Biblio

The present industrial scenario requires frequent transfer of data between remote servers and on premise devices and hence the risk of attacks on these data cannot be overlooked. Such security risk is even aggravated in case of sensitive information being compromised due to inefficient security implementations. Various forms of security implementations are being discussed and experimented for the same. With the introduction of devices with better processing capabilities, Public Key Infrastructure is a very popular technique being widely implemented, wherein symmetric and asymmetric key based encryptions are used inorder to secure the data being transferred and it has proven to be an effective technique. The PKI however suffers certain drawbacks and it is evident from the attacks. A system specifically designed for scenarios such as a factory having a centralised device management system requiring multiple devices to communicate and upload data safely to server is being put forward in this paper.

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.

Low-Power and Lossy Networks (LLNs) run on resource-constrained devices and play a key role in many Industrial Internet of Things and Cyber-Physical Systems based applications. But, achieving an energy-efficient routing in LLNs is a major challenge nowadays. This challenge is addressed by Routing Protocol for Low-power Lossy Networks (RPL), which is specified in RFC 6550 as a “Proposed Standard” at present. In RPL, a client node uses Destination Advertisement Object (DAO) control messages to pass on the destination information towards the root node. An attacker may exploit the DAO sending mechanism of RPL to perform a DAO Insider attack in LLNs. In this paper, it is shown that an aggressive attacker can drastically degrade the network performance. To address DAO Insider attack, a lightweight defense solution is proposed. The proposed solution uses an early blacklisting strategy to significantly mitigate the attack and restore RPL performance. The proposed solution is implemented and tested on Cooja Simulator.

Maintaining critical data and process availability is an important challenge of Industry 4.0. Given the variety of smart nodes, data and the access latency that can be tolerated by consumers in modern IoT-based industry, we propose a method for analyzing the resiliency of an IoT network. Due to the complexity of modern system structures, different components in the system can affect the system’s resiliency. Therefore, a fundamental problem is to propose methods to quantify the value of resilience contribution of a node in each system effectively. This paper aims to identify the most critical vertices of the network with respect to the latency constraint resiliency metric. Using important centrality metrics, we identify critical nodes in industrial IoT networks to analyze the degree of resiliency in the IoT environments. The results show that when nodes with the highest value of Closeness Centrality (CC) were disrupted Resiliency of Latency (RL) would have the lowest value. In other words, the results indicate the nodes with the high values for CC are most critical in an IoT network.

Protocol parsing is to discern and analyze packets' transmission fields, which plays an essential role in industrial security monitoring. The existing schemes parsing industrial protocols universally have problems, such as the limited parsing protocols, poor scalability, and high preliminary information requirements. This paper proposes a text similarity-based protocol parsing scheme (TPP) to identify and parse protocols for Industrial Internet of Things. TPP works in two stages, template generation and protocol parsing. In the template generation stage, TPP extracts protocol templates from protocol data packets by the cluster center extraction algorithm. The protocol templates will update continuously with the increase of the parsing packets' protocol types and quantities. In the protocol parsing phase, the protocol data packet will match the template according to the similarity measurement rules to identify and parse the fields of protocols. The similarity measurement method comprehensively measures the similarity between messages in terms of character position, sequence, and continuity to improve protocol parsing accuracy. We have implemented TPP in a smart industrial gateway and parsed more than 30 industrial protocols, including POWERLINK, DNP3, S7comm, Modbus-TCP, etc. We evaluate the performance of TPP by comparing it with the popular protocol analysis tool Netzob. The experimental results show that the accuracy of TPP is more than 20% higher than Netzob on average in industrial protocol identification and parsing.

The Internet of Things (IoT) provides significant benefits for industry due to connect the devices together through the internet. Attribute-Based Encryption (ABE) is a technique can enforce an access control over data to guarantee the data security. In this paper, we propose an ABE scheme for data in industrial IoT. The scheme achieves both security and high performance. When there is a shared subpolicy among the access policies of a sensor, the scheme optimizes the encryption of the messages. Through analysis and simulation, we show that our solution is security and efficient.

Industrial IoT (IIoT) is a specialized subset of IoT which involves the interconnection of industrial devices with ubiquitous control and intelligent processing services to improve industrial system's productivity and operational capability. In essence, IIoT adapts a use-case specific architecture based on RFID sense network, BLE sense network or WSN, where heterogeneous industrial IoT devices can collaborate with each other to achieve a common goal. Nonetheless, most of the IIoT deployments are brownfield in nature which involves both new and legacy technologies (SCADA (Supervisory Control and Data Acquisition System)). The merger of these technologies causes high degree of cross-linking and decentralization which ultimately increases the complexity of IIoT systems and introduce new vulnerabilities. Hence, industrial organizations becomes not only vulnerable to conventional SCADA attacks but also to a multitude of IIoT specific threats. However, there is a lack of understanding of these attacks both with respect to the literature and empirical evaluation. As a consequence, it is infeasible for industrial organizations, researchers and developers to analyze attacks and derive a robust security mechanism for IIoT. In this paper, we developed a multi-layer taxonomy of IIoT attacks by considering both brownfield and greenfield architecture of IIoT. The taxonomy consists of 11 layers 94 dimensions and approximately 100 attack techniques which helps to provide a holistic overview of the incident attack pattern, attack characteristics and impact on industrial system. Subsequently, we have exhibited the practical relevance of developed taxonomy by applying it to a real-world use-case. This research will benefit researchers and developers to best utilize developed taxonomy for analyzing attack sequence and to envisage an efficient security platform for futuristic IIoT applications.

The upsurge of Industrial Internet of Things is forcing industrial information systems to enable less hierarchical information flow. The connections between humans, devices, and their digital twins are growing in numbers, creating a need for new kind of security and trust solutions. To address these needs, industries are applying distributed ledger technologies, aka blockchains. A significant number of use cases have been studied in the sectors of logistics, energy markets, smart grid security, and food safety, with frequently reported benefits in transparency, reduced costs, and disintermediation. However, distributed ledger technologies have challenges with transaction throughput, latency, and resource requirements, which render the technology unusable in many cases, particularly with constrained Internet of Things devices.To overcome these challenges within the Industrial Internet of Things, we suggest a set of interledger approaches that enable trusted information exchange across different ledgers and constrained devices. With these approaches, the technically most suitable ledger technology can be selected for each use case while simultaneously enjoying the benefits of the most widespread ledger implementations. We present state of the art for distributed ledger technologies to support the use of interledger approaches in industrial settings.

The Internet of Things (IoT) is a vast collection of interconnected sensors, devices, and services that share data and information over the Internet with the objective of leveraging multiple information sources to optimize related systems. The technologies associated with the IoT have significantly improved the quality of many existing applications by reducing costs, improving functionality, increasing access to resources, and enhancing automation. The adoption of IoT by industries has led to the next industrial revolution: Industry 4.0. The rise of the Industrial IoT (IIoT) promises to enhance factory management, process optimization, worker safety, and more. However, the rollout of the IIoT is not without significant issues, and many of these act as major barriers that prevent fully achieving the vision of Industry 4.0. One major area of concern is the security and privacy of the massive datasets that are captured and stored, which may leak information about intellectual property, trade secrets, and other competitive knowledge. As a way forward toward solving security and privacy concerns, we aim in this paper to identify common input-output (I/O) design patterns that exist in applications of the IIoT. These design patterns enable constructing an abstract model representation of data flow semantics used by such applications, and therefore better understand how to secure the information related to IIoT operations. In this paper, we describe communication protocols and identify common I/O design patterns for IIoT applications with an emphasis on data flow in edge devices, which, in the industrial control system (ICS) setting, are most often involved in process control or monitoring.

Industrial Control systems traditionally achieved security by using proprietary protocols to communicate in an isolated environment from the outside. This paradigm is changed with the advent of the Industrial Internet of Things that foresees flexible and interconnected systems. In this contribution, a device acting as a connection between the operational technology network and information technology network is proposed. The device is an intrusion detection system related to legacy systems that is able to collect and reporting data to and from industrial IoT devices. It is based on the common signature based intrusion detection system developed in the information technology domain, however, to cope with the constraints of the operation technology domain, it exploits anomaly based features. Specifically, it is able to analyze the traffic on the network at application layer by mean of deep packet inspection, parsing the information carried by the proprietary protocols. At a later stage, it collect and aggregate data from and to IoT domain. A simple set up is considered to prove the effectiveness of the approach.

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Internet of Things (IoT) revolution in 5th Generation (5G) will dynamically support all user, devices and customer worldwide where these devices, mechanical and digital machines will be connected and are able to communicate and transfer data over the network. In industries, the evolution of these technologies, known as Industrial IoT (IIoT) will enable machines to be connected and communicate where else, Internet of Everything (IoE) makes the connection more relevant between all smart devices, machines and also people with a huge data, high speed and high security. The growth of these technologies has made Radio Frequency (RF) spectrum resources for wireless communication to be more saturated. In order to solve this problem, new wireless communication technologies are proposed to meet the demand and also to enhance the performance of the system and overcome the existing bandwidth limitations. Studies done shows that Light-Fidelity (Li-Fi), based on Visible Light Communications (VLC) is one of the most promising technology in future which is based on optical wireless communication. Initial study on the Li-Fi concept has focuses on achieving speed, bi-directional transmission concept and supports multiuser access. In this paper we propose a frame work focuses on the handover process for indoor environment by using the steerable Access Point (AP) and compare the output result with fix Access Point.

The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.

In industrial internet of things, various devices are connected to external internet. For the connected devices, the authentication is very important in the viewpoint of security; therefore, physical unclonable functions (PUFs) have attracted attention as authentication techniques. On the other hand, the risk of modeling attacks on PUFs, which clone the function of PUFs mathematically, is pointed out. Therefore, a resistant-PUF such as a lightweight PUF has been proposed. However, new analytical methods (side-channel attacks: SCAs), which use side-channel information such as power or electromagnetic waves, have been proposed. The countermeasure method has also been proposed; however, an evaluation using actual devices has not been studied. Since PUFs use small production variations, the implementation evaluation is very important. Therefore, this study proposes a SCA countermeasure of the lightweight PUF. The proposed method is based on the previous studies, and maintains power consumption consistency during the generation of response. In experiments using a field programmable gate array, the measured power consumption was constant regardless of output values of the PUF could be confirmed. Then, experimental results showed that the predicted rate of the response was about 50 %, and the proposed method had a tamper resistance against SCAs.

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

The server is an important for storing data, collected during the diagnostics of Smart Business Center (SBC) as a subsystem of Industrial Internet of Things including sensors, network equipment, components for start and storage of monitoring programs and technical diagnostics. The server is exposed most often to various kind of attacks, in particular, aimed at processor, interface system, random access memory. The goal of the paper is analyzing the methods of the SBC server protection from malicious actions, as well as the development and investigation of the Markov model of the server's functioning in the SBC network, taking into account the impact of DDoS-attacks.

Industrial Internet of Things (IIoT) is a trend of the smart industry. By collecting field data from sensors, the industry can make decisions dynamically in time for better performance. In most cases, IIoT is built on private networks and cannot be reached from the Internet. Currently, data transmission in most of IIoT network protocols is in plaintext without encryption protection. Once an attacker breaks into the field, the attacker can intercept data and injects malicious commands to field agents. In this paper, we propose a compound approach for defending command injection attacks in IIOT. First, we leverage the power of Software Defined Networking (SDN) to detect the injection attack. When the injection attack event is detected, the system owner is alarmed that someone tries to pretend a controller or a field agent to deceive the other entity. Second, we develop a lightweight authentication scheme to ensure the identity of the command sender. Command receiver can verify commands first before processing commands.

Today's emerging Industrial Internet of Things (IIoT) scenarios are characterized by the exchange of data between services across enterprises. Traditional access and usage control mechanisms are only able to determine if data may be used by a subject, but lack an understanding of how it may be used. The ability to control the way how data is processed is however crucial for enterprises to guarantee (and provide evidence of) compliant processing of critical data, as well as for users who need to control if their private data may be analyzed or linked with additional information - a major concern in IoT applications processing personal information. In this paper, we introduce LUCON, a data-centric security policy framework for distributed systems that considers data flows by controlling how messages may be routed across services and how they are combined and processed. LUCON policies prevent information leaks, bind data usage to obligations, and enforce data flows across services. Policy enforcement is based on a dynamic taint analysis at runtime and an upfront static verification of message routes against policies. We discuss the semantics of these two complementing enforcement models and illustrate how LUCON policies are compiled from a simple policy language into a first-order logic representation. We demonstrate the practical application of LUCON in a real-world IoT middleware and discuss its integration into Apache Camel. Finally, we evaluate the runtime impact of LUCON and discuss performance and scalability aspects.

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.

All over the world, objects are increasingly connected in networks such as the Industrial Internet of Things. Interconnections, intercommunications and interactions are driving the development of an entirely new whole in the form of the Industrial Internet of Things. Communication and interaction are the norm both for separate components, such as cyber-physical systems, and for the functioning of the system as a whole. This new whole can be likened to a natural ecosystem where the process of homeostasis ensures the stability and security of the whole. Components of such an industrial ecosystem, or even an industrial ecosystem as a whole, are increasingly targeted by cyber attacks. Such attacks not only threaten the functioning of one or multiple components, they also constitute a threat to the functioning of the new whole. General systems theory can offer a scientific framework for the development of measures to improve the security and stability of both separate components and the new whole.