Deployment Of Internet Of Things Devices Research Articles

Security paradigms for IoT in telecom networks: Conceptual challenges and solution pathways

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: IoT, Telecom, Networks, Security.

Blockchain in IOT Security

Abstract: The Internet of Things (IoT) paradigm is rapidly transforming various industries by enabling the interconnection of billions of devices. However, the pervasive deployment of IoT devices also introduces significant security challenges, including data integrity, confidentiality, and device authentication. Blockchain technology, initially popularized by cryptocurrencies, has emerged as a promising solution to enhance the security of IoT ecosystems. This paper provides a comprehensive review of the integration of blockchain technology into IoT security frameworks. We explore various blockchain-based security mechanisms, including distributed ledger technology, smart contracts, consensus algorithms, and cryptographic techniques, and analyze their effectiveness in addressing IoT security concerns. Furthermore, we discuss the current state-of-the-art implementations, challenges, and future research directions for leveraging blockchain in IoT security.

SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: Security, IoT, Telecom, Solutions, Network.

Leveraging IoT for Enhanced National Security : A Comprehensive Review

In an era marked by rapid technological advancement and evolving security threats, the integration of Internet of Things (IoT) technologies has emerged as a pivotal strategy for bolstering national security efforts. The integration of Internet of Things (IoT) technologies into national security frameworks offers unprecedented opportunities for enhancing situational awareness, proactive threat detection, and effective response mechanisms. By deploying IoT devices such as sensors, cameras, and drones across various sectors including border control, critical infrastructure protection, and emergency management, governments can achieve real-time monitoring and data-driven decision-making capabilities. This paper gives a comprehensive review of the use of IoT technologies in national security, including real-time surveillance, border control and critical infrastructure protection. It examines the challenges and propose strategies, including the development of robust regulatory frameworks.

Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deployment of IoT devices has enabled the provision of constant environmental feedback, thereby facilitating the automated adaptation of associated systems. This has brought about a fundamental transformation in the way contemporary society functions. The security of emerging technologies such as IoT has become a significant challenge due to the added complexities, misconfigurations, and conflicts between modern and legacy systems. This challenge has a notable impact on the reliability and accessibility of existing infrastructure. Edge computing (EC) is a collaborative computing system that brings data processing and analysis closer to the edge of the network, where the data is generated, rather than in a centralized cloud environment. The utilization of the IoT has become more prevalent in both everyday life and the manufacturing sector, with a particular emphasis on critical infrastructure. The IoT is presently being utilized across diverse domains, including but not limited to industrial, agricultural, healthcare, and logistical sectors. The security of IoT networks has implications for the safety of individuals, the security of the nation, and economic development. Notwithstanding, conventional intrusion detection techniques that rely on centralized cloud-based systems that have been suggested in previous studies for IoT network security are insufficient to meet the requirements for data confidentiality, network capacity, and prompt responsiveness. In addition, the integration of IoT applications into smart devices has been shown to augment their functionalities. However, it is important to note that this integration also brings about potential security vulnerabilities. Furthermore, a significant number of contemporary IoT devices exhibit restricted security capabilities, rendering them vulnerable to intricate attacks and impeding the extensive integration of IoT technologies. Also, a lot of IoT network devices have been put in place that don't have hardware security measures. This means that traditional intrusion detection systems (IDS) aren't enough to protect the IoT network ecosystem. To address these issues, this research suggests the IoT-Defender framework, which combines a Modified Genetic Algorithm (MGA) model with a deep Long Short-Term Memory (LSTM) network to find cyberattacks in IoT networks. This research represents a pioneering attempt to employ the MGA for feature selection and the GA for fine-tuning the LSTM parameters within an EC framework. The parameters of the LSTM model were fine-tuned through the manipulation of the number of hidden layers, utilizing the GA fitness function. The customization of the MGA aimed to enhance its performance in selecting relevant features, optimizing the use of limited resources on IoT devices and edge nodes. The fine-tuning process involved optimizing hyperparameters, architecture, and training strategies to maximize the LSTM network's effectiveness in learning and detecting patterns in IoT network traffic. The synergy between the MGA and LSTM aimed at creating a comprehensive and efficient IDS. The feature selection by the MGA contributes to improving the LSTM’s performance by providing it with more relevant and discriminating features. In order to solve the issue of class imbalance, we utilize the focal loss function, which provides greater weights to minority classes, hence improving the model’s capacity to learn from those particular classes. The performance of the IoT-Defender model was assessed on the BoT-IoT, UNSW-NB15, and N-BaIoT datasets utilizing a Raspberry Pi IoT device. The results of our study show that the IoT-Defender model works better than other methods. This is shown by its accuracy score of 99.41%, detection rate of 99.78%, precision score of 98.50%, false alarm rate of 2.56%, mean intersection over union (mIoU) of 0.68, and training time of 81.3 seconds on BoT-IoT. The proposed IoT model is designed to be lightweight and can be installed on edge servers to detect cyber-attacks in real-time, specifically in the context of IoT security.

IntelliCare: Integrating IoT and Machine Learning for Remote Patient Monitoring in Healthcare: A Comprehensive Framework

The development of smart health monitoring systems has emerged as a consequence of the integration of Internet of Things (IoT) and Machine Learning (ML) technologies within the healthcare sector. This transformation has significantly reshaped patient care methodologies, shifting from traditional approaches to electronic healthcare systems. Leveraging IoT technology fosters a contemporary medical device ecosystem, fostering seamless communication among healthcare professionals, patients, and medical devices. Through the deployment of IoT devices, encompassing sensors and transmitters, coupled with Machine Learning algorithms, various applications have arisen, spanning from remote patient monitoring to real-time health assessment during ambulance transit to medical facilities. This proposed framework aims to monitor essential physiological parameters including body temperature, blood pressure, heart rate, sweat analysis, glucose levels, ECG, EEG, and pulse oximetry, transmitting pertinent data for tailored processing and analysis. Implantable IoT devices serve as conduits for wireless communication, data storage, centralized computation, and portable processing, facilitating connectivity among sensors, GPS-enabled ambulances, medical practitioners, and patients. To mitigate potential health risks, sensors are equipped with Machine Learning capabilities to promptly assess illness severity and recommend appropriate interventions, potentially triggering automated alerts to healthcare providers. This seamless exchange of information via IoT and wireless networks enables rapid communication between doctors and patients, facilitating personalized medical recommendations, prescription management, and hospital selection based on individual health profiles.

Water quality assessment monitoring system using fuzzy logic and the internet of things

Water utilization has recently been at its highest level of demand. The water needed to be clean, healthy, and determined to be suitable for consumption. Therefore, it is necessary to have a system that can monitor the water quality so thatinformation related to wate r suitability can be received regularly and in real-time. This paper addresses the critical need for real-time water quality monitoring systems. This study proposed a novel approach integrating the Tsukamoto fuzzy algorithm into an internet of things (IoT)-based framework, forming part of the Fuzzy Inference System. Our system serves as a decision support tool, enabling continuous assessment of water quality. The method categorizes water quality into three levels: good, moderate, and unhealthy, providing timely and precise suitability information. The results demonstrate the effectiveness of the fuzzy logic method in delivering accurate output. Through remotely deployed IoT devices, water suitability and status can be monitored and analyzed in real-time over the internet. This research bridges the gap between traditional water quality assessment methods and the demands of our modern, technology-driven society, ensuring a reliable supply of safe and consumable water.

Using IoT in Double Skin Facades toward Thermal Comfort: A Review

Integrating the Internet of Things (IoT) into building design and operation has ushered in a new era of sustainable architecture and enhanced occupant comfort. This paper presents comprehensive case studies focused on the application of IoT technology in the context of double-skin facades, explicitly focusing on achieving and optimizing thermal comfort. These case studies, located in an urban setting, represent a paradigm shift in architectural design principles. Double skin facades, renowned for their potential to enhance energy efficiency and occupant well-being, are further empowered through the intelligent deployment of IoT devices. This synergy between architectural innovation and digital technology is instrumental in addressing the pressing challenges of energy consumption and occupant comfort in urbanized areas. It delves into the sensors, actuators, and data analytics systems employed to monitor and control the indoor environment. Special attention is given to the dynamic adaptation of the facade system in response to real-time environmental conditions, occupancy patterns, and user preferences. One of the case studies—PH01-BRK, projects the practical application of these principles in South Dakota’s first custom passive and net-zero home, completed in 2016, using advanced thermal analysis tools, software for heating and cooling requirements, and solar data assessment. Furthermore, the paper investigates the impact of IoT-enabled double-skin facades on thermal comfort, energy efficiency, and indoor air quality within the Brookings Passive House. Quantitative data and occupant feedback are analyzed to assess the effectiveness of the IoT-driven system in maintaining optimal thermal conditions while minimizing energy consumption. The findings of this case study underscore the potential of IoT technology in revolutionizing the design and operation of sustainable buildings. It demonstrates that integrating IoT into double-skin facades can significantly enhance thermal comfort, reduce energy consumption, and create healthier indoor environments.

IoT Integration in Telemedicine: Investigating the Role of Internet of Things Devices in Facilitating Remote Patient Monitoring and Data Transmission

Introduction: The integration of Internet of Things (IoT) technology with telemedicine has ushered in a groundbreaking era for healthcare, allowing for remote monitoring and data transmission of patients. This research paper aims to explore the complex relationship between IoT devices and telemedicine, examining their combined potential to improve patient care and transform healthcare workflows. Method: To gain a deeper understanding of the impact of IoT-powered telemedicine, a qualitative study was conducted to explore the experiences and perspectives of both healthcare practitioners and patients. Using in-depth interviews and content analysis, we examined the integration of IoT devices, such as wearables and remote sensors, into telemedicine platforms. This allowed us to uncover real-time health data collection and transmission from patients' homes to medical professionals. Result: This study's analysis of the benefits, challenges, and user satisfaction indicates that IoT-powered remote patient monitoring significantly enhances patient care by facilitating timely interventions and reducing hospital admissions, ultimately leading to improved healthcare outcomes and highlighting the effectiveness of IoT-powered remote patient monitoring in relation to healthcare outcomes. Furthermore, we analyze the complex technical factors that influence the effective deployment of IoT devices in telehealth settings. The integration of telemedicine with IoT technology is a complex process that requires careful consideration of several factors, including data security, interoperability, and data analytics. These factors play a crucial role in ensuring the effective and secure exchange of health information between different systems. The insights gained from this research can help to inform the development of best practices for telemedicine, ultimately improving patient care and outcomes. The findings also have implications for healthcare providers, technology developers, and policymakers who seek to leverage IoTintegrated telehealth solutions in their work.

N-Accesses: A Blockchain-Based Access Control Framework for Secure IoT Data Management.

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.

Holistic Trash Collection System Integrating Human Collaboration with Technology

Effective waste management is of paramount importance as it contributes significantly to environmental preservation, mitigates health hazards, and aids in the preservation of precious resources. Conversely, mishandling waste not only presents severe environmental risks but can also disrupt the balance of ecosystems and pose threats to biodiversity. The emission of carbon dioxide, methane, and greenhouse gases (GHGs) can constitute a significant factor in the progression of global warming and climate change, consequently giving rise to atmospheric pollution. This pollution, in turn, has the potential to exacerbate respiratory ailments, elevate the likelihood of cardiovascular disorders, and negatively impact overall public health. Hence, efficient management of trash is extremely crucial in any society. It requires integrating technology and innovative solutions, which can help eradicate this global issue. The internet of things (IoT) is a revolutionary communication paradigm with significant contributions to remote monitoring and control. IoT-based trash management aids remote garbage level monitoring but entails drawbacks like high installation and maintenance costs, increased electronic waste production (53 million metric tons in 2013), and substantial energy consumption for always-vigilant IoT devices. Our research endeavors to formulate a comprehensive model for an efficient and cost-effective waste collection system. It emphasizes the need for global commitment by policymakers, stakeholders, and civil society, working together to achieve a common goal. In order to mitigate the depletion of manpower, fuel resources, and time, our proposed method leverages quick response (QR) codes to enable the remote monitoring of waste bin capacity across diverse city locations. We propose to minimize the deployment of IoT devices, utilizing them only when absolutely necessary and thereby allocating their use exclusively to central garbage collection facilities. Our solution places the onus of monitoring garbage levels at the community level firmly on the shoulders of civilians, demonstrating that a critical aspect of any technology is its ability to interact and collaborate with humans. Within our framework, citizens will employ our proposed mobile application to scan QR codes affixed to waste bins, select the relevant garbage level, and transmit this data to the waste collection teams’ database. Subsequently, these teams will plan for optimized garbage collection procedures, considering parameters such as garbage volume and the most efficient collection routes aimed at minimizing both time and fuel consumption.

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment.

In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.

Multi-UAV Path Learning for Age and Power Optimization in IoT With UAV Battery Recharge

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

Secure and Efficient Smart Healthcare System Based on Federated Learning

The rapid development of smart healthcare system in the Internet of Things (IoT) has made the early detection of many chronic diseases more convenient, quick, and economical. However, when healthcare organizations collect users’ health data through deployed IoT devices, there are issues of compromising users’ privacy. In view of this situation, this paper introduces federated learning technology to solve the problem of data security. In this paper, we consider the two main problems of federated learning applications in IoT smart healthcare system: (1) how to reduce the time overhead of system running and (2) how to authenticate that the user device uploading data is deployed by the system itself. To solve the above problems, we propose the first federated learning scheme based on full dynamic secret sharing. First, we use a two-mask protocol to keep the user’s local model parameters confidential during federated learning. Then, based on homogeneous linear recursive equation, homomorphic hash function, and elliptic curve cryptosystem, the full dynamic secret sharing and user identity authentication are realized. In addition, our scheme allows users to join or quit during training. Finally, we have carried out simulation test on this scheme. The experimental results show that the efficiency of our scheme is improved by about 60% on average in the case of no user dropping and by about 30% in the case of some users dropping.

A Survey of Symbiotic Radio: Methodologies, Applications, and Future Directions.

The sixth generation (6G) wireless technology aims to achieve global connectivity with environmentally sustainable networks to improve the overall quality of life. The driving force behind these networks is the rapid evolution of the Internet of Things (IoT), which has led to a proliferation of wireless applications across various domains through the massive deployment of IoT devices. The major challenge is to support these devices with limited radio spectrum and energy-efficient communication. Symbiotic radio (SRad) technology is a promising solution that enables cooperative resource-sharing among radio systems through symbiotic relationships. By fostering mutualistic and competitive resource sharing, SRad technology enables the achievement of both common and individual objectives among the different systems. It is a cutting-edge approach that allows for the creation of new paradigms and efficient resource sharing and management. In this article, we present a detailed survey of SRad with the goal of offering valuable insights for future research and applications. To achieve this, we delve into the fundamental concepts of SRad technology, including radio symbiosis and its symbiotic relationships for coexistence and resource sharing among radio systems. We then review the state-of-the-art methodologies in-depth and introduce potential applications. Finally, we identify and discuss the open challenges and future research directions in this field.

Detecting Vulnerability on IoT Device Firmware: A Survey

Internet of things (IoT) devices make up 30% of all network-connected endpoints, introducing vulnerabilities and novel attacks that make many companies as primary targets for cybercriminals. To address this increasing threat surface, every organization deploying IoT devices needs to consider security risks to ensure those devices are secure and trusted. Among all the solutions for security risks, firmware security analysis is essential to fix software bugs, patch vulnerabilities, or add new security features to protect users of those vulnerable devices. However, firmware security analysis has never been an easy job due to the diversity of the execution environment and the close source of firmware. These two distinct features complicate the operations to unpack firmware samples for detailed analysis. They also make it difficult to create visual environments to emulate the running of device firmware. Although researchers have developed many novel methods to overcome various challenges in the past decade, critical barriers impede firmware security analysis in practice. Therefore, this survey is motivated to systematically review and analyze the research challenges and their solutions, considering both breadth and depth. Specifically, based on the analysis perspectives, various methods that perform security analysis on IoT devices are introduced and classified into four categories. The challenges in each category are discussed in detail, and potential solutions are proposed subsequently. We then discuss the flaws of these solutions and provide future directions for this research field. This survey can be utilized by a broad range of readers, including software developers, cyber security researchers, and software security engineers, to better understand firmware security analysis.

A novel hybrid authentication protocol utilizing lattice-based cryptography for IoT devices in fog networks

The Internet of Things (IoT) has taken the interconnected world by storm. Due to their immense applicability, IoT devices are being scaled at exponential proportions worldwide. But, very little focus has been given to securing such devices. As these devices are constrained in numerous aspects, it leaves network designers and administrators with no choice but to deploy them with minimal or no security at all. We have seen distributed denial-of-service attacks being raised using such devices during the infamous Mirai botnet attack in 2016. Therefore we propose a lightweight authentication protocol to provide proper access to such devices. We have considered several aspects while designing our authentication protocol, such as scalability, movement, user registration, device registration, etc. To define the architecture we used a three-layered model consisting of cloud, fog, and edge devices. We have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and usage. We also provide a fail-safe mechanism for a situation where an authenticating server might fail, and the deployed IoT devices can self-organize to keep providing services with no human intervention. We find that our protocol works the fastest when using ring learning with errors. We prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications tool. In conclusion, we propose a safe, hybrid, and fast authentication protocol for authenticating IoT devices in a fog computing environment.

Hybrid intelligent intrusion detection system for internet of things

The Internet of Things (IoT) has become one of the dynamic and fascinating fields of research with new technology emerging. This can be explained by the ever-rising development of data globally (due to mass deployment of IoT devices) which needs to be protected and secure to avoid misuse. The emergence of IoT and learning algorithms in an area Machine Learning (ML) and Deep Learning (DL) has given new scope to cyber security. The security attacks on IoT networks are possible to detect and prevent intelligently using ML and DL techniques. ML and DL can make traditional attack detection methods efficient, reliable, and robust. The aim of this article is to develop the novel hybrid intrusion (attack) detection model using DL techniques, Convolutional neural network (CNN) and Long short-term memory (LSTM) to achieve better attack detection accuracy. The model is trained and tested using two different datasets namely UNSW-NB1511https://research.unsw.edu.au/projects/unsw-nb15-dataset. and NSL-Botnet22https://research.unsw.edu.au/projects/bot-iot-dataset. to verify the adaptability of the model to different datasets. The developed model has a better detection rate and low false positive rate (FPR) when compared to a state-of-the-art work. The proposed model accuracy is 99.4% with NSL-Botnet dataset and 93% with UNSW-NB15 for binary classification and 92% with NSL-Botnet and 82% with UNSW-NB15.

Revisiting the internet of things: New trends, opportunities and grand challenges

The Internet of Things (IoT) has brought the dream of ubiquitous data access from physical environments into reality. IoT embeds sensors and actuators in physical objects so that they can communicate and exchange data between themselves to improve efficiency along with enabling real-time intelligent services and offering better quality of life to people. The number of deployed IoT devices has rapidly grown in the past five years in a way that makes IoT the most disruptive technology in recent history. In this paper, we reevaluate the position of IoT in our life and provide deep insights on its enabling technologies, applications, rising trends and grand challenges. The paper also highlights the role of artificial intelligence to make IoT the top transformative technology that has been ever developed in human history.

A flexible Compilation-as-a-Service and Remote-Programming-as-a-Service platform for IoT devices

The Internet-of-Things (IoT) presents itself as an emerging technology, which is able to interconnect a massive number of heterogeneous smart objects. Several complex data-driven applications, such as smart cities applications, home automation, health monitoring, etc., have been realized through the existence of these ubiquitous networks of smart objects. The ability to remotely update the devices forming an IoT network is of paramount importance, as it enables adding new functionality in their firmware, either for resolving software bugs and security vulnerabilities or for application re-purposing, without the need to physically access them. In this work, we present a flexible Compilation-as-a-Service and Remote-Programming-as-a-Service platform that jointly offers cloud-based compilation and Firmware-Over-The-Air (FOTA) update functionalities for deployed IoT devices, in a reliable and secure manner. Our system is capable of easily supporting various embedded operating systems and heterogeneous hardware platforms. We describe the system architecture and elaborate on the implementation details of all system components. In addition, we perform an extensive performance evaluation of a Proof-of-Concept (PoC) deployment of our system and discuss results in terms of system response, scalability and resource utilization.