The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in cyber and physical realms. In order to manage such destructive effects, incident responders and cyber investigators require the capabilities to find these rogue IoT and contain them quickly. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT's network behaviorial fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there is limited network activity traces. This research proposes the novel model construct that learns to infer the network behaviorial fingerprint of specific IoT based on limited network activity traces using a One-Card Time Series Meta-Learner called DeepNetPrint. Our research also demonstrates the application of DeepNetPrint to identify IoT devices that performs comparatively well against leading supervised learning models. Our solution would enable cyber investigator to identify specific IoT of interest while overcoming the constraints of having only limited network traces of the IoT.
Deep-learning-based device fingerprinting has recently been recognized as a key enabler for automated network access authentication. Its robustness to impersonation attacks due to the inherent difficulty of replicating physical features is what distinguishes it from conventional cryptographic solutions. Although device fingerprinting has shown promising performances, its sensitivity to changes in the network operating environment still poses a major limitation. This paper presents an experimental framework that aims to study and overcome the sensitivity of LoRa-enabled device fingerprinting to…
University of Technology Sydney（悉尼科技大学）；University of Engineering and Technology, Vietnam National University, Hanoi（河内越南国立大学工程技术大学）；Advanced Institute of Engineering and Technology (AVITECH)（高等工程技术研究院（AVITECH））
Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome t…
The rapid development of IoT applications and their use in various fields of everyday life has resulted in an escalated number of different possible cyber-threats, and has consequently raised the need of securing IoT devices. Collecting Cyber-Threat Intelligence (e.g., zero-day vulnerabilities or trending exploits) from various online sources and utilizing it to proactively secure IoT systems or prepare mitigation scenarios has proven to be a promising direction. In this work, we focus on social media monitoring and investigate real-time Cyber-Threat Intelligence detection from the Twitter str…
Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates…
University of Wisconsin – Eau Claire（威斯康星大学欧克莱尔分校）
Modern scientific advancements often contribute to the introduction and refinement of never-before-seen technologies. This can be quite the task for humans to maintain and monitor and as a result, our society has become reliant on machine learning to assist in this task. With new technology comes new methods and thus new ways to circumvent existing cyber security measures. This study examines the effectiveness of three distinct Internet of Things cyber security algorithms currently used in industry today for malware and intrusion detection: Random Forest (RF), Support-Vector Machine (SVM), and…
University of Tennessee at Chattanooga（田纳西大学查塔努加分校）
It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices that masquerade as authorized devices to circumvent digital authentication approaches. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demon…
The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one's network. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carrie…
The Internet-of-Things (IoT) has brought in new challenges in, device identification --what the device is, and, authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or scalability problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. We believe that device fingerprinting can be used to solve bot…
Electronic locks can provide security- and convenience-enhancing features, with fingerprint readers an increasingly common feature in these products. When equipped with a wireless radio, they become a smart lock and join the billions of IoT devices proliferating our world. However, such capabilities can also be used to transform smart locks into fingerprint harvesters that compromise an individual's security without their knowledge. We have named this the droplock attack. This paper demonstrates how the harvesting technique works, shows that off-the-shelf smart locks can be invisibly modified …
The goal of this paper is to propose a blockchain-based platform to enhance transparency and traceability of cybersecurity certification information motivated by the recently adopted EU Cybersecurity Act. The proposed platform is generic and intended to support the trusted exchange of cybersecurity certification information for any electronic product, service, or process. However, for the purposes of this paper, we focus on the case study of the cybersecurity certification of IoT devices, which are explicitly referenced in the recently adopted Cybersecurity Act as one of the main domains where…
Hanoi University of Science and Technology（河内理工大学）；ENS（高等纺织与艺术学院（ENSATT））
Internet of Things and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as…
Especially in context of critical urban infrastructures, trust in IoT data is of utmost importance. While most technology stacks provide means for authentication and encryption of device-to-cloud traffic, there are currently no mechanisms to rule out physical tampering with an IoT device's sensors. Addressing this gap, we introduce a new method for extracting a hardware fingerprint of an IoT sensor which can be used for secret-free authentication. By comparing the fingerprint against reference measurements recorded prior to deployment, we can tell whether the sensing hardware connected to the …
The next generation of cellular technology, 6G, is being developed to enable a wide range of new applications and services for the Internet of Things (IoT). One of 6G's main advantages for IoT applications is its ability to support much higher data rates and bandwidth as well as to support ultra-low latency. However, with this increased connectivity will come to an increased risk of cyber threats, as attackers will be able to exploit the large network of connected devices. Generative Artificial Intelligence (AI) can be used to detect and prevent cyber attacks by continuously learning and adapt…
The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that -given the appropriate tools and methods-may be identified, crawled and subsequently leveraged to actionable cyber-threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker forums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a …
Internet of Things (IoT) is highly vulnerable to emerging Advanced Persistent Threats (APTs) that are often operated by well-resourced adversaries. Achieving perfect security for IoT networks is often cost-prohibitive if not impossible. Cyber insurance is a valuable mechanism to mitigate cyber risks for IoT systems. In this work, we propose a bi-level game-theoretic framework called FlipIn to design incentive-compatible and welfare-maximizing cyber insurance contracts. The framework captures the strategic interactions among APT attackers, IoT defenders, and cyber insurance insurers, and incorp…
Abstract--- With the rapid growth of the Internet of Things (IoT), current Cloud systems face various drawbacks such as lack of mobility support, location-awareness, geo-distribution, high latency, as well as cyber threats. Fog/Edge computing has been proposed for addressing some of the drawbacks, as it enables computing resources at the network's edges and it locally offers big-data analytics rather than transmitting them to the Cloud. The Fog is defined as a Cloud-like system having similar functions, including software-, platform- and infrastructure-as services. The deployment of Fog applic…
IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detec…
The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analy…
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
The various types of communication technologies and mobility features in Internet of Things (IoT) on the one hand enable fruitful and attractive applications, but on the other hand facilitates malware propagation, thereby raising new challenges on handling IoT-empowered malware for cyber security. Comparing with the malware propagation control scheme in traditional wireless networks where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to be patched. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and pra…
University of Oxford（牛津大学）；Huddersfield University（哈德斯菲尔德大学）；University of Warwick（华威大学）；University of Kent（肯特大学）；University College London（伦敦大学学院）；Keele University（基尔大学）
Recent years have seen exponential development in wireless sensor devices and their rebirth as IoT, as well as increased popularity in wireless home devices such as bulbs, fans, and microwave. As they can be used in various fields such as medical devices, environmental studies, fire department or military application, etc., security of these low powered devices will always be a concern for all the user and security experts. Users nowadays want to control all these "smart" wireless home devices through their smartphones using an internet connection. Attacks such as distributed attacks on these …
The rapid growth of Information and Communication Technology (ICT) in the 21st century has resulted in the emergence of a novel technological paradigm; known as the Internet of Things, or IoT. The IoT, which is at the heart of today's smart infrastructure, aids in the creation of a ubiquitous network of things by simplifying interconnection between smart digital devices and enabling Machine to Machine (M2M) communication. As of now, there are numerous examples of IoT use cases available, assisting every person in this world towards making their lives easier and more convenient. With the latest…
The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but re…
Internet of Things (IoT) is the interconnection of heterogeneous smart devices through the Internet with diverse application areas. The huge number of smart devices and the complexity of networks has made it impossible to secure the data and communication between devices. Various conventional security controls are insufficient to prevent numerous attacks against these information-rich devices. Along with enhancing existing approaches, a peripheral defence, Intrusion Detection System (IDS), proved efficient in most scenarios. However, conventional IDS approaches are unsuitable to mitigate conti…
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction
Badji Mokhtar-Annaba University（巴吉·穆克塔尔-安纳巴大学）；University of La Rochelle（拉罗谢尔大学）；National Higher School of Technology and Engineering -Annaba（安纳巴国家技术与工程高等学校）
How do healthcare organizations (from small Practices to large HDOs) evaluate adherence to the cybersecurity and privacy protection of Medical Internet of Things (MIoT) used in clinical settings? This paper suggests an approach for such evaluation using National Institute of Standards and Technology (NIST) guidance. Through application of NISTIR 8228 Expectations it is possible to quantitatively assess cybersecurity and privacy protection, and determine relative compliance with recommended standards. This approach allows organizations to evaluate the level of risk a MiOT device poses to IT sys…
Internet of Things (IoT) is the next big evolutionary step in the world of internet. The main intention behind the IoT is to enable safer living and risk mitigation on different levels of life. With the advent of IoT botnets, the view towards IoT devices has changed from enabler of enhanced living into Internet of vulnerabilities for cyber criminals. IoT botnets has exposed two different glaring issues, 1) A large number of IoT devices are accessible over public Internet. 2) Security (if considered at all) is often an afterthought in the architecture of many wide spread IoT devices. In this ar…
As the internet penetration rate in Africa increases, so does the proliferation of the Internet of Things (IoT) devices. Along with this growth in internet access is the risk of cyberattacks to vulnerable IoT devices mushrooming in the African cyberspace. One way to determine IoT vulnerabilities is to find open ports within Africa s cyberspace. Our research leverages Shodan search engine, a powerful tool for discovering IoT devices facing the public internet, to find open ports across Africa. We conduct an analysis of our findings, ranking countries from most to least vulnerable to cyberattack…
Cybersecurity is a very emerging field that protects systems, networks, and data from digital attacks. With the increase in the scale of the Internet and the evolution of cyber attacks, developing novel cybersecurity tools has become important, particularly for Internet of things (IoT) networks. This paper provides a systematic review of the application of deep learning (DL) approaches for cybersecurity. This paper provides a short description of DL methods which is used in cybersecurity, including deep belief networks, generative adversarial networks, recurrent neural networks, and others. Ne…
The concept of traditional farming is changing rapidly with the introduction of smart technologies like the Internet of Things (IoT). Under the concept of smart agriculture, precision agriculture is gaining popularity to enable Decision Support System (DSS)-based farming management that utilizes widespread IoT sensors and wireless connectivity to enable automated detection and optimization of resources. Undoubtedly the success of the system would be impacted on crop productivity, where failure would impact severely. Like many other cyber-physical systems, one of the growing challenges to avoid…
Universiti Brunei Darussalam（文莱达鲁萨兰大学）；Sylhet International University（锡尔赫特国际大学）
The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The meth…
While the Internet of things (IoT) promises to improve areas such as energy efficiency, health care, and transportation, it is highly vulnerable to cyberattacks. In particular, DDoS attacks work by overflowing the bandwidth of a server. But many IoT devices form part of cyber-physical systems (CPS). Therefore, they can be used to launch a "physical" denial-of-service attack (PDoS) in which IoT devices overflow the "physical bandwidth" of a CPS. In this paper, we quantify the population-based risk to a group of IoT devices targeted by malware for a PDoS attack. To model the recruitment of bots,…
Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited s…
We consider the future cyber security of industrial control systems. As best as we can see, much of this future unfolds in the context of the Internet of Things (IoT). In fact, we envision that all industrial and infrastructure environments, and cyber-physical systems in general, will take the form reminiscent of what today is referred to as the IoT. IoT is envisioned as multitude of heterogeneous devices densely interconnected and communicating with the objective of accomplishing a diverse range of objectives, often collaboratively. One can argue that in the relatively near future, the IoT co…
