Biblio

This paper uses the test tool provided by the Internet Protocol Version 6 (IPv6) Forum to test the protocol conformance of IPv6 devices. The installation and testing process of IPv6 Ready Logo protocol conformance test suite developed by TAHI PROJECT team is described in detail. This section describes the test content and evaluation criteria of the suite, analyzes the problems encountered during the installation and use of the suite, describes the method of analyzing the test results of the suite, and describes the test content added to the latest version of the test suite. The test suite can realize automatic testing, the test cases accurately reflect the requirements of the IPv6 protocol specification, can be used to judge whether IPv6-based Internet of Things(IoT) devices meets the relevant protocol standards.

Over the last few years, the deployment of Internet of Things (IoT) is attaining much more concern on smart computing devices. With the exponential growth of small devices and at the same time cheap prices of these sensing devices, there raises an important question for the security of the stored information as these devices generate a large amount of private data for observing and controlling purposes. Distributed Denial of Service (DDoS) attacks are current examples of major security threats to IoT devices. As yet, no standard protocol can fully ensure the security of IoT devices. But adaptive decision making along with elasticity and incessant monitoring is required. These difficulties can be resolved with the assistance of Software Defined Networking (SDN) which can viably deal with the security dangers to the IoT devices in a powerful and versatile way without hampering the lightweightness of the IoT devices. Although SDN performs quite well for managing and controlling IoT devices, security is still an open concern. Nonetheless, there are a few challenges relating to the mitigation of DDoS attacks in IoT systems implemented with SDN architecture. In this paper, a brief overview of some of the popular DDoS attack mitigation techniques and their limitations are described. Also, the challenges of implementing these techniques in SDN-based architecture to IoT devices have been presented.

As the demand for smart devices in homes increases, more and more manufacturers have been launching these devices on a mass scale. But what they are missing out on is taking care of the security part of these IoT devices which results in a more vulnerable system. This paper presents an idea through a small-scale working model and the studies that made the same possible. IoT devices face numerous threats these days with the ease of access to powerful hacking tools such as aircrack-ng which provides services like monitoring, attacking and cracking Wifi networks. The essential thought of the proposed system is to give an idea of how some common attacks are carried out, how these attacks work and to device some form of prevention as an additional security layer for IoT devices in general. The system proposed here prevents most forms of attacks that target the victim IoT device using their MAC addresses. These include DoS and DDoS attacks, both of which are the main focus of this paper. This paper also points out some of the future research work that can be followed up.

Recent technological advancements have enabled proliferated use of small embedded and IoT devices for collecting, processing, and transferring the security-critical information and user data. This exponential use has acted as a catalyst in the recent growth of sophisticated attacks such as the replay, man-in-the-middle, and malicious code modification to slink, leak, tweak or exploit the security-critical information in malevolent activities. Therefore, secure communication and software state assurance (at run-time and boot-time) of the device has emerged as open security problems. Furthermore, these devices need to have an appropriate recovery mechanism to bring them back to the known-good operational state. Previous researchers have demonstrated independent methods for attack detection and safeguard. However, the majority of them lack in providing onboard system recovery and secure communication techniques. To bridge this gap, this manuscript proposes SRACARE - a framework that utilizes the custom lightweight, secure communication protocol that performs remote/local attestation, and secure boot with an onboard resilience recovery mechanism to protect the devices from the above-mentioned attacks. The prototype employs an efficient lightweight, low-power 32-bit RISC-V processor, secure communication protocol, code authentication, and resilience engine running on the Artix 7 Field Programmable Gate Array (FPGA) board. This work presents the performance evaluation and state-of-the-art comparison results, which shows promising resilience to attacks and demonstrate the novel protection mechanism with onboard recovery. The framework achieves these with only 8% performance overhead and a very small increase in hardware-software footprint.

Today, Internet of Things (IoT) devices mostly operate in enclosed, proprietary environments. To unfold the full potential of IoT applications, a unifying and permissionless environment is crucial. All IoT devices, even unknown to each other, would be able to trade services and assets across various domains. In order to realize those applications, uniquely resolvable identities are essential. However, quantifiable trust in identities and their authentication are not trivially provided in such an environment due to the absence of a trusted authority. This research presents a new identity and trust framework for IoT devices, based on Distributed Ledger Technology (DLT). IoT devices assign identities to themselves, which are managed publicly and decentralized on the DLT's network as Self Sovereign Identities (SSI). In addition to the Identity Management System (IdMS), the framework provides a Web of Trust (WoT) approach to enable automatic trust rating of arbitrary identities. For the framework we used the IOTA Tangle to access and store data, achieving high scalability and low computational overhead. To demonstrate the feasibility of our framework, we provide a proof-of-concept implementation and evaluate the set objectives for real world applicability as well as the vulnerability against common threats in IdMSs and WoTs.

The growing prevalence of Internet-of-Things (IoT) technology has led to an increase in the development of heterogeneous smart applications. Smart applications may involve a collaborative participation between IoT devices. Participation of IoT devices for specific application requires a tamper-proof identity to be generated and stored, in order to completely represent the device, as well as to eliminate the possibility of identity spoofing and presence of rogue devices in a network. In this paper, we present a composite Identity-of-Things (IDoT) approach on IoT devices with permissioned blockchain implementation for distributed identity management model. Our proposed approach considers both application and device domains in generating the composite identity. In addition, the use of permissioned blockchain for identity storage and verification allows the identity to be immutable. A simulation has been carried out to demonstrate the application of the proposed identity management model.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

With the development of the Internet of Things (IoT), it has been widely deployed. As many embedded devices are connected to the network and massive amounts of security-sensitive data are stored in these devices, embedded devices in IoT have become the target of attackers. The trusted computing is a key technology to guarantee the security and trustworthiness of devices' execution environment. This paper focuses on security problems on IoT devices, and proposes a security architecture for IoT devices based on the trusted computing technology. This paper implements a security management system for IoT devices, which can perform integrity measurement, real-time monitoring and security management for embedded applications, providing a safe and reliable execution environment and whitelist-based security protection for IoT devices. This paper also designs and implements an embedded security protection system based on trusted computing technology, containing a measurement and control component in the kernel and a remote graphical management interface for administrators. The kernel layer enforces the integrity measurement and control of the embedded application on the device. The graphical management interface communicates with the remote embedded device through the TCP/IP protocol, and provides a feature-rich and user-friendly interaction interface. It implements functions such as knowledge base scanning, whitelist management, log management, security policy management, and cryptographic algorithm performance testing.

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of smart and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are attractive targets for security attackers. Hence, with their integration into both the industry and consumer devices, they added a new surface for cybersecurity attacks. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. The design of secure systems is a complex task, especially if they must adhere to other constraints, such as performance, power consumption, and others. A range of design space exploration tools have been proposed in academics, which aim to support system designers in their task of finding the optimal selection of hardware components and task mappings. Said tools offer a limited way of modeling attack scenarios as constraints for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early design phase. It offers designers to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and security risk. The framework's feasibility and performance is demonstrated by revisiting a potential system design of an industry partner.

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Today our world benefits from Internet of Things (IoT) technology; however, new security problems arise when these IoT devices are introduced into our homes. Because many of these IoT devices have access to the Internet and they have little to no security, they make our smart homes highly vulnerable to compromise. Some of the threats include IoT botnets and generic confidentiality, integrity, and availability (CIA) attacks. Our research explores botnet detection by experimenting with supervised machine learning and deep-learning classifiers. Further, our approach assesses classifier performance on unbalanced datasets that contain benign data, mixed in with small amounts of malicious data. We demonstrate that the classifiers can separate malicious activity from benign activity within a small IoT network dataset. The classifiers can also separate malicious activity from benign activity in increasingly larger datasets. Our experiments have demonstrated incremental improvement in results for (1) accuracy, (2) probability of detection, and (3) probability of false alarm. The best performance results include 99.9% accuracy, 99.8% probability of detection, and 0% probability of false alarm. This paper also demonstrates how the performance of these classifiers increases, as IoT training datasets become larger and larger.

The facial recognition time by time takes more importance, due to the extend kind of applications it has, but it is still challenging when faces big variations in the characteristics of the biometric data used in the process and especially referring to the transportation of information through the internet in the internet of things context. Based on the systematic review and rigorous study that supports the extraction of the most relevant information on this topic [1], a software architecture proposal which contains basic security requirements necessary for the treatment of the data involved in the application of facial recognition techniques, oriented to an IoT environment was generated. Concluding that the security and privacy considerations of the information registered in IoT devices represent a challenge and it is a priority to be able to guarantee that the data circulating on the network are only accessible to the user that was designed for this.

With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.

IoT devices such as Google Home and Amazon Echo provide great convenience to our lives. Many of these IoT devices collect data including Personal Identifiable Information such as names, phone numbers, and addresses and thus IoT security is important. However, conducting security analysis on IoT devices is challenging due to the variety, the volume of the devices, and the special skills required for hardware and software analysis. In this research, we create and demonstrate a dynamic analysis security testing infrastructure for capturing network traffic from IoT devices. The network traffic is automatically mirrored to a server for live traffic monitoring and offline data analysis. Using the dynamic analysis security testing infrastructure, we conduct extensive security analysis on network traffic from Google Home and Amazon Echo. Our testing results indicate that Google Home enforces tighter security controls than Amazon Echo while both Google and Amazon devices provide the desired security level to protect user data in general. The dynamic analysis security testing infrastructure presented in the paper can be utilized to conduct similar security analysis on any IoT devices.

In recent days the attention of the researchers has been grabbed by the advent of fog computing which is found to be a conservatory of cloud computing. The fog computing is found to be more advantageous and it solves mighty issues of the cloud namely higher delay and also no proper mobility awareness and location related awareness are found in the cloud environment. The IoT devices are connected to the fog nodes which support the cloud services to accumulate and process a component of data. The presence of Fog nodes not only reduces the demands of processing data, but it had improved the quality of service in real time scenarios. Nevertheless the fog node endures from challenges of false data injection, privacy violation in IoT devices and violating integrity of data. This paper is going to address the key issues related to homomorphic encryption algorithms which is used by various researchers for providing data integrity and authenticity of the devices with their merits and demerits.

The Internet of Things enables interaction between IoT devices and users through the cloud. The cloud provides services such as account monitoring, device management, and device control. As the center of the IoT platform, the cloud provides services to IoT devices and IoT applications through APIs. Therefore, the permission verification of the API is essential. However, we found that some APIs are unverified, which allows unauthorized users to access cloud resources or control devices; it could threaten the security of devices and cloud. To check for unauthorized access to the API, we developed IoT-APIScanner, a framework to check the permission verification of the cloud API. Through observation, we found there is a large amount of interactive information between IoT application and cloud, which include the APIs and related parameters, so we can extract them by analyzing the code of the IoT application, and use this for mutating API test cases. Through these test cases, we can effectively check the permissions of the API. In our research, we extracted a total of 5 platform APIs. Among them, the proportion of APIs without permission verification reached 13.3%. Our research shows that attackers could use the API without permission verification to obtain user privacy or control of devices.

The Internet of Things (IoT) integrates the Internet and electronic devices belonging to different domains, such as smart home automation, industrial processes, military applications, health, and environmental monitoring. Usually, IoT devices have limited resources and Low Power and Lossy Networks (LLNs) are being used to interconnect such devices. Routing Protocol for Low-Power and Lossy Networks (RPL) is one of the preferred routing protocols for this type of network, since it was specially developed for LLNs, also known as IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). In this paper the most well-known routing attacks against 6LoWPAN networks were studied and implemented through simulation, conducting a behavioral analysis of network components (resources, topology, and data traffic) under attack condition. In order to achieve a better understanding on how attacks in 6LoWPAN work, we first conducted a study on 6LoWPAN networks and RPL protocol functioning. Furthermore, we also studied a series of well-known routing attacks against this type of Wireless Sensor Networks and these attacks were then simulated using Cooja simulator provided by Contiki operating system. The results obtained after the simulations are discussed along with other previous researches. This analysis may be of real interest when it comes to identify indicators of compromise for each type of attack and appropriate countermeasures for prevention and detection of these attacks.

The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store and analyze sensitive data as well as the real-time data processing system to be as fast as possible. The cloud services used to store and process sensitive data are often come out to be vulnerable to outside threats. Furthermore, to analyze streaming IoT data swiftly, they are in need of a fast and efficient system. The Paper will envision the aspects of complexity dealing with real time data from various devices in parallel, building solution to ingest data from different IOT devices, forming a secure platform to process data in a short time, and using various techniques of IOT edge computing to provide meaningful intuitive results to users. The paper envisions two modules of building a real time data analytics system. In the first module, we propose to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics with real-time data collection from various IoT devices in parallel. We envision a framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, end-to-end data encryption mechanism, and strong access control policies. Moreover, we design a generic framework to simplify the process of collecting and storing heterogeneous data coming from diverse IoT devices. In the second module, we envision a drone-based data processing system in real-time using edge computing and on-device computing. As, we know the use of drones is growing rapidly across many application domains including real-time monitoring, remote sensing, search and rescue, delivery of goods, security and surveillance, civil infrastructure inspection etc. This paper demonstrates the potential drone applications and their challenges discussing current research trends and provide future insights for potential use cases using edge and on-device computing.

Internet of Things is the new paradigm towards which the world is moving today. As these devices proliferate, security issues at these scales become more and more intimidating. Traditional approach like an antivirus does not work well with these devices and there is a need to look for a more trusted solution. For a device with reasonable computational power, we use a software trusted platform module for the cryptographic operations. In this paper, we have developed a model to remotely attest to the integrity of the processes running in the device. We have also explored the various features of the TPM (Trusted Platform Module) to gain insight into its working and also to ascertain those which can make this process better. This model depends on the server and the TPM to behave as roots of trust for this model. The client computes the HMAC (Hashed Message Authentication Code) values and appends a nonce and sends these values periodically to the server via asymmetric encryption. The HMAC values are verified by the server by comparing with its known good values (KGV) and the trustworthiness of the process is determined and accordingly an authorization response is sent.

In the Internet of Things architecture, devices are frequently connected to the Internet either directly or indirectly. However, many IoT devices lack built-in security features such as device level encryption, user authentication and basic firewall protection. This paper discusses security risks in the layers of general Internet of Things architecture and shows examples of potential risks at each level of the architecture. The paper also compares IoT security solutions provided by three major vendors and shows that the solutions are mutually complementary. Nevertheless, none of the examined IoT solutions provides security at the device level of the IoT architecture model. In order to address risks at the device level of the architecture, an implementation of Trusted Platform Module and Unique Device Identifier on IoT devices and gateways for encryption, authentication and device management is advocated in the paper.

The recent trend of military is to combined Internet of Things (IoT) knowledge to their field for enhancing the impact in battlefield. That's why Internet of battlefield (IoBT) is our concern. This paper discusses how Fog Radio Access Network(F-RAN) can provide support for local computing in Industrial IoT and IoBT. F-RAN can play a vital role because of IoT devices are becoming popular and the fifth generation (5G) communication is also an emerging issue with ultra-low latency, energy consumption, bandwidth efficiency and wide range of coverage area. To overcome the disadvantages of cloud radio access networks (C-RAN) F-RAN can be introduced where a large number of F-RAN nodes can take part in joint distributed computing and content sharing scheme. The F-RAN in IoBT is effective for enhancing the computing ability with fog computing and edge computing at the network edge. Since the computing capability of the fog equipment are weak, to overcome the difficulties of fog computing in IoBT this paper illustrates some challenging issues and solutions to improve battlefield efficiency. Therefore, the distributed computing load balancing problem of the F-RAN is researched. The simulation result indicates that the load balancing strategy has better performance for F-RAN architecture in the battlefield.

Partitioning security components of IoT devices to enable the use of Trusted Execution Environments adds resilience against side-channel attacks. Devices are hardened against extraction of sensitive information, but at the same time additional effort must be spent for the integration of the TEE and software partitioning. To perform partitioning, the developer typically inserts Remote Procedure Calls into the software. Existing RPC-based solutions require the developer to write Interface Definition Language files to generate RPC stubs. In this work, we present an RPC-based framework that supports software partitioning via a graphical user interface. The framework extracts required information about the interfaces from source-code header files to eliminate the need for IDL files. With this approach the TEE integration time is reduced and reuse of existing libraries is supported. We evaluate a Proof-of-Concept by partitioning a TLS library for IoT devices and compare our approach to other RPC-based solutions.

The extensive increase in the number of IoT devices and the massive data generated and sent to the cloud hinder the cloud abilities to handle it. Further, some IoT devices are latency-sensitive. Such sensitivity makes it harder for far clouds to handle the IoT needs in a timely manner. A new technology named "Fog computing" has emerged as a solution to such problems. Fog computing relies on close by computational devices to handle the conventional cloud load. However, Fog computing introduced additional problems related to the trustworthiness and safety of such devices. Unfortunately, the suggested architectures did not consider such problem. In this paper we present a novel self-configuring fog architecture to support IoT networks with security and trust in mind. We realize the concept of Moving-target defense by mobilizing the applications inside the fog using live migrations. Performance evaluations using a benchmark for mobilized applications showed that the added overhead of live migrations is very small making it deployable in real scenarios. Finally, we presented a mathematical model to estimate the survival probabilities of both static and mobile applications within the fog. Moreover, this work can be extended to other systems such as mobile ad-hoc networks (MANETS) or in vehicular cloud computing (VCC).

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.