Biblio

The Internet of Things poses some of the biggest security challenges in the present day. Companies, users and infrastructures are constantly under attack by malicious actors. Increasingly, attacks are being launched by hacking into one vulnerable device and hence disabling entire networks resulting in great loss. A strong identity management framework can help better protect these devices by issuing a unique identity and managing the same through its lifecycle. Identity of Things (IDoT) is a term that has been used to describe the importance of device identities in IoT networks. Since the traditional identity and access management (IAM) solutions are inadequate in managing identities for IoT, the Identity of Things (IDoT) is emerging as the solution for issuance of Identities to every type of device within the IoT IAM infrastructure. This paper presents the survey of recent research works proposed in the area of device identities and various commercial solutions offered by organizations specializing in IoT device security.

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

Big Data (BD) is the combination of several technologies which address the gathering, analyzing and storing of massive heterogeneous data. The tremendous spurt of the Internet of Things (IoT) and different technologies are the fundamental incentive behind this enduring development. Moreover, the analysis of this data requires high-performance servers for advanced and parallel data analytics. Thus, data owners with their limited capabilities may outsource their data to a powerful but untrusted environment, i.e., the Cloud. Furthermore, data analytic techniques performed on external cloud may arise various security intimidations regarding the confidentiality and the integrity of the aforementioned; transferred, analyzed, and stored data. To countermeasure these security issues and challenges, several techniques have been addressed. This survey paper aims to summarize and emphasize the security threats within Big Data framework, in addition, it is worth mentioning research work related to Big Data Analytics (BDA).

Recently, Cloud Computing became one of today’s great innovations for provisioning Information Technology (IT) resources. Moreover, a new model has been introduced named Fog Computing, which addresses Cloud Computing paradigm issues regarding time delay and high cost. However, security challenges are still a big concern about the vulnerabilities to both Cloud and Fog Computing systems. Man- in- the- Middle (MITM) is considered one of the most destructive attacks in a Fog Computing context. Moreover, it’s very complex to detect MiTM attacks as it is performed passively at the Software-Defined Networking (SDN) level, also the Fog Computing paradigm is ideally suitable for MITM attacks. In this paper, a MITM mitigation scheme will be proposed consisting of an SDN network (Fog Leaders) which controls a layer of Fog Nodes. Furthermore, Multi-Path TCP (MPTCP) has been used between all edge devices and Fog Nodes to improve resource utilization and security. The proposed solution performance evaluation has been carried out in a simulation environment using Mininet, Ryu SDN controller and Multipath TCP (MPTCP) Linux kernel. The experimental results showed that the proposed solution improves security, network resiliency and resource utilization without any significant overheads compared to the traditional TCP implementation.

Smart building security systems typically consist of sensors and controllers that monitor power operating systems, alarms, camera monitoring, access controls, and many other important information and security systems. These systems are managed and controlled through online platforms. A successful attack on one of these platforms may result in the failure of one or more critical intelligent systems in the building. In this paper, the security requirements in the application layer of any IoT system were discussed, in particular the role of IoT platforms in dealing with the security problems that smart buildings are exposed to and the extent of their strength to reduce the attacks they are exposed to, where an experimental platform was designed to test the presence of security vulnerabilities and This was done by using the Zed Attack Proxy (ZAP) tool, according to the OWASP standards and security level assessment, and the importance of this paper comes as a contribution to providing information about the most famous IoT platforms and stimulating work to explore security concerns in IoT-based platforms.

The latest generation of IoT systems incorporate machine learning (ML) technologies on edge devices. This introduces new engineering challenges to bring ML onto resource-constrained hardware, and complications for ensuring system security and privacy. Existing research prescribes iterative processes for machine learning enabled IoT products to ease development and increase product success. However, these processes mostly focus on existing practices used in other generic software development areas and are not specialized for the purpose of machine learning or IoT devices. This research seeks to characterize engineering processes and security practices for ML-enabled IoT systems through the lens of the engineering lifecycle. We collected data from practitioners through a survey (N=25) and interviews (N=4). We found that security processes and engineering methods vary by company. Respondents emphasized the engineering cost of security analysis and threat modeling, and trade-offs with business needs. Engineers reduce their security investment if it is not an explicit requirement. The threats of IP theft and reverse engineering were a consistent concern among practitioners when deploying ML for IoT devices. Based on our findings, we recommend further research into understanding engineering cost, compliance, and security trade-offs.

Even as Internet of Things (IoT) network security grows, concerns about the security of IoT devices have arisen. Although a few companies produce IP-connected gadgets for such ranging from small office, their security policies and implementations are often weak. They also require firmware updates or revisions to boost security and reduce vulnerabilities in equipment. A brownfield advance is necessary to verify systems where these helpless devices are present: putting in place basic security mechanisms within the system to render the system powerless possibly. Gadgets should cohabit without threatening their security in the same device. IoT network security has evolved into a platform that can segregate a large number of IoT devices, allowing law enforcement to compel the communication of defenseless devices in order to reduce the damage done by its unlawful transaction. IoT network security appears to be doable in well-known gadget types and can be deployed with minimum transparency.

IoT technology is finding new applications every day and everywhere in our daily lives. With that, come new use cases with new challenges in terms of device and data security. One of such challenges arises from the fact that many IoT devices/nodes are no longer being deployed on owners' premises, but rather on public or private property other than the owner's. With potential physical access to the IoT node, adversaries can launch many attacks that circumvent conventional protection methods. In this paper, we propose Secure SoC (SecSoC), a secure system-on-chip architecture that mitigates such attacks. This include logical memory dump attacks, bus snooping attacks, and compromised operating systems. SecSoC relies on two main mechanisms, (1) providing security extensions to the compute engine that runs the user application without changing its instruction set, (2) adding a security management unit (SMU) that provide HW security primitives for encryption, hashing, random number generators, and secrets store (keys, certificates, etc.). SecSoC ensures that no secret or sensitive data can leave the SoC IC in plaintext. SecSoC is being implemented in Bluespec System V erilog. The experimental results will reveal the area, power, and cycle time overhead of these security extensions. Overall performance (total execution time) will also be evaluated using IoT benchmarks.

Recently, as the use of Internet of Things (IoT) devices has expanded, security issues have emerged. As a solution to the IoT security problem, PUF (Physical Unclonable Function) technology has been proposed, and research on key generation or device authentication using it has been actively conducted. In this paper, we propose a method to apply PUF-based device authentication technology to the Open Connectivity Foundation (OCF) open platform. The proposed method can greatly improve the security level of IoT open platform by utilizing PUF technology.

While 5G Edge Computing along with IoT technology has transformed the future of healthcare data transmission, it presents security vulnerabilities and risks when transmitting patients' confidential information. Currently, there are very few reliable security solutions available for healthcare data that routes through SDN routers in 5G Edge Computing. These solutions do not provide cryptographic security from IoT sensor devices. In this paper, we studied how 5G edge computing integrated with IoT network helps healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, and finally proposed a cryptographic end-to-end security solution initiated at IoT sensor devices and routed through SDN routers. Our proposed solution with cryptographic security initiated at IoT sensor goes through SDN control plane and data plane in 5G edge computing and provides an end-to-end secured communication from IoT device to doctor's office. A prototype built with two-layer encrypted communication has been lab tested with promising results. This analysis will help future security implementation for eHealth in 5G and beyond networks.

In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.

In order to solve the problem of untargeted data security grading methods in the process of power grid data governance, this paper analyzes the mainstream data security grading standards at home and abroad, investigates and sorts out the characteristics of power grid data security grading requirements, and proposes a method that considers national, social, and A grid data security classification scheme for the security impact of four dimensions of individuals and enterprises. The plan determines the principle of power grid data security classification. Based on the basic idea of “who will be affected to what extent and to what extent when the power grid data security is damaged”, it defines three classification factors that need to be considered: the degree of impact, the scope of influence, and the objects of influence, and the power grid data is divided into five security levels. In the operation stage of power grid data security grading, this paper sorts out the experience and gives the recommended grading process. This scheme basically conforms to the status quo of power grid data classification, and lays the foundation for power grid data governance.

With the growing number of IoT applications and devices, IoT security breaches are a dangerous reality. Cost pressure and complexity of security tests for embedded systems and networked infrastructure are often the excuse for skipping them completely. In our paper we introduce SecLab security test lab to overcome that problem. Based on a flexible and lightweight architecture, SecLab allows developers and IoT security specialists to harden their systems with a low entry hurdle. The open architecture supports the reuse of existing external security test libraries and scalability for the assessment of complex IoT Systems. A reference implementation of security tests in a realistic IoT application scenario proves the approach.

The Internet of Things (IoT) is advancing technology by creating smart surroundings that make it easier for humans to do their work. This technological advancement not only improves human life and expands economic opportunities, but also allows intruders or attackers to discover and exploit numerous methods in order to circumvent the security of IoT networks. Hence, security and privacy are the key concerns to the IoT networks. It is vital to protect computer and IoT networks from many sorts of anomalies and attacks. Traditional intrusion detection systems (IDS) collect and employ large amounts of data with irrelevant and inappropriate attributes to train machine learning models, resulting in long detection times and a high rate of misclassification. This research presents an advance approach for the design of IDS for IoT networks based on the Particle Swarm Optimization Algorithm (PSO) for feature selection and the Extreme Gradient Boosting (XGB) model for PSO fitness function. The classifier utilized in the intrusion detection process is Random Forest (RF). The IoTID20 is being utilized to evaluate the efficacy and robustness of our suggested strategy. The proposed system attains the following level of accuracy on the IoTID20 dataset for different levels of classification: Binary classification 98 %, multiclass classification 83 %. The results indicate that the proposed framework effectively detects cyber threats and improves the security of IoT networks.

Consumer IoT devices may suffer malware attacks, and be recruited into botnets or worse. There is evidence that generic advice to device owners to address IoT malware can be successful, but this does not account for emerging forms of persistent IoT malware. Less is known about persistent malware, which resides on persistent storage, requiring targeted manual effort to remove it. This paper presents a field study on the removal of persistent IoT malware by consumers. We partnered with an ISP to contrast remediation times of 760 customers across three malware categories: Windows malware, non-persistent IoT malware, and persistent IoT malware. We also contacted ISP customers identified as having persistent IoT malware on their network-attached storage devices, specifically QSnatch. We found that persistent IoT malware exhibits a mean infection duration many times higher than Windows or Mirai malware; QSnatch has a survival probability of 30% after 180 days, whereby most if not all other observed malware types have been removed. For interviewed device users, QSnatch infections lasted longer, so are apparently more difficult to get rid of, yet participants did not report experiencing difficulty in following notification instructions. We see two factors driving this paradoxical finding: First, most users reported having high technical competency. Also, we found evidence of planning behavior for these tasks and the need for multiple notifications. Our findings demonstrate the critical nature of interventions from outside for persistent malware, since automatic scan of an AV tool or a power cycle, like we are used to for Windows malware and Mirai infections, will not solve persistent IoT malware infections.

This paper investigates what cues people use to spot a phishing email when the email is spoken back to them by the Alexa voice assistant, instead of read on a screen. We configured Alexa to read there emails to a sample of 52 participants and ask for their phishing evaluations. We also asked a control group of another 52 participants to evaluate these emails on a regular screen to compare the plausibility of phishing pretexting in voice assistant environments. The results suggest that Alexa can be used for pretexting users that lack phishing awareness to receive and act upon a relatively urgent email from an authoritative sender. Inspecting the sender (authority cue”) and relying on their personal experiences helped participants with higher phishing awareness to use Alexa towards a preliminary email screening to flag an email as potentially “phishing.”

Recently, information leakage accidents have been continuously occurring due to cyberattacks, and internal information leakage has also been occurring additionally. In this situation, many hacking accidents and DDoS attacks related to IoT are reported, and cyber threat detection field is expanding. Therefore, in this study, the trend related to the commercialization and generalization of IoT technology and the degree of standardization of IoT have been analyzed. Based on the reality of IoT analyzed through this process, research and analysis on what points are required in IoT security control was conducted, and then IoT security control strategy was presented. In this strategy, the IoT environment was divided into IoT device, IoT network/communication, and IoT service/platform in line with the basic strategic framework of 'Pre-response-accident response-post-response', and the strategic direction of security control was established suitable for each of them.

To bring Internet-grade security to billions of IoT devices and make them first-class Internet citizens, IoT devices must move away from pre-shared keys to digital certificates. Public Key Infrastructure, PKI, the digital certificate management solution on the Internet, is inevitable to bring certificate-based security to IoT. Recent research efforts has shown the feasibility of PKI for IoT using Internet security protocols. New and proposed standards enable IoT devices to implement more lightweight solutions for application layer security, offering real end-to-end security also in the presence of proxies.In this paper we present LICE, an application layer enrollment protocol for IoT, an important missing piece before certificate-based security can be used with new IoT standards such as OSCORE and EDHOC. Using LICE, enrollment operations can complete by consuming less than 800 bytes of data, less than a third of the corresponding operations using state-of-art EST-coaps over DTLS. To show the feasibility of our solution, we implement and evaluate the protocol on real IoT hardware in a lossy low-power radio network environment.

This paper deals with the different security issues and their probable solution related to the Internet of things (IoT). We firstly examine and found out the basic possible threats and security attacks in IoT. As we all are familiar with the fact that IoT had its impact in today’s era. We are very much dependent on smart technologies these days. Security is always an immense challenge in the IoT domain. We had tried to focus on some of the most common possible attacks and also examined the layer of the system model of IoT in which it had happened. In the later section of the paper, we had proposed all the possible solutions for the issues and attacks. This work will be used for giving some possible solutions for the attacks in different layers and we can stop them at the earliest.

With the development of information technology, the Internet of things (IOT) has gradually become the third wave of global information industry revolution after computer and Internet. Artificial intelligence (AI) and IOT technology is an important prerequisite for the rapid development of the current information society. However, while AI and IOT technologies bring convenient and intelligent services to people, they also have many defects and imperfect development. Therefore, it is necessary to pay more attention to the development of AI and IOT technologies, actively improve the application system, and create a network security management system for AI and IOT applications that can timely detect intrusion, assess risk and prevent viruses. In this paper, the network security risks caused by AI and IOT applications are analyzed. Therefore, in order to ensure the security of IOT environment, network security and privacy security have become the primary problems to be solved, and management should be strengthened from technical to legal aspects.

Recently, it is predicted that interworking between heterogeneous devices will be accelerated due to the openness of the IoT (Internet of Things) platform, but various security threats are also expected to increase. However, most IoT open platforms remain at the level that utilizes existing security technologies. Therefore, a more secure security technology is required to prevent illegal copying and leakage of important data through stealing, theft, and hacking of IoT devices. In addition, a technique capable of ensuring interoperability with existing standard technologies is required. This paper proposes an IoT device authentication method based on PUF (Physical Unclonable Function) that operates on an IoT open platform. By utilizing PUF technology, the proposed method can effectively respond to the threat of exposure of the authentication key of the existing IoT open platform. Above all, the proposed method can contribute to compatibility and interoperability with existing technologies by providing a device authentication method that can be effectively applied to the OCF Iotivity standard specification, which is a representative IoT open platform.

In the security platform of Internet of Things (IoT), a licensed Low Power Wide Area Network (LPWAN) technology, named Narrowband Internet of Things (NB-IoT) is playing a vital role in transferring the information between objects. This technology is preferable for applications having a low data rate. As the number of subscribers increases, attack possibilities raise simultaneously. So securing the transmission between the objects becomes a big task. Bandwidth spoofing is one of the most sensitive attack that can be performed on the communication channel that lies between the access point and user equipment. This research proposal objective is to secure the system from the attack based on Unmanned Aerial vehicles (UAVs) enabled Small Cell Access (SCA) device which acts as an intruder between the user and valid SCA and investigating the scenario when any intruder device comes within the communication range of the NB-IoT enabled device. Here, this article also proposed a mathematical solution for the proposed scenario.

Modern IoT infrastructure consists of different sub-systems, devices, applications, platforms, varied connectivity protocols with distinct operating environments scattered across different subsystems within the whole network. Each of these subsystems of the global system has its peculiar computational and security challenges. A security loophole in one subsystem has a directly negative impact on the security of the whole system. The nature and intensity of recent cyber-attacks within IoT networks have increased in recent times. Blockchain technology promises several security benefits including a decentralized authentication mechanism that addresses almost readily the challenges with a centralized authentication mechanism that has the challenges of introducing a single point of failure that affects data and system availability anytime such systems are compromised. The different design specifications and the unique functional requirements for most IoT devices require a strong yet universal authentication mechanism for multimedia data that assures an additional security layer to IoT data. In this paper, the authors propose a decentralized authentication to validate data integrity at the IoT node level. The proposed mechanism guarantees integrity, privacy, and availability of IoT node data.

In the Internet of Things, it is more important than ever to effectively address the problem of secure transmission based on steganographic substitution by synthesizing digital sensor data. In this case, the degree to which the grayscale message is obscured is a necessary issue. To ensure information security in IoT systems, various methods are used and information security problems are solved to one degree or another. The article proposes a method and algorithm for a computer image in grayscale, in which the value of each pixel is one sample, representing the amount of light, carrying only information about the intensity. The proposed method in grayscale using steganographic coding provides a secure implementation of data transmission in the IoT system. Study results were analyzed using PSNR (Peak Signal to Noise Ratio).