Guest Editorial Special Issue on Security and Forensics of Internet of Things: Problems and Solutions

The Internet of Things (IoT) has experienced significant growth over recent years and Gartner predicts that, by 2020, 21 billion IoT endpoints will be in use. The potential behind widespread usage of small devices capable of collecting, transmitting, or acting upon data has been fueling interest both from industry and academia. Security and forensics are two of the topics facing major challenges in this paradigm, on par with or even more prominent than other computing paradigms. Aspects such as low processing power and small storage capacity of such IoT devices contribute to their typically poor built-in security and forensics capabilities. Their reliance on cloud computing and mobile apps to operate and provide services increases the attack surface, distributing the collection of digital evidence and making reconstruction activities (to answer questions as what, where, when, who, why, and how) harder

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept

Radio frequency fingerprint identification for Internet of Things: A survey

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field

Design of a Robust Radio-Frequency Fingerprint Identification Scheme for Multimode LFM Radar

International audienceRadar is an indispensable part of the Internet of Things (IoT). Specific emitter identification is essential to identify the legitimate radars and, more importantly, to reject the malicious radars. Conventional methods rely on pulse parameters that are not capable to identify the specific emitter as two radars may have the same configuration or a malicious radar can perform spoofing attacks. Radio frequency fingerprint (RFF) is the unique and intrinsic hardware characteristic of devices resulted from hardware imperfection, which can be used as the device identity. This paper proposes a robust and reliable radar identification scheme based on the RFF, taking linear frequency modulation (LFM) radar as a case study. This scheme first classifies the operation mode of the pulses, then eliminates the noise effect, and finally identifies the radar emitters based on the transient and modulation-based RFF features. Experimental results verify the effectiveness of our radar identification scheme among three real LFM radars (same model) operating at four modes, each mode with 2,000 pulses from each radar. The identification rates of the four modes are all higher than 90% when the signal-tonoise ratio (SNR) is about 5 dB. In addition, mode 3 achieves almost 100% identification accuracy even when the SNR is as low as-10 dB

Security of Internet of Things (IoT) Using Federated Learning and Deep Learning — Recent Advancements, Issues and Prospects

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. In this context, this review focuses on the implementation of federated learning (FL) and deep learning (DL) algorithms for IoT security. Unlike conventional ML techniques, FL models can maintain the privacy of data while sharing information with other systems. The study suggests that FL can overcome the drawbacks of conventional ML techniques in terms of maintaining the privacy of data while sharing information with other systems. The study discusses different models, overview, comparisons, and summarization of FL and DL-based techniques for IoT security

A Vision of the Internet of Things: A Review of Critical Challenges

Today, Information Communication Technology has brought many benefits to have a better life. Meanwhile, the concept of the Internet of Things (IoT), which has transformed the traditional lifestyle into a modern lifestyle and is growing rapidly, is of great importance. This research deals with the critical challenges of IoT. Although not much time has passed since the advent of the concept of the IoT, today the Internet of Things has faced a great deal of complexity in the industry, which requires in-depth studies to realise its potential and challenges. This study introduces and examines IoT challenges including security and privacy, scalability, interoperability, mobility, protocol & standardisation, and energy consumption. In this study, the relationship between these challenges has been clearly defined. Finally, based on the research, some main challenges or sub-challenges considered for these challenges

Neural-network-aided automatic modulation classification

Automatic modulation classification (AMC) is a pattern matching problem which significantly impacts divers telecommunication systems, with significant applications in military and civilian contexts alike. Although its appearance in the literature is far from novel, recent developments in machine learning technologies have triggered an increased interest in this area of research. In the first part of this thesis, an AMC system is studied where, in addition to the typical point-to-point setup of one receiver and one transmitter, a second transmitter is also present, which is considered an interfering device. A convolutional neural network (CNN) is used for classification. In addition to studying the effect of interference strength, we propose a modification attempting to leverage some of the debilitating results of interference, and also study the effect of signal quantisation upon classification performance. Consequently, we assess a cooperative setting of AMC, namely one where the receiver features multiple antennas, and receives different versions of the same signal from the single-antenna transmitter. Through the combination of data from different antennas, it is evidenced that this cooperative approach leads to notable performance improvements over the established baseline. Finally, the cooperative scenario is expanded to a more complicated setting, where a realistic geographic distribution of four receiving nodes is modelled, and furthermore, the decision-making mechanism with regard to the identity of a signal resides in a fusion centre independent of the receivers, connected to them over finite-bandwidth backhaul links. In addition to the common concerns over classification accuracy and inference time, data reduction methods of various types (including “trained” lossy compression) are implemented with the objective of minimising the data load placed upon the backhaul links.Open Acces

Intelligent Management System for Driverless Vehicles

This research addresses concerns related to driverless vehicles by proposing the development of an Intelligent Management System (IMS). Emphasised in 'The Pathway to Driverless Cars Summary report and action plan' by the UK Department of Transport, key areas for improvement lie in vehicle reliability, maintenance, and passenger safety. The study targets compliance with Society of Automotive Engineers (SAE) Level 5 automation, concentrating on fully autonomous vehicles to enhance commuter satisfaction and overall vehicle performance. Despite advancements, challenges such as on-road safety and integration persist. The research unfolds through a two-stage development process aimed at achieving an Intelligent Management System for Driverless Vehicles (IMSDV). The initial stage, described in chapter 3 involves the creation of a 'Single Seat Driverless Pod' as a test apparatus, simulating various features found in existing driverless vehicles. This includes the development of mechanical steering components and a control system incorporating electronic hardware, sensors, actuators, controllers, wireless remote access, and software. The subsequent phase, described in chapter 4 focuses on autonomous navigation using Google Maps, intelligent motion control, localisation, and tracking algorithms within the driverless pod. The latter chapters of the thesis present the investigation of possible improvements in steering system components. A novel encapsulated vehicle wheel condition monitoring system, integrating the Internet of Things (IoT), is proposed to enhance maintainability, reliability, and passenger safety for driverless vehicles. Testing and validation are conducted in two segments. The driverless pod undergoes initial testing to validate its features and generate data for further sub-system development. Separately, the IoT-based monitoring system undergoes individual testing. The final step involves integrating the IoT capabilities into the driverless pod, testing the sub-system, and capturing relevant data. The thesis outlines the research scope, emphasising significant contributions, with a particular focus on the monitoring system for steering components in driverless vehicles, employing embedded IoT technology. This augmentation, alongside other original contribution, is strategically poised to enhance the maintainability, reliability, and safety of driverless vehicles at SAE Level 5. The concluding chapter succinctly revisits these distinctive contributions and additionally provides recommendations for advancing intelligent management systems for driverless vehicles

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field