Biblio

Technical debt is an analogy introduced in 1992 by Cunningham to help explain how intentional decisions not to follow a gold standard or best practice in order to save time or effort during creation of software can later on lead to a product of lower quality in terms of product quality itself, reliability, maintainability or extensibility. Little work has been done so far that applies this analogy to cyber physical (production) systems (CP(P)S). Also there is only little work that uses this analogy for security related issues. This work aims to fill this gap: We want to find out which security related symptoms within the field of cyber physical production systems can be traced back to TD items during all phases, from requirements and design down to maintenance and operation. This work shall support experts from the field by being a first step in exploring the relationship between not following security best practices and concrete increase of costs due to TD as consequence.

As the connectivity within manufacturing processes increases in light of Industry 4.0, information security becomes a pressing issue for product suppliers, systems integrators, and asset owners. Reaching new heights in digitizing the manufacturing industry also provides more targets for cyber attacks, hence, cyber-physical production systems (CPPSs) must be adequately secured to prevent malicious acts. To achieve a sufficient level of security, proper defense mechanisms must be integrated already early on in the systems' lifecycle and not just eventually in the operation phase. Although standardization efforts exist with the objective of guiding involved stakeholders toward the establishment of a holistic industrial security concept (e.g., IEC 62443), a dedicated security development lifecycle for systems integrators is missing. This represents a major challenge for engineers who lack sufficient information security knowledge, as they may not be able to identify security-related activities that can be performed along the production systems engineering (PSE) process. In this paper, we propose a novel methodology named Security Development Lifecycle for Cyber-Physical Production Systems (SDL-CPPS) that aims to foster security by design for CPPSs, i.e., the engineering of smart production systems with security in mind. More specifically, we derive security-related activities based on (i) security standards and guidelines, and (ii) relevant literature, leading to a security-improved PSE process that can be implemented by systems integrators. Furthermore, this paper informs domain experts on how they can conduct these security-enhancing activities and provides pointers to relevant works that may fill the potential knowledge gap. Finally, we review the proposed approach by means of discussions in a workshop setting with technical managers of an Austrian-based systems integrator to identify barriers to adopting the SDL-CPPS.

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.

In this work, we use a subjective approach to compute cyber resilience metrics for industrial control systems. We utilize the extended form of the R4 resilience framework and span the metrics over physical, technical, and organizational domains of resilience. We develop a qualitative cyber resilience assessment tool using the framework and a subjective questionnaire method. We make sure the questionnaires are realistic, balanced, and pertinent to ICS by involving subject matter experts into the process and following security guidelines and standards practices. We provide detail mathematical explanation of the resilience computation procedure. We discuss several usages of the qualitative tool by generating simulation results. We provide a system architecture of the simulation engine and the validation of the tool. We think the qualitative simulation tool would give useful insights for industrial control systems' overall resilience assessment and security analysis.

Manufacturing enterprises are constantly exploring new ways to improve their own production processes to address the increasing demand of customized production. However, such enterprises show a low degree of flexibility, which mainly results from the need to configure new production equipment at design and run time. In this paper we propose self-adaptation as an approach to improve data transmission flexibility in Industry 4.0 environments. We implement an autonomic manager using a generic autonomic management framework, which applies the most appropriate data transmission configuration based on security and business process related requirements, such as performance. The experimental evaluation is carried out in a MQTT infrastructure and the results show that using self-adaptation can significantly improve the trade-off between security and performance. We then propose to integrate anomaly detection methods as a solution to support self-adaptation by monitoring and learning the normal behavior of an industrial system and show how this can be used by the generic autonomic management framework.

In today's IIoT world, most of the IoT platform providers like Microsoft, Amazon and Google are focused towards connecting devices and extract data from the devices and send the data to the Cloud for analytics. Only there are few companies concentrating on Security measures implemented on Edge Node. Gartner estimates that by 2020, more than 25 percent of all enterprise attackers will make use of the Industrial IoT. As Cyber Security Threat is getting more important, it is essential to ensure protection of data both at rest and at motion. The reflex of Cyber Security in the Industrial IoT Domain is much more severe when compared to the Consumer IoT Segment. The new bottleneck in this are security services which employ computationally intensive software operations and system services [1]. Resilient services consume considerable resources in a design. When such measures are added to thwart security attacks, the resource requirements grow even more demanding. Since the standard IIoT Gateways and other sub devices are resource constrained in nature the conventional design for security services will not be applicable in this case. This paper proposes an intelligent architectural paradigm for the Constrained IIoT Gateways that can efficiently identify the Cyber-Attacks in the Industrial IoT domain.

With the globalization of manufacturing and supply chains, ensuring the security and trustworthiness of ICs has become an urgent challenge. Split manufacturing (SM) and layout camouflaging (LC) are promising techniques to protect the intellectual property (IP) of ICs from malicious entities during and after manufacturing (i.e., from untrusted foundries and reverse-engineering by end-users). In this paper, we strive for “the best of both worlds,” that is of SM and LC. To do so, we extend both techniques towards 3D integration, an up-and-coming design and manufacturing paradigm based on stacking and interconnecting of multiple chips/dies/tiers. Initially, we review prior art and their limitations. We also put forward a novel, practical threat model of IP piracy which is in line with the business models of present-day design houses. Next, we discuss how 3D integration is a naturally strong match to combine SM and LC. We propose a security-driven CAD and manufacturing flow for face-to-face (F2F) 3D ICs, along with obfuscation of interconnects. Based on this CAD flow, we conduct comprehensive experiments on DRC-clean layouts. Strengthened by an extensive security analysis (also based on a novel attack to recover obfuscated F2F interconnects), we argue that entering the next, third dimension is eminent for effective and efficient IP protection.

Within “Industrie 4.0” approach 3D printing technology is characterized as one of the disruptive innovations. Conventional supply chains are replaced by value-added networks. The spatially distributed development of printed components, e.g. for the rapid delivery of spare parts, creates a new challenge when differentiating between “original part”, “copy” or “counterfeit” becomes necessary. This is especially true for safety-critical products. Based on these changes classic branded products adopt the characteristics of licensing models as we know them in the areas of software and digital media. This paper describes the use of digital rights management as a key technology for the successful transition to Additive Manufacturing methods and a key for its commercial implementation and the prevention of intellectual property theft. Risks will be identified along the process chain and solution concepts are presented. These are currently being developed by an 8-partner project named SAMPL (Secure Additive Manufacturing Platform).

With the increase in the number of cyber attacks on industrial control systems, especially in critical infrastructure facilities, the problem of comprehensive analysis of the security of such systems becomes urgent. This, in turn, requires the availability of fundamental mathematical, methodological and instrumental basis for modeling automated systems, modeling attacks on their information resources, which would allow realtime system protection analysis. The paper proposes a basis for simulating protected industrial control systems, based on the developed reference security model, and a model for attacks on information resources of automated systems. On the basis of these mathematical models, a complex model of a protected automated system was developed, which can be used to build protection systems for automated systems used in production.

The authors carry out the analysis of the process of modeling threats to information security of automated process control systems. Basic principles of security threats model formation are considered. The approach to protection of automated process control systems based on the Shtakelberg game in a strategic form was modeled. An abstract mathematical model of information security threats to automated process control systems was developed. A formalized representation of a threat model is described, taking into account an intruder's potential. Presentation of the process of applying the described threat model in the form of a continuous Deming-Shewhart cycle is proposed.

The intelligent production line is a complex application with a large number of independent equipment network integration. In view of the characteristics of CPS, the existing modeling methods cannot well meet the application requirements of large scale high-performance system. a formal simulation verification framework and verification method are designed for the performance constraints such as the real-time and security of the intelligent production line based on soft bus. A model-based service-oriented integration approach is employed, which adopts a model-centric way to automate the development course of the entire software life cycle. Developing experience indicate that the proposed approach based on the formal modeling and verification framework in this paper can improve the performance of the system, which is also helpful to achieve the balance of the production line and maintain the reasonable use rate of the processing equipment.

Modern cybercrimes have exponentially grown over the last one decade. Ransomware is one of the types of malware which is the result of sophisticated attempt to compromise the modern computer systems. The governments and large corporations are investing heavily to combat this cyber threat against their critical infrastructure. It has been observed that over the last few years that Industrial Control Systems (ICS) have become the main target of Ransomware due to the sensitive operations involved in the day to day processes of these industries. As the technology is evolving, more and more traditional industrial systems are replaced with advanced industry methods involving advanced technologies such as Internet of Things (IoT). These technology shift help improve business productivity and keep the company's global competitive in an overflowing competitive market. However, the systems involved need secure measures to protect integrity and availability which will help avoid any malfunctioning to their operations due to the cyber-attacks. There have been several cyber-attack incidents on healthcare, pharmaceutical, water cleaning and energy sector. These ICS' s are operated by remote control facilities and variety of other devices such as programmable logic controllers (PLC) and sensors to make a network. Cyber criminals are exploring vulnerabilities in the design of these ICS's to take the command and control of these systems and disrupt daily operations until ransomware is paid. This paper will provide critical analysis of the impact of Ransomware threat on SCADA systems.

Fault detection in a machine at earlier stage can prevent severe damage and loss to the industries. Fault detection techniques are broadly classified into three categories; signature extraction-based, model-based and knowledge-based approach. Model-based techniques are efficient for raising an alarm signal if there is any fault in the machine. This paper focuses on one such model based-technique to identify the internal faults of induction machine. The model developed is deployed in the end to make it feasible to use in real time. K-Nearest Neighbors (KNN) and grid search cross validation (CV) have been used to train and optimize the model to give the best results. The advantage of proposed algorithm is the accuracy in prediction which has been seen to be 80%. Finally, a user friendly interface has been built using Flask, a python web framework.

Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.

This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed.

We present a unified communication architecture for security requirements in the industrial internet of things. Formulating security requirements in the language of OPC UA provides a unified method to communicate and compare security requirements within a heavily heterogeneous landscape of machines in the field. Our machine-readable data model provides a fully automatable approach for security requirement communication within the rapidly evolving fourth industrial revolution, which is characterized by high-grade interconnection of industrial infrastructures and self-configuring production systems. Capturing security requirements in an OPC UA compliant and unified data model for industrial control systems enables strong use cases within modern production plants and future supply chains. We implement our data model as well as an OPC UA server that operates on this model to show the feasibility of our approach. Further, we deploy and evaluate our framework within a reference project realized by 14 industrial partners and 7 research facilities within Germany.

Networked Control Systems (NCS) are widely used in Industry 4.0 to obtain better management and operational capabilities, as well as to reduce costs. However, despite the benefits provided by NCSs, the integration of communication networks with physical plants can also expose these systems to cyber threats. This work proposes a link monitoring strategy to identify linear time-invariant transfer functions performed by a Man-in-the-Middle during controlled data injection attacks in NCSs. The results demonstrate that the proposed identification scheme provides adequate accuracy when estimating the attack function, and does not interfere in the plant behavior when the system is not under attack.

Aiming at the operation characteristics of power industry control system, this paper deeply analyses the attack mechanism and characteristics of power industry control system intrusion. On the basis of classifying and sorting out the attack characteristics of power industrial control system, this paper also attaches importance to break the basic theory and consequential technologies of industrial control network space security, and constructs the network intrusion as well as attack model of power industrial control system to realize the precise characterization of attackers' attack behavior, which provides a theoretical model for the analysis and early warning of attack behavior analysis of power industrial control systems.

Industrial Internet of Things (IIoT) is a fusion of industrial automation systems and IoT systems. It features comprehensive sensing, interconnected transmission, intelligent processing, self-organization and self-maintenance. Its applications span intelligent transportation, smart factories, and intelligence. Many areas such as power grid and intelligent environment detection. With the widespread application of IIoT technology, the cyber security threats to industrial IoT systems are increasing day by day, and information security issues have become a major challenge in the development process. In order to protect the industrial IoT system from network attacks, this paper aims to study the industrial IoT information security protection technology, and the typical architecture of industrial Internet of things system, and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture, and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.

Industrial control systems (ICS) are becoming more integral to modern life as they are being integrated into critical infrastructure. These systems typically lack application layer encryption and the placement of common network intrusion services have large blind spots. We propose the novel architecture, Cloud Based Intrusion Detection and Prevention System (CB-IDPS), to detect and prevent threats in ICS networks by using software defined networking (SDN) to route traffic to the cloud for inspection using network function virtualization (NFV) and service function chaining. CB-IDPS uses Amazon Web Services to create a virtual private cloud for packet inspection. The CB-IDPS framework is designed with considerations to the ICS delay constraints, dynamic traffic routing, scalability, resilience, and visibility. CB-IDPS is presented in the context of a micro grid energy management system as the test case to prove that the latency of CB-IDPS is within acceptable delay thresholds. The implementation of CB-IDPS uses the OpenDaylight software for the SDN controller and commonly used network security tools such as Zeek and Snort. To our knowledge, this is the first attempt at using NFV in an ICS context for network security.

Many modern business models of the manufacturing industry use the possibilities of digitization. In particular, the idea of connecting machines to networks and communication infrastructure is gaining momentum. However, in addition to the considerable economic advantages, this development also brings decisive disadvantages. By connecting previously encapsulated industrial networks with untrustworthy external networks such as the Internet, machines and systems are suddenly exposed to the same threats as conventional IT systems. A key problem today is the typical network paradigm with static routers and switches that cannot meet the dynamic requirements of a modern industrial network. Current security solutions often only threat symptoms instead of tackling the cause. In this paper we will therefore analyze the weaknesses of current networks and security solutions using the example of industrial remote maintenance. We will then present a novel concept of how Software-Defined Networking (SDN) in combination with a policy framework that supports attribute-based access control can be used to meet current and future security requirements in dynamic industrial networks. Furthermore, we will introduce an examplary implementation of this novel security framework for the use case of industrial remote maintenance and evaluate the solution. Our results show that SDN in combination with an Attribute-based Access Control (ABAC) policy framework is perfectly suited to increase flexibility and security of modern industrial networks at the same time.

The Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) systems are the backbones for monitoring and supervising factories, power grids, water distribution systems, nuclear plants, and other critical infrastructures. These systems are installed by third party contractors, maintained by site engineers, and operate for a long time. This makes tracing the documentation of the systems' changes and updates challenging since some of their components' information (type, manufacturer, model, etc.) may not be up-to-date, leading to possibly unaccounted security vulnerabilities in the systems. Device recognition is useful first step in vulnerability identification and defense augmentation, but due to the lack of full traceability in case of legacy ICS/SCADA systems, the typical device recognition based on document inspection is not applicable. In this paper, we propose a hybrid approach involving the mix of communication-patterns and passive-fingerprinting to identify the unknown devices' types, manufacturers, and models. The algorithm uses the ICS/SCADA devices's communication-patterns to recognize the control hierarchy levels of the devices. In conjunction, certain distinguishable features in the communication-packets are used to recognize the device manufacturer, and model. We have implemented this hybrid approach in Python, and tested on traffic data from a water treatment SCADA testbed in Singapore (iTrust).

The continuing decrease in feature size of integrated circuits, and the increase of the complexity and cost of design and fabrication has led to outsourcing the design and fabrication of integrated circuits to third parties across the globe, and in turn has introduced several security vulnerabilities. The adversaries in the supply chain can pirate integrated circuits, overproduce these circuits, perform reverse engineering, and/or insert hardware Trojans in these circuits. Developing countermeasures against such security threats is highly crucial. Accordingly, this paper first develops a learning-based trust verification framework to detect hardware Trojans. To tackle Trojan insertion, IP piracy and overproduction, logic locking schemes and in particular stripped functionality logic locking is discussed and its resiliency against the state-of-the-art attacks is investigated.

The main issues with drug safety in the counterfeit medicine supply chain, are to do with how the drugs are initially manufactured. The traceability of right and active pharmaceutical ingredients during actual manufacture is a difficult process, so detecting drugs that do not contain the intended active ingredients can ultimately lead to end-consumer patient harm or even death. Blockchain's advanced features make it capable of providing a basis for complete traceability of drugs, from manufacturer to end consumer, and the ability to identify counterfeit-drug. This paper aims to address the issue of drug safety using Blockchain and encrypted QR(quick response) code security.

Supply chain management (SCM) is fundamental for gaining financial, environmental and social benefits in the supply chain industry. However, traditional SCM mechanisms usually suffer from a wide scope of issues such as lack of information sharing, long delays for data retrieval, and unreliability in product tracing. Recent advances in blockchain technology show great potential to tackle these issues due to its salient features including immutability, transparency, and decentralization. Although there are some proof-of-concept studies and surveys on blockchain-based SCM from the perspective of logistics, the underlying technical challenges are not clearly identified. In this paper, we provide a comprehensive analysis of potential opportunities, new requirements, and principles of designing blockchain-based SCM systems. We summarize and discuss four crucial technical challenges in terms of scalability, throughput, access control, data retrieval and review the promising solutions. Finally, a case study of designing blockchain-based food traceability system is reported to provide more insights on how to tackle these technical challenges in practice.