Risk Prediction of IoT Devices Based on Vulnerability Analysis

Recently, Internet of Things (IoT) devices and applications are becoming increasingly popular among users in various IoT domains, such as Wearable IoT, Smart Cities, Smart Home, and Smart Industry. With a range of IoT devices, cyber attack surface has hugely expanded from traditional user workstations to small autonomous devices connected to the Internet. In today's connected world, every user owns multiple connected smart devices which seamlessly connect to their organization's network. Therefore, secure and fine-grained access control policies need to be implemented at the organizational level to defend against such attacks. In this paper, we propose an Attribute-Based Access Control (ABAC) approach to defend against cyber attacks in the context of an organization environment which are launched through compromised IoT devices owned by various legitimate users. For example, a wearable IoT device of an employee of an organization which can connect to the organization's network and compromise the whole network and lack of secure access control mechanism will enable IoT Warfare in the future. Therefore, secure and fine-grained ABAC access control mechanisms and policies need to be employed for access control and authorization requirements of IoT devices.

Purpose: Identification of Internet of Thing (IoT) devices in smart home is the most important function for a local server/controller to administer and control the home smoothly. The IoT devices continuously send and receive requests, acknowledgments, packets, etc. for efficient data communication and these communication patterns need to be classified. Design/Methodology/Approach: Therefore, to run the smart home smoothly, in this work a framework using cloud computing is proposed to identify the correct IoT device communicating with the local server based on supervised machine learning. The best supervised machine intelligence model will be installed at the local server to classify the devices on the basis of data communication patterns. Findings/Result: Simulation is performed using Orange 3.26 data analytics tool by considering an IoT devices data communication dataset collected from Kaggle data repository. From the simulation results it is observed that Random Forest (RF) shows better performance than existing supervised machine learning models in terms of classification accuracy (CA) to classify the IoT devices with high accuracy. Originality/Value: A cloud based framework is proposed for a smart home to identify the correct IoT device communicating with the local server based on supervised machine learning. Based on the data communication pattern of the IoT devices, an IoT device is accurately identified. Paper Type: Methodology Paper.

The technology advance and convergence of cyber physical systems, smart sensors, short-range wireless communications, cloud computing, and smartphone apps have driven the proliferation of Internet of things (IoT) devices in smart homes and smart industry. In light of the high heterogeneity of IoT system, the prevalence of system vulnerabilities in IoT devices and applications, and the broad attack surface across the entire IoT protocol stack, a fundamental and urgent research problem of IoT security is how to effectively collect, analyze, extract, model, and visualize the massive network traffic of IoT devices for understanding what is happening to IoT devices. Towards this end, this paper develops and demonstrates an end-to-end system with three key components, i.e., the IoT network traffic monitoring system via programmable home routers, the backend IoT traffic behavior analysis system in the cloud, and the frontend IoT visualization system via smartphone apps, for monitoring, analyzing and virtualizing network traffic behavior of heterogeneous IoT devices in smart homes. The main contributions of this demonstration paper is to present a novel system with an end-to-end process of collecting, analyzing and visualizing IoT network traffic in smart homes.

Connecting food supply chains

Undeniably, Internet of Things (IoT) devices are gradually getting better over time; and IoT-based systems play a significant role in our lives. The pervasiveness of the new essential service models is expanding, and includes self-driving cars, smart homes, smart cities, as well as promoting the development of some traditional fields such as agriculture, healthcare, and transportation; the development of IoT devices has not shown any sign of cooling down. On the one hand, several studies are coming up with many scenarios for IoT platforms, but some critical issues related to performance, speed, power consumption, availability, security, and scalability are not yet fully resolved. On the other hand, IoT devices are manufactured and developed by different organizations and individuals; hence, there is no unified standard (uniformity of IoT devices), i.e., sending and receiving messages among them and between them and the upper layer (e.g., edge devices). To address these issues, this paper proposes an IoT Platform called BMDD (Broker-less and Microservice architecture, Decentralized identity, and Dynamic transmission messages) that has a combination of two architectural models, including broker-less and microservices, with cutting-edge technologies such as decentralized identity and dynamic message transmission. The main contributions of this article are five-fold, including: (i) proposing broker-less and microservice for the IoT platform which can reduce single failure point of brokering architecture, easy to scale out and improve failover; (ii) providing a decentralized authentication mechanism which is suitable for IoT devices attribute (i.e., mobility, distributed); (iii) applying the Role-Based Access Control (RBAC) model for the authorization process; (iv) exploiting the gRPC protocol combined with the Kafka message queue enhances transmission rates, transmission reliability, and reduces power consumption in comparison with MQTT protocol; and (v) developing a dynamic message transmission mechanism that helps users communicate with any device, regardless of the manufacturer, since it provides very high homogeneity.

With the proliferation of Internet of Things (IoT) devices and platforms, it becomes a trend that IoT devices associated with different IoT platforms coexist in a smart home, demonstrating the following characteristics. First, a smart home may use more than one platform to support its devices and automation. Second, IoT devices of a home may transmit messages over different paths. By selectively delaying IoT messages, our study finds that two issues, inconsistency and disorder, can be exacerbated by attackers significantly. We then explore how these issues can be exploited and present seven types of exploitation, collectively referred to as Delay-based Automation Interference (DAI) attacks. DAI attacks cause home automation to yield incorrect interaction results, placing the IoT devices and smart home in insecure, unsafe, or unexpected states. It is worth highlighting that DAI attacks do not depend on any IoT implementation vulnerabilities or leaked keys/tokens, and they do not trigger alarms at any layers of the IoT protocol stack. To demonstrate and evaluate the new attacks, we set up two real-world testbeds, where commercial IoT devices and apps are deployed. The week-long experiments from both testbeds show that an attacker has adequate opportunities to launch DAI attacks that cause security or safety issues.

Smart home consists of various Internet of things (IoT) devices. These IoT devices are designed to help and simplify people’s lives. The technical progress of the IoT field is aimed at simplifying human life, thereby creating new cyber threats. Different scientific papers are mentioned that number of IoT devices is growing constantly by 15% per year. As a result, around 1.6 billion IoT devices will be used globally over the internet. It means that IoT devices will be accessed over internet by consumers. Nowadays, Internet is accessible easily by everyone, so they can afford freely the ecosystem of IoT devices at home. Within this development of IoT ecosystem, consumers can face serious problems of transmission and storage of information by IoT devices. These problems might be data theft from IoT devices, using such IoT devices for Denial of Service (DoS) attacks, user tracking and so on. Local cyber threats provide an opportunity for an attacker to gain access to a home network and take advantages of it. Global cyber threats are dangerous because IoT devices can be controlled remotely from anywhere in the world without the knowledge of the user. One of the risks is that the user’s home network of IoT devices could be controlled by botnets to carry out cyber-attacks. The article describes and analyzes current threats to IoT smart home devices and provides examples of data collected and processed by smart devices. Collecting information about users through IoT devices is a novelty of this work.

Internet of Things (IoT) devices are getting increasingly popular, becoming a core element for the next generations of informational architectures: smart city, smart factory, smart home, smart health-care and many others. IoT systems are mainly comprised of embedded devices with limited computing capabilities while having a cloud component which processes the data and delivers it to the end-users. IoT devices access the user private data, thus requiring robust security solution which must address features like usability and scalability. In this paper we discuss about an IoT authentication service for smart-home devices using a smart-phone as security anchor, QR codes and attribute based cryptography (ABC). Regarding the fact that in an IoT ecosystem some of the IoT devices and the cloud components may be considered untrusted, we propose a privacy preserving attribute based access control protocol to handle the device authentication to the cloud service. For the smart-phone centric authentication to the cloud component, we employ the FIDO UAF protocol and we extend it, by adding an attributed based privacy preserving component.

One of the impediments to transforming urban cities into smart cities is the security and privacy concerns that arise due to use of Internet of Things (IoT) devices in various smart city applications. While IoT device vendors publish their security and privacy policies, manual evaluation of these policies is tedious and prone to misinterpretation as there is a lot of variability in the language used across IoT vendors. Local administrations and policy analysts are faced with understanding the implications of integrating IoT devices with differing security and privacy characteristics but lack methods that support them in analysis of privacy characteristics from a holistic perspective. In this paper, a methodology for knowledge elicitation from textual information is outlined to evaluate privacy characteristics of IoT devices. The methodology includes natural language processing and deep learning techniques to evaluate the relevance of IoT privacy policies to the National Institute of Standards and Technology (NIST) security and privacy framework 5 . Based on the analysis, text similarity scores are calculated for each IoT privacy policy document and each section of the policy document is labeled to NIST categories and functions. Analysis of these resulting labels and scores helps analysts to gain insights on each privacy policy as well as provide a holistic perspective of the privacy characteristics of IoT devices used in smart city applications. For example, all the policy documents used in the study talk about Protect domain and half of the documents cover Detect domain. However, most of the policies contain gaps regarding the Identify , Respond , and Recover domains. The study has implications for policy analysts, IoT vendors, and smart city administrators in terms of understanding the privacy gaps in IoT devices with respect to the NIST framework which can ultimately support policy alignment to address privacy concerns for smart cities.

Internet of Thing (IoT) devices are rapidly becoming an indispensable part of our life with their increasing deployment in many promising areas, including tele-health, smart city, intelligent agriculture. Understanding the mobility of IoT devices is essential to improve quality of service in IoT applications, such as route planning in logistic management, infrastructure deployment, cellular network update and congestion detection in intelligent traffic. Despite its importance, there are not many results pertaining to the mobility of IoT devices. In this article, we aim to answer three research questions: (i) what are the mobility patterns of IoT device? (ii) what are the differences between IoT device and smartphone mobility patterns? (iii) how the IoT device mobility patterns differ among device types and usage scenarios? We present a comprehensive characterization of IoT device mobility patterns from the perspective of cellular data networks, using a 36-days long signal trace, including 1.5 million IoT devices and 0.425 million smartphones, collected from a nation-wide cellular network in China. We first investigate the basic patterns of IoT devices from two perspectives: temporal and spatial characteristics. Our study finds that IoT device mobility exhibits significantly different patterns compared with smartphones in multiple aspects. For instance, IoT devices move more frequently and have larger radius of gyration. Then we explore the essential mobility of IoT devices by utilizing two models that reveal the nature of human mobility, i.e., exploration and preferential return (EPR) model and entropy based predictability model. We find that IoT devices, with few exceptions, behave totally different from human, and we further derive a new formulation to describe their movement. We also find the gap mobility predictability and predictability limit between IoT and human is not as big as people expected.

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

This paper proposes a DNS Name Autoconfiguration (called DNSNA) for not only the global DNS names, but also the local DNS names of Internet of Things (IoT) devices. Since there exist so many devices in the IoT environments, it is inefficient to manually configure the Domain Name System (DNS) names of such IoT devices. By this scheme, the DNS names of IoT devices can be autoconfigured with the device's category and model in IPv6-based IoT environments. This DNS name lets user easily identify each IoT device for monitoring and remote-controlling in IoT environments. In the procedure to generate and register an IoT device's DNS name, the standard protocols of Internet Engineering Task Force (IETF) are used. Since the proposed scheme resolves an IoT device's DNS name into an IPv6 address in unicast through an authoritative DNS server, it generates less traffic than Multicast DNS (mDNS), which is a legacy DNS application for the DNS name service in IoT environments. Thus, the proposed scheme is more appropriate in global IoT networks than mDNS. This paper explains the design of the proposed scheme and its service scenario, such as smart road and smart home. The results of the simulation prove that our proposal outperforms the legacy scheme in terms of energy consumption.

The traditional infrastructures are assisted by introducing the promising applications of Internet of things (IoT) (smart cities, smart homes, smart girds and smart health) with smart objects. In cloud servers, DDoS attacks happened and cause a problem of overwhelming. But Internet of things (IoT) devices increase in number which leads to cause the large-scale DDoS attacks influence from the IoT devices. Therefore, design and implementation of efficient counter-based IoT DDoS attack detection system using machine learning is proposed in this paper. Different network parameters values are used in detection of abnormal defense activities and DDoS attacks by the proposed framework. With the help of wired or wireless networks, the required dataset as sensor data is collected from the different sensors which are equipped on the eight smart poles which are constructed on certain campus. According to types of DDoS attacks, the features are extracted. Different machine learning classifiers are used in this proposed DDoS attack detection method as neural network, LSVM, random tree and decision tree (J-48). In the real IoT environment, DDoS attack detection method with best accuracy is obtained by using feature selection. Therefore, from the experimental results, the accuracy performance is having or achieving the higher accuracy. IoT DDoS attacks detection system results effectively block the harmed devices.KeywordsDDoS attacksMachine learning (ML)Internet of things (IoT)Software defined networks (SDN)

E-CIS: Edge-based classifier identification scheme in green & sustainable IoT smart city

Recently, smart cities, smart homes, and smart medical systems have challenged the functionality and connectivity of the large-scale Internet of Things (IoT) devices. Thus, with the idea of offloading intensive computing tasks from them to edge nodes (ENs), edge computing emerged to supplement these limited devices. Benefit from this advantage, IoT devices can save more energy and still maintain the quality of the services they should provide. However, computational offload decisions involve federation and complex resource management and should be determined in the real-time face to dynamic workloads and radio environments. Therefore, in this work, we use multiple deep reinforcement learning (DRL) agents deployed on multiple edge nodes to indicate the decisions of the IoT devices. On the other hand, with the aim of making DRL-based decisions feasible and further reducing the transmission costs between the IoT devices and edge nodes, federated learning (FL) is used to train DRL agents in a distributed fashion. The experimental results demonstrate the effectiveness of the decision scheme and federated learning in the dynamic IoT system.