networks (LLNs) and use routing protocol for LLNs (RPL) for routing. Unfortunately, it is extremely vulnerable to a large variety of external and internal attacks to cause devastating and calamitous effects. However, this article's scope revolves around internal attacks only, where nodes are already part of a legitimate network. Various trust‐based mechanisms have been proposed to secure the RPL protocol from insider attackers. Existing trust mechanisms cause high energy depletion due to complex computation on the node level, which consequently decreases the performance of LLNs. Therefore, this article presents a novel hierarchical trust‐based mechanism "CTrust‐RPL" by assessing the trust of nodes based on their forwarding behaviors. This study ships complex trust‐related computations to the higher layer, known as the controller, to save computational, storage, and energy resources at the node level. We also compare the proposed mechanism with a state‐of‐the‐art technique called Sec‐trust. Our mechanism demonstrates superior performance in detecting and isolating blackhole attacks. The results depict that CTrust‐RPL detects and isolates 10% more malicious nodes than Sec‐trust in the same time‐lapse. The average packet loss ratio difference is less for our proposed mechanism, with 35% more energy efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has penetrated its roots in almost every domain of life. Smart healthcare is one of the major domains that extensively uses IoT infrastructures and solutions. IoT-based smart healthcare systems have immensely added value to the healthcare domain with the use of wearable and mobile devices. This leads to a substantial use of health data sharing for the improved, accurate, and timely diagnosis. However, smart healthcare systems are highly vulnerable to several security breaches and various malignant attacks, such as privacy leakage, tempering, forgery, etc. Recently, the blockchain technology emerged as a propitious solution against such breaches and challenges. This paper presents an up-to-date survey on different challenges and open issues faced in smart healthcare due to the traditional security measures along with the security requirements of such domains. It also amalgamates the potentials of blockchain technology as a promising security measure, highlights potential challenges in the healthcare domain, and provides an analysis of different blockchain-based security solutions. [ABSTRACT FROM AUTHOR]

Summary The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low‐latency and enhanced security for mission‐critical data. However, fog security and privacy for SCADA‐based IoT critical infrastructures remains an under‐researched area. To address this challenge, this contribution proposes a novel security "toolbox" to reinforce the integrity, security, and privacy of SCADA‐based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic‐based access approach to the cloud services using identity‐based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog‐based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real‐world scenario. These results can pave the way toward the development of a more secure and trusted SCADA‐based IoT critical infrastructure, which is essential to counter cyber threats against next‐generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog‐based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform. [ABSTRACT FROM AUTHOR]

The ubiquitous use of Internet-of-Things (IoT) is enabling a new era of wireless Sensor Nodes (SN s) that can be subject to attacks like any other piece of hardware and software. Unfortunately, an open and challenging issue is to what extent legitimate SN s can be trusted. This paper presents an energy-efficient, software-defined-network-based Mobile Code-driven Trust Mechanism (MCTM) for addressing this issue by assessing trust of SN s based on their forwarding behaviors. MCTM uses mobile code to visit the SN s based on pre-defined itineraries while collecting necessary details about these SN s in preparation for assessing their trust. The results gained from the experiments demonstrate a superior performance over a state-of-art technique that is energy-efficient management based on Software-Defined Network (SDN) for SN s. Message overhead is reduced by approximately 50%, which results in consuming less energy when detecting malicious SN s. • A new Mobile Code-driven Trust Mechanism (MCTM) for detecting internal attacks targeting IoT. • We resort to trust and mobile code to isolate malicious nodes and maintain sensitive data transfer, respectively. • The proposed work mainly focuses on malicious forwarding attacks. • The results obtained demonstrate a superior performance over a state-of-art technique. [ABSTRACT FROM AUTHOR]

• Emphasis on human activity recognition in smart cities. • Issues with the recognizing human activities accurately in society. • A novel activity recognition framework to improve accuracy. • Use of a large dataset for several extensive experimental analysis and results. According to the Sustainable Development Agenda 2030 of the World Health Organization, maintaining physical activities have multiple societal privileges for healthier cities and societies. The amalgamation of the Internet of Things (IoT) and pervasive smartphones has become of paramount importance to produce a significant breakthrough in various domains of smart cities, including healthcare, fitness, skill assessment, and personal assistants, to support independent living. The IoT-supported devices capacitate, embedded with sensors, enabled numerous context-aware applications to recognize physical activities. There are some activity recognition applications; however, they are still deficient in recognizing activities accurately. In this paper, a novel framework for human activity recognition (HAR) is proposed using raw readings from a combination of fused smartphone sensors: accelerometer, gyroscope, magnetometer , and Google Fit activity tracking module. The proposed framework applies deep recurrent neural network (DRNN) to an extensive training dataset. The latter consists of five activity classes from 12 individuals using a deep recurrent neural network (DRNN). An extensive training dataset is used consisting of five activity classes from a group of 12 individuals. The designed android application (runs in the background) collects data from the smartphone's embedded sensors fused with the Google Fit API to validate the results proposed framework. The proposed framework shows promising results in recognizing human activities compared to other similar studies and achieves an accuracy of 99.43% for activity recognition using DRNN. [ABSTRACT FROM AUTHOR]

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime. [ABSTRACT FROM AUTHOR]

• Emphasizing security challenges and requirements of IoT-based systems. • Examining ML as a security measure for the IoT domain and the challenges it poses on IoT. • Analyzing constraints of the prevailing ML security techniques proposed for IoT domain security. Internet of Things (IoT) is a pervasively-used technology for the last few years. IoT technologies are also responsible for intensifying various everyday smart applications improving the standard of living. However, the inter-crossing of IoT systems and the multi-directional elements responsible for these systems' placement have raised new safety concerns. They generate and share a massive amount of sensitive data. Unfortunately, both the data and the devices are susceptible to many privacy and security challenges. Much research has been done to secure these infrastructures; however, Machine Learning (ML), among others, provides higher accuracy. This survey covers the major security issues and open challenges encountered by IoT infrastructures. It also encompasses an in-depth study and analysis of ML-based state-of-the-art solutions used in securing such domains. The security challenges and requirements in IoT-based systems have been highlighted, along with a discussion on how ML supports security measures in the said domain. Furthermore, the challenges associated with ML-based security solutions have been identified concerning IoT. An analysis of prevailing ML security techniques' constraints is also contemplated. [ABSTRACT FROM AUTHOR]

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today's society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today's hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are deployed as Low power and Lossy Networks (LLN) to provide innovative services in various application domains such as smart cities, smart health, and smart communities. The LLN is a form of a network where the interconnected devices are highly resource-constrained (i.e., power, memory, and processing) and characterized by high loss rates, low data rates, and instability in the communication links. Additionally, IoT devices produce a massive amount of confidential and security-sensitive data. Various cryptographic-based techniques exist that can effectively cope with security attacks but are not suitable for IoT as they incur high consumption of resources (i.e., memory, storage and processing). One way to address this problem is by offloading the additional security-related operations to a more resourceful entity such as a fog-based node. Generally, fog computing enables security and analysis of latency-sensitive data directly at the network's edge. This paper proposes a novel Fog Security Service (FSS) to provide end-to-end security at the fog layer for IoT devices using two well-established cryptographic schemes, identity-based encryption, and identity-based signature. The FSS provides security services such as authentication, confidentiality, and non-repudiation. The proposed architecture would be implemented and evaluated in an OPNET simulator using a single network topology with different traffic loads. The FSS performed better when compared with the APaaS and the legacy method. [ABSTRACT FROM AUTHOR]