Markus Miettinen, Samuel Marchal, I. Hafeez
et al.
With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IoT Sentinel is effective in identifying device types and has minimal performance overhead.
The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. This paper gives a near complete and up-to-date view of the IoT authentication field. It provides a summary of a large range of authentication protocols proposed in the literature. Using a multi-criteria classification previously introduced in our work, it compares and evaluates the proposed authentication protocols, showing their strengths and weaknesses, which constitutes a fundamental first step for researchers and developers addressing this domain.
Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.
The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.
Thangaramya Kalidoss, K. Kulothungan, Logambigai Rajasekaran
et al.
Abstract Wireless Sensor Networks (WSNs) are used in the design of Internet of Things (IoT) for sensing the environment, collecting the data and to send them to the base station and the locations used for analysis. In WSNs for IoT, intelligent routing is an important phenomena that is necessary to enhance the Quality of Service (QoS) in the network. Moreover, the energy required for communication in the IoT based sensor networks is an important challenge to avoid immense packet loss or packet drop, fast energy depletion and unfairness across the network leading to reduction in node performance and increase in delay with respect to packet delivery. Hence, there is an extreme need to check energy usage by the nodes in order to enhance the overall network performance through the application of intelligent machine learning techniques for making effective routing decisions. Many approaches are already available in the literature on energy efficient routing for WSNs. However, they must be enhanced to suite the WSN in IoT environment. Therefore, a new Neuro-Fuzzy Rule Based Cluster Formation and Routing Protocol for performing efficient routing in IoT based WSNs. From the experiments conducted in this research work using the proposed model, it is proved that the proposed routing algorithm provided better network performance in terms of the metrics namely energy utilization, packet delivery ratio, delay and network lifetime.
In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.
Device authentication is an essential security feature for Internet of Things (IoT). Many IoT devices are deployed in the open and public places, which makes them vulnerable to physical and cloning attacks. Therefore, any authentication protocol designed for IoT devices should be robust even in cases when an IoT device is captured by an adversary. Moreover, many of the IoT devices have limited storage and computational capabilities. Hence, it is desirable that the security solutions for IoT devices should be computationally efficient. To address all these requirements, in this paper, we present a lightweight and privacy-preserving two-factor authentication scheme for IoT devices, where physically uncloneable functions have been considered as one of the authentication factors. Security and performance analysis show that our proposed scheme is not only robust against several attacks, but also very efficient in terms of computational efficiently.
The cloud-based Internet of Things (IoT) develops rapidly but suffer from large latency and backhaul bandwidth requirement, the technology of fog computing and caching has emerged as a promising paradigm for IoT to provide proximity services, and thus reduce service latency and save backhaul bandwidth. However, the performance of the fog-enabled IoT depends on the intelligent and efficient management of various network resources, and consequently the synergy of caching, computing, and communications becomes the big challenge. This paper simultaneously tackles the issues of content caching strategy, computation offloading policy, and radio resource allocation, and propose a joint optimization solution for the fog-enabled IoT. Since wireless signals and service requests have stochastic properties, we use the actor–critic reinforcement learning framework to solve the joint decision-making problem with the objective of minimizing the average end-to-end delay. The deep neural network (DNN) is employed as the function approximator to estimate the value functions in the critic part due to the extremely large state and action space in our problem. The actor part uses another DNN to represent a parameterized stochastic policy and improves the policy with the help of the critic. Furthermore, the Natural policy gradient method is used to avoid converging to the local maximum. Using the numerical simulations, we demonstrate the learning capacity of the proposed algorithm and analyze the end-to-end service latency.
Low-power wide area networks (LPWANs) constitute a type of networks which is used to connect things to the Internet from a wide variety of sectors. These types of technologies provide the Internet of Things (IoT) devices with the ability to transmit few bytes of data for long ranges, taking into consideration minimum power consumption. In parallel, IoT applications will cover a wide range of human and life needs from smart environments (cities, home, transportation, etc.) to health and quality of life. Among these popular LPWANs technologies, we have identified the unlicensed frequency band (LoRa, DASH7, SigFox, Wi-SUN, etc.), and the licensed frequency band standards (NB-IoT, LTE Cat-M, EC-GSM-IoT, etc.). In general, both types of standards only consider fixed interconnected things, and less attention has been provided to the mobility of the things or devices. In this paper, we address the mobility of the things and the connectivity in each of the three LPWAN standards: LoRaWAN, DASH7, and NB-IoT. In particular, we show how the mobility of things can be achieved when transmitting and receiving data. Then, we provide a general and technical comparison for the three standards. Finally, we illustrate several application scenarios where the mobility is required, and we show how to select the most suited standard. We also discuss the research challenges and perspectives.
—Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has changed the way we live, play, and work. To date, the traditional approach to securing IoT has treated devices individually. However, in practice, it has been recently shown that the interactions among devices are often the real cause of safety and security violations. In this paper, we present I O TG UARD , a dynamic, policy-based enforcement system for IoT, which protects users from unsafe and insecure device states by monitoring the behavior of IoT and trigger-action platform apps. I O TG UARD operates in three phases: (a) implementation of a code instrumentor that adds extra logic to an app’s source code to collect app’s information at runtime, (b) storing the apps’ information in a dynamic model that represents the runtime execution behavior of apps, and (c) identifying IoT safety and security policies, and enforcing relevant policies on the dynamic model of individual apps or sets of interacting apps. We demonstrate I O TG UARD on 20 flawed apps and find that I O TG UARD correctly enforces 12 of the 12 policy violations. In addition, we evaluate I O TG UARD on 35 SmartThings IoT and 30 IFTTT trigger-action platform market apps executed in a simulated smart home. I O TG UARD enforces 11 unique policies and blocks 16 states in six (17.1%) SmartThings and five (16.6%) IFTTT apps. I O TG UARD imposes only 17.3% runtime overhead on an app and 19.8% for five interacting apps. Through this effort, we introduce a rigorously grounded system for enforcing correct operation of IoT devices through systematically identified IoT policies, demonstrating the effectiveness and value of monitoring IoT apps with tools such as I O TG UARD .
Quoc-Dung Ngo, Huy-Trung Nguyen, Van-Hoang Le
et al.
Abstract Due to a lack of security design as well as the specific characteristics of IoT devices such as the heterogeneity of processor architecture, IoT malware detection has to deal with very unique challenges, especially on detecting cross-architecture IoT malware. Therefore, the IoT malware detection domain is the focus of research by the security community in recent years. There are many studies taking advantage of well-known dynamic or static analysis for detecting IoT malware; however, static-based methods are more effective when addressing the multi-architecture issue. In this paper, we give a thorough survey of static IoT malware detection. We first introduce the definition, evolution and security threats of IoT malware. Then, we summarize, compare and analyze existing IoT malware detection methods proposed in recent years. Finally, we carry out exactly the methods of existing studies based on the same IoT malware dataset and an experimental configuration to evaluate objectively and increasing the reliability of these studies in detecting IoT malware.
Migrating to Post-Quantum Cryptography (PQC) is critical for securing resource-constrained Internet of Things (IoT) devices against the “harvest-now, decrypt-later” threat. While ML-KEM (CRYSTALS-Kyber) has been standardized under FIPS 203 for general encryption, these devices often operate on unreliable networks suffering from high latency and packet loss. Our recent systematic review identified a critical gap that existing research overwhelmingly focuses on Transport Layer Security (TLS). This leaves the resilience of lightweight protocols like MQTT and CoAP under challenging network conditions largely unexplored. This paper introduces PQC-IoTNet, a novel Software-in-the-Loop (SITL) framework to address this gap. Our three-tier architecture integrates a Python-based IoT client with kernel-level emulation to test the full protocol stack. Validation results comparing Kyber and ECC demonstrate the framework’s ability to capture critical performance cliffs caused by TCP retransmissions. Notably, the framework revealed that while Kyber maintained an 18% speed advantage over ECC at 5% packet loss, both protocols experienced nonlinear latency spikes. This work provides a reproducible blueprint to identify operational boundaries and select resilient protocols for secure IoT systems.
A smart city is an urbanization region that collects data using several digital and physical devices. The information collected from such devices is used efficiently to manage revenues, resources, and assets, etc., while the information obtained from such devices is utilized to boost performance throughout the city. Cloud-based Internet of Things (IoT) applications could help smart cities that contain information gathered from citizens, devices, homes, and other things. This information is processed and analyzed to monitor and manage transportation networks, electric utilities, resources management, water supply systems, waste management, crime detection, security mechanisms, proficiency, digital library, healthcare facilities, and other opportunities. A cloud service provider offers public cloud services that can update the IoT environment, enabling third-party activities to embed IoT data within electronic devices executing on the IoT. In this paper, the author explored cloud-based IoT applications and their roles in smart cities.
Assessing the security of IoT-based smart environments such as smart homes and smart cities is becoming fundamentally essential to implementing the correct control measures and effectively reducing security threats and risks brought about by deploying IoT-based smart technologies. The problem, however, is in finding security standards and assessment frameworks that best meets the security requirements as well as comprehensively assesses and exposes the security posture of IoT-based smart environments. To explore this gap, this paper presents a review of existing security standards and assessment frameworks which also includes several NIST special publications on security techniques highlighting their primary areas of focus to uncover those that can potentially address some of the security needs of IoT-based smart environments. Cumulatively a total of 80 ISO/IEC security standards, 32 ETSI standards and 37 different conventional security assessment frameworks which included 7 NIST special publications on security techniques were reviewed. To present an all-inclusive and up-to-date state-of-the-art research, the review process considered both published security standards and assessment frameworks as well as those under development. The findings show that most of the conventional security standards and assessment frameworks do not directly address the security needs of IoT-based smart environments but have the potential to be adapted into IoT-based smart environments. With this insight into the state-of-the-art research on security standards and assessment frameworks, this study helps advance the IoT field by opening new research directions as well as opportunities for developing new security standards and assessment frameworks that will address future IoT-based smart environments security concerns. This paper also discusses open problems and challenges related to IoT-based smart environments security issues. As a new contribution, a taxonomy of challenges for IoT-based smart environment security concerns drawn from the extensive literature examined during this study is proposed in this paper which also maps the identified challenges to potential proposed solutions.
Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people’s daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain’s proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.