Comparative analysis of IoT device security

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distribution, a huge number of IoT devices may connect to the firmware server and excessive network traffic may occur. Consequently, a method using a peer-to-peer file sharing system and a blockchain has been proposed. One study proposed an update scheme considering incentives for distributors to help with updating. However, this method requires the IoT devices, which have limited processing power, to decrypt encrypted files, and the distribution nodes to pay the transaction cost. This paper proposes a firmware distribution method that provides incentives for distributors to help with distribution to reduce the gas costs, using a smart contract and access control based on update records. The additional computations performed by IoT devices and distributors key management were reduced compared to those in previous studies by using access control instead of encryption. Further, the proposed approach was implemented and evaluated using a Raspberry Pi and laptop. The evaluation demonstrated that the gas cost per update was successfully lowered compared to the costs in previous studies.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

Software-defined Naming, Discovery and Session Control for IoT Devices and Smart Phones in the Constraint Networks

In this modern era, we are habituated to small smart home appliances that are typically known as the Internet of Things (IoT) devices. While these devices make our daily life easier with the help of internet connectivity, they are becoming an easy target for attackers to penetrate various cyber-attacks. Distributed Denial of Service (DDoS) is one of the pernicious attacks that can be attempted easily on IoT devices. To make our home smart, these devices play a vital role and as a security expert, it is our responsibility to ensure the security of these IoT devices. In this paper, we propose a security scheme to ensure the IoT devices' security. With the capability of the ensemble machine learning technique, we designed and implemented an intrusion detection system in securing IoT devices. To utilize the full capability of our security solution, we verified it with various well-known and benchmark datasets including the IoT dataset. The results show the efficiency of our security solution by producing better F-1 score and lower false alarms compared to any existing approaches.

In the modern security-conscious world, Deep Packet Inspection (DPI) proxies are increasingly often used on industrial and enterprise networks to perform TLS unwrapping on all outbound connections. However, enabling TLS unwrapping requires local devices to have the DPI proxy Certificate Authority certificates installed. While for conventional computing devices this is addressed via enterprise management, it's a difficult problem for Internet of Things (IoT) devices which are generally not under enterprise management, and may not even be capable of it due to their resource-constrained nature. Thus, for typical IoT devices, being installed on a network with DPI requires either manual device configuration or custom DPI proxy configuration, both of which solutions have significant shortcomings. This poses a serious challenge to the deployment of IoT devices on DPI-enabled intranets. The authors propose a solution to this problem: a method of installing on IoT devices the CA certificates for DPI proxy CAs, as well as other security configuration (security bootstrapping). The proposed solution respects the DPI policies, while allowing the commissioning of IoT and IIoT devices without the need for additional manual configuration either at device scope or at network scope. This is accomplished by performing the bootstrap operation over unsecured connection, and downloading certificates using TLS validation at application level. The resulting solution is light-weight and secure, yet does not require validation of the DPI proxy's CA certificates in order to perform the security bootstrapping, thus avoiding the chicken-and-egg problem inherent in using TLS on DPI-enabled intranets.

This dissertation proposes a solution to the growing concern about the security of Internet of Things (IoT) devices in home or business environments. Entitled MAIOS (Multi Accountability IoT Oriented Switch), the system offers an SDN (Software Defined Networking) switch architecture, which provides effective segmentation for IoT devices, increasing their protection against cyber threats. The research begins by contextualizing the exponential increase in IoT devices and the security challenges associated with them. MAIOS addresses this issue through a centralized control layer, which allows efficient management of IoT devices and the implementation of dynamic security policies, which can be applied from an SDN control plane application layer. The system was designed considering functional, non-functional requirements and deployment scenarios, either in home or business environments. To increase usability, an intuitive interface was developed for users, facilitating the configuration and monitoring of IoT devices. The solution is based on open source technologies such as pfSense proxy/firewall and Home Assistant to provide functionalities such as firewall, IoT control and real-time traffic monitoring. The effectiveness of MAIOS is evaluated through security and performance tests in simulated environments, which demonstrated its ability to identify and mitigate cyber threats, providing robust security for IoT devices. This dissertation offers a contribution to the field of IoT security, presenting a practical, affordable and effective solution to protect devices in residential and business networks. MAIOS represents an important advance in ensuring digital security in an increasingly connected world.

The development and use of Internet of Things (IoT) devices have grown significantly in recent years. Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices. Corporations have begun to embrace the IoT concept. Identifying true and suitable devices, security faults that might be used for bad reasons, and administration of such devices are only a few of the issues that IoT, a new concept in technological progress, provides. In some ways, IoT device traffic differs from regular device traffic. Devices with particular features can be classified into categories, irrespective of their function or performance. Ever-changing and complex environments, like a smart home, demand this classification scheme. A total of 41 IoT devices were employed in this investigation. To build a multiclass classification model, IoT devices contributed 13 network traffic parameters. To further preprocess the raw data received, preprocessing techniques like Normalization and Dataset Scaling were utilized. Feature engineering techniques can extract features from the text data. A total of 117,423 feature vectors are contained in the dataset after stratification, which are used to further improve the classification model. In this study, a variety of performance indicators were employed to show the performance of the logiboosted algorithms. Logi-XGB scored 80.2% accuracy following application of the logit-boosted algorithms to the dataset for classification, whereas Logi-GBC achieved 77.8% accuracy. Meanwhile, Logi-ABC attained 80.7% accuracy. Logi-CBC, on the other hand, received the highest Accuracy score of 85.6%. The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37% each. Our suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode.

A network-independent tool-based usable authentication system for Internet of Things devices

The massive growth in the popularity of Internet of Things (IoT) and hence expansion in the number of IoT devices has led to network control issues. The heterogeneity observed in the generated data from each device has further contributed to latency delays and network traffic concerns. An integral part of current network research encompasses the monitoring of network activities, device identification, and secure exchange of information between different devices. The recognition and administration of these persistently increasing IoT devices have posed major challenges in various fields of their application, like Cyber-Physical Systems (CPSs). Hence, the management of network traffic flow between these devices has become a concerning issue. The prolonged inconsistency in cybersecurity systems and constrained computational capabilities have further made IoT devices more vulnerable to adversarial threats. To this end, the preservation and administration of network activities become crucial to manage. In this chapter, we address the network traffic administration issue for different IoT devices. We focus on the efficient characterization of inter-arrival rates of data generated from IoT devices for packet-level and flow-level analysis. Thus, making identification and management of IoT devices exceedingly significant for securing stable functioning of network activities. We also discuss some influential works conjectured to IoT devices and network analysis. The empirical results obtained from real-world network flows have been reported to provide a precise understanding of our observations. Finally, the strengths and weaknesses of some state-of-the-art technologies are discussed along with relevant future scopes.KeywordsInternet of Things (IoT)Network traffic analysisCyber-Physical System (CPS)IoT devicesFlow controlDevice identification

The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software.

The Internet of Things (IoT) is a vast network of real, tangible objects or "things" that can communicate and share data with other systems and gadgets over the Internet. A vital component of assuring the secure and dependable operation of IoT systems and devices is IoT security. Attackers may use IoT devices to get unauthorized access, change functionality, or compromise the data that the device collects and transmits. The risks of IoT security breaches grow as more devices connect and exchange sensitive data. To check the vulnerability in Iot devices, a novel Blockchain enabled Iot based Security Assessment for intrusion (Block-ISAI) technique has been proposed. Bag of Words (BoW) technique is used for feature extraction of API documents which helps to make the document simpler. Blockchain technology is utilized for secure data storage and IoT device registration. In order to detect intrusion, a deep learning architecture is designed using the verified data The attack is either detected or not detected when the vulnerability is found using the GloVe-CNN-BiLSTM Model. If the vulnerability is detected then alerts will be given. Utilizing evaluation measures like accuracy, time efficiency, precision, F1 score, detection rate, recall, false alarm rate, usability and reliability the efficacy of the suggested ISAI technique has been assessed. By the comparison analysis, the proposed ISAI technique’s detection rate is 18.22%, 19.43%, and 3.13% higher than the existing HIDS, NIDS, and ML-DDoS techniques respectively. The accuracy of the proposed system is increased by 0.69%, 6.04%, and 36.15% as compared to the HIDS, NIDS, and ML-DDoS method using UNSW-NB 15 dataset and increases by 2.37%, 18.32%, and 5.95% using KDDCUP 19 dataset respectively.