Federated Detection at the Edge: Collaborative Anomaly Detection for Resource-Limited IoT

The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

This chapter highlights the growing urgency for robust Intrusion Detection Systems (IDS) in the Internet of Things (IoT) security, focusing on sustainable cybersecurity strategies within the modern, highly interconnected digital ecosystem. As the rapid expansion of IoT devices presents vast attack surfaces, it is crucial to develop IDS that protect interconnected environments. This chapter performs a thorough analysis of IDS detection mechanisms and IoT security datasets, critically assessing the relevance and reliability. The evaluation emphasizes essential dataset attributes, including source authenticity, diversity, data volume, and real-world applicability. By systematically analyzing these criteria, the paper identifies datasets that offer comprehensive insights into IoT security's challenges, offering researchers a detailed resource for selecting datasets that align with the complexity of current IDS development. This review thus serves as a foundational guide for advancing IDS technology, aiming to foster more resilient and adaptive security solutions within the IoT landscape.

Internet of Things (IoT) has emerged as a new paradigm for integrating internet resources and physical objects. It provides a better standard of living in different domains, like industrial processes, home automation, and environmental monitoring. The growth of IoT depends on the need to connect more devices via the Internet. However, anywhere internet connectivity is involved, security poses as an enormous challenge. Intrusion Detection Systems (IDS) can protect IoTs by applying rules related to IoTs operation. This paper reviews some of the mechanisms of IoT-related IDS, which protect IoT devices against various attacks. The paper includes a summary of the recent developments of IDS against many security threats. A review is presented regarding various IDS designs developed in the last decade with different methods, ideas, and approaches toward a better understanding of suitable IDS platforms that provide security against the global growth of attacks and intruders. It also involves the examination of the IDS basics, types, and components of the previously proposed systems, as well as discussing the pros and disadvantages of each. We organize the taxonomy of investigated IDS approaches using the detection approaches. This work aims to provide a thorough summary of the existing IDS designs and issues to empower research and development for IDS about IoTs.

The Internet of Things (IoT) is a fast-expanding network of smart heterogeneous objects. It refers to the physical devices that are capable of communicating with other physical devices. Unlike the wireless sensor networks (WSNs), IoT is connected to worldwide Internet that exposes it to global intrusion in addition to wireless attacks inside an IoT network. It is protected by cryptographic and network security techniques, but they are vulnerable to internal and external attacks. The IoT devices are resource constrained in terms of limited storage, battery, limited transmission range and processing. To protect these tiny devices from the inside and outside cyberattacks, we need a lightweight security system that can run efficiently and effectively on these IoT devices. We also need a system, which can identify the type of intrusion and trigger an alarm in intrusion scenario to take appropriate preventive measure. Hence, an Intrusion Detection System (IDS) plays an important role to prevent such cyberattacks in IoT. These devices can be static or mobile in an IoT environment; this must be considered while designing IDS for IoT system. This book chapter presents various IDSs for an IoT system and their comparisons in terms of detection rate, false positive and accuracy in both static and mobility of devices.

The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to address evolving attack strategies. This study investigates the vulnerabilities of IoT networks and the pressing need to detect and classify DoS/DDoS attacks in real time. Traditional IDS, while effective in recognizing known attack patterns, fall short in identifying new attack types due to their reliance on historical data. To bridge this gap, this research focuses on developing an enhanced hybrid network intrusion detection and classification system. This study seeks to make contribution to the advancement of resilient security measures within IoT environments. The framework uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks to efficiently detect and categorize DoS/DDoS attacks in real time. To achieve this, a simulation model implementing the proposed hybrid algorithm will be developed. The framework utilized the MQTT-IoT-IDS2020 dataset, and a comparison was presented with recent approaches found in the literature. The results demonstrated enhanced performance in terms of true positive rate, accuracy, speed of detection.

Recently, the internet of things (IoT) has evolved into a breakthrough for creating intelligent settings. Any technology's reliance on the IoT model is seen as having major security and privacy issues. The various conceivable attacks carried out by intruders give rise to privacy and security considerations. Therefore, creating an intrusion detection system is crucial for identifying attacks and anomalies in the IoT system. In this work, a deep belief network (DBN) algorithm model for the intrusion detection system has been proposed. The CICIDS 2017 dataset is used for the performance analysis of the current IDS model in terms of assaults and anomaly detection. Accuracy, recall, precision, F1-score, detection rate, and other characteristics were all improved by the proposed method. IoT technology has revolutionized how healthcare is provided to patients. Medical institutions are very concerned about IoT security because of the network enabled IoT devices' integration with healthcare network infrastructure.

The Internet of Things (IoT) is increasingly deployed at the edge under resource and environmental constraints, which limits the practicality of traditional intrusion detection systems (IDSs) on IoT hardware. This paper presents two IDS configurations. First, we develop a baseline IDS with fixed hyperparameters, achieving 99.20% accuracy and ~0.002 ms/sample inference latency on a desktop machine; this configuration is suitable for high-performance platforms but is not intended for constrained IoT deployment. Second, we propose a lightweight, edge-oriented IDS that applies ANOVA-based filter feature selection and uses a genetic algorithm (GA) for the bounded hyperparameter tuning of the classifier under stratified cross-validation, enabling efficient execution on Raspberry Pi-class devices. The lightweight IDS achieves 98.95% accuracy with ~4.3 ms/sample end-to-end inference latency on Raspberry Pi while detecting both low-volume and high-volume (DoS/DDoS) attacks. Experiments are conducted in a Raspberry Pi-based real lab using an up-to-date mixed-modal dataset combining system/network telemetry and heterogeneous physical sensors. Overall, the proposed framework demonstrates a practical, hardware-aware, and reproducible way to balance detection performance and edge-level latency using established techniques for real-world IoT IDS deployment.

Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute‐force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC‐IDS‐2017, and BOT‐IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state‐of‐the‐art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

Internet of Things (IoT) devices are vulnerable to a range of attacks that could compromise data or damage essential products. A cost-effective and energy-efficient Intrusion Detection System (IDS) solution for the IoT is needed to balance the security with the resource constraints of IoT devices. These IDS solutions optimize processing speed and energy consumption while maintaining robust threat detection. In this paper, Energy and Cost Effective IDS for IoT, using DNN-CFOA model is proposed. In this work, Deep and Convolutional Network (DNN) has been applied for the task of intrusion detection from patterns and Catch Fish Optimization Algorithm (CFOA) has been applied to optimize the weights of DNN model. Experimental results show that the proposed DNN-CFOA model outperforms the existing models with respect to accuracy and F1-score metrics.

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

The benefits of the Internet of Medical Things (IoMT) in providing seamless healthcare to the world are at the forefront of technological advancement. However, security concerns of any IoMT systems are high since they threaten to compromise personal information of patients and can even cause health hazards. Researchers are exploring the use of various techniques to ensure a high level of security of IoMT systems. One key concern is that the computing power of any Internet of Things (IoT) device is relatively low, hence mechanisms that require low computational power are appropriate for designing Intrusion Detection Systems (IDS). In this research work, a blockchain IDS coalition is proposed for securing IoMT networks and devices. The blockchain ledger is compact and uses less processing resources. Additionally, the ledger requires less communication overhead. The cryptographic hashes in the suggested architecture ensure complete data secrecy and integrity between parties who are trusted and those who are untrustworthy. Peer-to-peer networks in both central and cluster networks are also included in this work for complete decentralization. The proposed model can counter various attacks, including Denial of Service (DoS), anonymity attacks, impersonation attacks, Man-In-The-Middle (MITM), and Cross-Site Scripting (XSS). The proposed method achieved an F1- score as high as 100% and reported an AUC value of over 99%.

In computer networks, intrusion detection systems play the major role to disturb the whole networks. Many latest researches have been done on IDS. Imperfection of intrusion detection systems (IDS) has given a chance for data processing to make many vital contributions to the sphere of intrusion detection. In recent years, several researchers are mistreating data processing techniques for building IDS. In this paper, various data processing techniques like deep belief neural network for IDS in IoT for serving to IDS to achieve higher detection rate are discussed. The term Internet of things (IoT), generally called Internet of objects, proposes the engineered interconnection of basic things, which is regularly observed as a self-managing remote procedure of sensors whose reason is to interconnect all things.

The Internet of Things (IoT) is a cutting-edge concept that unites the Internet with actual physical objects from a variety of industries, such as home automation, manufacturing, human health, and environmental monitoring. Users are now able to use gadgets with Internet access for routine tasks. Along with many advantages, IoT also presents a number of security-related difficulties. The security of the IoT network has grown in importance as the popularity of the Internet of Things increases daily. An IoT network is more secure thanks to encryption and authentication, yet it can be challenging to defend IoT devices from online threats. Cyberattacks on the Internet of Things system not only result in data loss, but they also have the potential to bring the entire system to a standstill. The performance of the network can be negatively impacted by hostile activities, which are easily detected by intrusion detection systems (IDS). To safeguard IoT systems, an efficient intrusion detection system (IDS) is required. Another cutting-edge technology that helps security systems counter the most recent threats is blockchain. In this post, we analysed state-of-the-art blockchain-based intrusion detection methods for Internet of Things devices. New advancements to alleviate security threats are also listed in a table. Finally, we list the major questions brought on by the current restrictions.