Biblio

Machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial machinery. The use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems.

Transformer is the key equipment of power system, and its stable operation is very important to the security of power system In practical application, with the progress of technology, the performance of transformer becomes more and more important, but faults also occur from time to time in practical application, and the traditional manual fault diagnosis needs to consume a lot of time and energy. At present, the rapid development of artificial intelligence technology provides a new research direction for timely and accurate detection and treatment of transformer faults. In this paper, a method of transformer fault diagnosis using artificial neural network is proposed. The neural network algorithm is used for off-line learning and training of the operation state data of normal and fault states. By adjusting the relationship between neuron nodes, the mapping relationship between fault characteristics and fault location is established by using network layer learning, Finally, the reasoning process from fault feature to fault location is realized to realize intelligent fault diagnosis.

The Internet of Things (IoT) continuously grows as applications require connectivity and sensor networks are being deployed in multiple application domains. With the increased applicability demand, the need for testing and development frameworks also increases. This paper presents a novel simulation framework for testing IPv6 over Low Power Wireless Personal Networks (6LoWPAN) networks using the Mininet-WiFi simulator. The goal of the simulation framework is to allow easier automation testing of large-scale networks and to also allow easy configuration. This framework is a starting point for many development scenarios targeting traffic management, Quality of Service (QoS) or security network features. A basic smart city simulation is presented which demonstrates the working principles of the framework.

Mesh networks based on the wireless local area network (WLAN) technology, as specified by the standards amendment IEEE 802.11s, provide for a flexible and low-cost interconnection of devices and embedded systems for various use cases. To assess the real-world performance of WLAN mesh networks and potential optimization strategies, suitable testbeds and measurement tools are required. Designed for highly automated transport-layer throughput and latency measurements, the software FLExible Network Tester (Flent) is a promising candidate. However, so far Flent does not integrate information specific to IEEE 802.11s networks, such as peer link status data or mesh routing metrics. Consequently, we propose Flent extensions that allow to additionally capture IEEE 802.11s information as part of the automated performance tests. For the functional validation of our extensions, we conduct Flent measurements in a mesh mobility scenario using the network emulation framework Mininet-WiFi.

This paper investigates the robustness of the received signal strength (RSS)-based physical layer authentication (PLA) for wireless mesh networks, through experimental results. Specifically, we develop a secure wireless mesh networking framework and apply the RSS-based PLA scheme, with the aim to perform continuous authentication. The mesh setup comprises three Raspberry-PI4 computing nodes (acting as Alice, Bob, and Eve) and a server. The server role is to perform the initial authentication when a new node joins the mesh network. After that, the legitimate nodes in the mesh network perform continuous authentication, by leveraging the RSS feature of wireless signals. In particular, Bob tries to authenticate Alice in the presence of Eve. The performance of the presented framework is quantified through extensive experimental results in an outdoor environment, where various nodes' positions, relative distances, and pedestrian speeds scenarios are considered. The obtained results demonstrate the robustness of the underlying model, where an authentication rate of 99% for the static case can be achieved. Meanwhile, at the pedestrian speed, the authentication rate can drop to 85%. On the other hand, the detection rate improves when the distance between the legitimate and wiretap links is large (exceeds 20 meters) or when Alice and Eve are moving in different mobility patterns.

ETSI DECT-2020 New Radio (NR) is a new flexible radio interface targeted to support a broad range of wireless Internet of Things (IoT) applications. Recent reports have shown that DECT-2020 NR achieves good delay performance and it has been shown to fulfill both massive machine-type communications (mMTC) and ultra-reliable low latency communications (URLLC) requirements for 5th generation (5G) networks. A unique aspect of DECT-2020 as a 5G technology is that it is an autonomous wireless mesh network (WMN) protocol where the devices construct and uphold the network independently without the need for base stations or core network architecture. Instead, DECT-2020 NR relies on part of the network devices taking the role of a router to relay data through the network. This makes deployment of a DECT-2020 NR network affordable and extremely easy, but due to the nature of the medium access protocol, the routing responsibility adds an additional energy consumption burden to the nodes, who in the IoT domain are likely to be equipped with a limited battery capacity. In this paper, we analyze by system level simulations the energy consumption of DECT-2020 NR networks with different network sizes and topologies and how the reported low latencies can be upheld given the energy constraints of IoT devices.

How can high-level directives concerning risk, cybersecurity and compliance be operationalized in the central nervous system of any organization above a certain complexity? How can the effectiveness of technological solutions for security be proven and measured, and how can this technology be aligned with the governance and financial goals at the board level? These are the essential questions for any CEO, CIO or CISO that is concerned with the wellbeing of the firm. The concept of Zero Trust (ZT) approaches information and cybersecurity from the perspective of the asset to be protected, and from the value that asset represents. Zero Trust has been around for quite some time. Most professionals associate Zero Trust with a particular architectural approach to cybersecurity, involving concepts such as segments, resources that are accessed in a secure manner and the maxim “always verify never trust”. This paper describes the current state of the art in Zero Trust usage. We investigate the limitations of current approaches and how these are addressed in the form of Critical Success Factors in the Zero Trust Framework developed by ON2IT ‘Zero Trust Innovators’ (1). Furthermore, this paper describes the design and engineering of a Zero Trust artefact that addresses the problems at hand (2), according to Design Science Research (DSR). The last part of this paper outlines the setup of an empirical validation trough practitioner oriented research, in order to gain a broader acceptance and implementation of Zero Trust strategies (3). The final result is a proposed framework and associated technology which, via Zero Trust principles, addresses multiple layers of the organization to grasp and align cybersecurity risks and understand the readiness and fitness of the organization and its measures to counter cybersecurity risks.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

The Enterprise Architecture and Systems Engineering communities are often faced with complexity barriers that develop due to the fact that modern systems must be agile and resilient. This requires dynamic changes to the system so as to adapt to changing missions as well as changes in the internal and external environments. The requirement is not entirely new, but practitioners need guidance on how to manage the life cycle of such systems. This is a problem because we must be able to architect systems by alleviating the difficulties in systems life cycle management (e.g., by helping the enterprise- or systems engineer organise and maintain models and architecture descriptions of the system of interest). Building on Pask’s conversation theoretic model of aware (human or machine) individuals, the paper proposes a reference model for systems that maintain their own models real time, act efficiently, and create system-level awareness on all levers of aggregation.

Federated Learning (FL) is a multiparty learning computing approach that can aid privacy-preservation machine learning. However, FL has several potential security and privacy threats. First, the existing FL requires a central coordinator for the learning process which brings a single point of failure and trust issues for the shared trained model. Second, during the learning process, intentionally unreliable model updates performed by Byzantine colluding parties can lower the quality and convergence of the shared ML models. Therefore, discovering verifiable local model updates (i.e., integrity or correctness) and trusted parties in FL becomes crucial. In this paper, we propose a resilient and verifiable FL algorithm based on a reputation scheme to cope with unreliable parties. We develop a selection algorithm for task publisher and blockchain-based multiparty learning architecture approach where local model updates are securely exchanged and verified without the central party. We also proposed a novel auditing scheme to ensure our proposed approach is resilient up to 50% Byzantine colluding attack in a malicious scenario.

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the software to a benign state. This manuscript presents CARE - the first secure boot framework that provides malicious code modification attack detection, resilience, and onboard recovery mechanism for the compromised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the integrity and authenticity of the software and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to provide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides promising resilience and recovery mechanism with very little (8%) performance and resource overhead.

Physically Unclonable Functions (PUFs) are hardware-based security primitives that utilize manufacturing process variations to realize binary keys (Weak PUFs) or binary functions (Strong PUFs). This primitive is desirable for key generation and authentication in constrained devices, due to its low power and low area overhead. However, in recent years many research papers are focused on the vulnerability of PUFs to modeling attacks. This attack is possible because the PUFs challenge and response exchanges are usually transmitted over communication channel without encryption. Thus, an attacker can collect challenge-response pairs and use it as input into a learning algorithm, to create a model that can predict responses given new challenges. In this paper we introduce a serial and a parallel novel 64-bits memory-based controlled PUF (Mc-PUF) architecture for device authentication that has high uniqueness and randomness, reliable, and resilient against modeling attacks. These architectures generate a response by utilizing bits extracted from the fingerprint of a synchronous random-access memory (SRAM) PUF with a control logic. The synthesis of the serial architecture yielded an area of 1.136K GE, while the parallel architecture was 3.013K GE. The best prediction accuracy obtained from the modeling attack was 50%, which prevents an attacker from accurately predicting responses to future challenges. We also showcase the scalability of the design through XOR-ing several Mc-PUFs, further improving upon its security and performance. The remainder of the paper presents the proposed architectures, along with their hardware implementations, area and power consumption, and security resilience against modeling attacks. The 3-XOR Mc-PUF had the greatest overhead, but it produced the best randomness, uniqueness, and resilience against modeling attacks.

In this paper we use a seeded modular coding scheme for implementing physical layer security in a wiretap scenario. This modular scheme consists of a traditional coding layer and a security layer. For the traditional coding layer, we use a polar code. We evaluate the performance of the seeded modular coding scheme in an experimental setup with software defined radios and compare these results to simulation results. In order to assess the secrecy level of the scheme, we employ the distinguishing security metric. In our experiments, we compare the distinguishing error rate for different seeds and block lengths.

With technology at our fingertips waiting to be exploited, the past decade saw the revolutionizing Human Computer Interactions. The ease with which a user could interact was the Unique Selling Proposition (USP) of a sales team. Human Computer Interactions have many underlying parameters like Data Visualization and Presentation as some to deal with. With the race, on for better and faster presentations, evolved many frameworks to be widely used by all software developers. As the need grew for user friendly applications, more and more software professionals were lured into the front-end sophistication domain. Application frameworks have evolved to such an extent that with just a few clicks and feeding values as per requirements we are able to produce a commercially usable application in a few minutes. These frameworks generate quantum lines of codes in minutes which leaves a contrail of bugs to be discovered in the future. We have also succumbed to the benchmarking in Software Quality Metrics and have made ourselves comfortable with buggy software's to be rectified in future. The exponential evolution in the cyber domain has also attracted attackers equally. Average human awareness and knowledge has also improved in the cyber domain due to the prolonged exposure to technology for over three decades. As the attack sophistication grows and zero day attacks become more popular than ever, the suffering end users only receive remedial measures in spite of the latest Antivirus, Intrusion Detection and Protection Systems installed. We designed a software to display the complete services and applications running in users Operating System in the easiest perceivable manner aided by Computer Graphics and Data Visualization techniques. We further designed a study by empowering the fence sitter users with tools to actively participate in protecting themselves from threats. The designed threats had impressions from the complete threat canvas in some form or other restricted to systems functioning. Network threats and any sort of packet transfer to and from the system in form of threat was kept out of the scope of this experiment. We discovered that end users had a good idea of their working environment which can be used exponentially enhances machine learning for zero day threats and segment the unmarked the vast threat landscape faster for a more reliable output.

The past decade has shown us the power of cyber space and we getting dependent on the same. The exponential evolution in the domain has attracted attackers and defenders of technology equally. This inevitable domain has led to the increase in average human awareness and knowledge too. As we see the attack sophistication grow the protectors have always been a step ahead mitigating the attacks. A study of the various Threat Detection, Protection and Mitigation Systems revealed to us a common similarity wherein users have been totally ignored or the systems rely heavily on the user inputs for its correct functioning. Compiling the above we designed a study wherein user inputs were taken in addition to independent Detection and Prevention systems to identify and mitigate the risks. This approach led us to a conclusion that involvement of users exponentially enhances machine learning and segments the data sets faster for a more reliable output.

Objective measures are ubiquitous in the formulation, design and implementation of deep space missions. Tour durations, flyby altitudes, propellant budgets, power consumption, and other metrics are essential to developing and managing NASA missions. But beyond the simple metrics of cost and workforce, it has been difficult to identify objective, quantitative measures that assist in evaluating choices made during formulation or implementation phases in terms of their impact on flight operations. As part of the development of the Europa Clipper Mission system, a set of operations metrics have been defined along with the necessary design information and software tooling to calculate them. We have applied these methods and metrics to help assess the impact to the flight team on the six options for the Clipper Tour that are currently being vetted for selection in the fall of 2021. To generate these metrics, the Clipper MOS team first designed the set of essential processes by which flight operations will be conducted, using a standard approach and template to identify (among other aspects) timelines for each process, along with their time constraints (e.g., uplinks for sequence execution). Each of the resulting 50 processes is documented in a common format and concurred by stakeholders. Process timelines were converted into generic schedules and workforce-loaded using COTS scheduling software, based on the inputs of the process authors and domain experts. Custom code was generated to create an operations schedule for a specific portion of Clipper's prime mission, with instances of a given process scheduled based on specific timing rules (e.g., process X starts once per week on Thursdays) or relative to mission events (e.g., sequence generation process begins on a Monday, at least three weeks before each Europa closest approach). Over a 5-month period, and for each of six Clipper candidate tours, the result was a 20,000+ line, workforce-loaded schedule that documents all of the process-driven work effort at the level of individual roles, along with a significant portion of the level-of-effort work. Post-processing code calculated the absolute and relative number of work hours during a nominal 5 day / 40 hour work week, the work effort during 2nd and 3rd shift, as well as 1st shift on weekends. The resultant schedules and shift tables were used to generate objective measures that can be related to both human factors and to operational risk and showed that Clipper tours which utilize 6:1 resonant (21.25 day) orbits instead of 4:1 resonant (14.17 day) orbits during the first dozen or so Europa flybys are advantageous to flight operations. A similar approach can be extended to assist missions in more objective assessments of a number of mission issues and trades, including tour selection and spacecraft design for operability.

Flexibility and speed in the development of new industrial machines are essential factors for the success of capital goods industries. When assembling a printed circuit board (PCB), since all the components are surface mounted devices (SMD), the whole process is automatic. However, in many PCBs, it is necessary to place components that are not SMDs, called pin through hole components (PTH), having to be inserted manually, which leads to delays in the production line. This work proposes and validates a prototype work cell based on a collaborative robot and vision systems whose objective is to insert these components in a completely autonomous or semi-autonomous way. Different tests were made to validate this work cell, showing the correct implementation and the possibility of replacing the human worker on this PCB assembly task.

Cloud Computing (CC) has emerged as an on-demand accessible tool in different practical applications such as digital industry, academics, manufacturing, health sector and others. In this paper different security threats faced by CC are discussed with suitable examples. Moreover, an artificial intelligence based security enabled CC is also discussed based on suitable empirical data. It is found that an artificial neural network (ANN) is an effective system to detect the level of risk factors associated with CC along with mitigating those risk issues with appropriate algorithms. Hence, it provides a desired level of protection against cyber attacks, internal confidential threats and external threat of data theft from a cloud computing system. Levenberg–Marquardt (LMBP) algorithms are also found as a significant tool to estimate the level of security performance around a cloud computing system. ANN is used to improve the performance level of data security across a cloud computing network and make it security enabled to ensure a protected data transmission to clients associated with the system.

The widespread application of industrial Internet identifiers has increased the security risks of industrial Internet and identifier resolution system. In order to improve the security capabilities of identifier resolution system, this paper analyzes the security challenges faced by identifier resolution system at this stage, and in line with the concept of layered security defense in depth, divides the security domains of identifier resolution system and proposes a multi-level security strategy based on security domains by deploying appropriate protective measures in each security domain.

The security proposed for Vehicle-to-Everything (V2X) systems in the European Union is specified in the ETSI Cooperative Intelligent Transport System (C-ITS) standards, and related documents are based on the trusted PKI/CAs. The C-ITS trust model platform comprises an EU Root CA and additional Root CAs run in Europe by member state authorities or private organizations offering certificates to individual users. A new method is described in this paper where the security in V2X is based on the Distributed Public Keystore (DPK) platform developed for Ethereum blockchain. The V2X security is considered as one application of the DPK platform. The DPK stores and distributes the vehicles, RSUs, or other C-ITS role-players’ public keys. It establishes a generic key exchange/ agreement scheme that provides mutual key, entity authentication, and distributing a session key between two peers. V2X communication based on this scheme can establish an end-to-end (e2e) secure session and enables vehicle authentication without the need for a vehicle certificate signed by a trusted Certificate Authority.

Over past years, the high demand to efficiently process deep learning (DL) models has driven the market of the chip design companies. However, the new Deep Chip architectures, a common term to refer to DL hardware accelerator, have slightly paid attention to the security requirements in quantized neural networks (QNNs), while the black/white -box adversarial attacks can jeopardize the integrity of the inference accelerator. Therefore in this paper, a comprehensive study of the resiliency of QNN topologies to black-box attacks is examined. Herein, different attack scenarios are performed on an FPGA-processor co-design, and the collected results are extensively analyzed to give an estimation of the impact’s degree of different types of attacks on the QNN topology. To be specific, we evaluated the sensitivity of the QNN accelerator to a range number of bit-flip attacks (BFAs) that might occur in the operational lifetime of the device. The BFAs are injected at uniformly distributed times either across the entire QNN or per individual layer during the image classification. The acquired results are utilized to build the entropy-based model that can be leveraged to construct resilient QNN architectures to bit-flip attacks.

To improve efficiency and resource usage in data retrieval, an Internet of Things (IoT) search engine organizes a vast amount of scattered data and responds to client queries with processed results. Machine learning provides a deep understanding of complex patterns and enables enhanced feedback to users through well-trained models. Nonetheless, machine learning models are prone to adversarial attacks via the injection of elaborate perturbations, resulting in subverted outputs. Particularly, adversarial attacks on time-series data demand urgent attention, as sensors in IoT systems are collecting an increasing volume of sequential data. This paper investigates adversarial attacks on time-series analysis in an IoT search engine (IoTSE) system. Specifically, we consider the Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) as our base model, implemented in a simulated federated learning scheme. We propose the Federated Adversarial Learning for IoT Search Engine (FALIoTSE) that exploits the shared parameters of the federated model as the target for adversarial example generation and resiliency. Using a real-world smart parking garage dataset, the impact of an attack on FALIoTSE is demonstrated under various levels of perturbation. The experiments show that the training error increases significantly with noises from the gradient.

This paper proposes an intelligent line congestion prognosis scheme based on wide-area measurements, which accurately identifies an impending congestion and the problem causing the congestion. Due to the increasing penetration of renewable energy resources and uncertainty of load/generation patterns in the Active Distribution Networks (ADNs), power line congestion is one of the issues that could happen during peak load conditions or high-power injection by renewable energy resources. Congestion would have devastating effects on both the economical and technical operation of the grid. Hence, it is crucial to accurately predict congestions to alleviate the problem in-time and command proper control actions; such as, power redispatch, incorporating ancillary services and energy storage systems, and load curtailment. We use neural network methods in this work due to their outstanding performance in predicting the nonlinear behavior of the power system. Bayesian Regularization, along with Levenberg-Marquardt algorithm, is used to train the proposed neural networks to predict an impending congestion and its cause. The proposed method is validated using the IEEE 13-bus test system. Utilizing the proposed method, extreme control actions (i.e., protection actions and load curtailment) can be avoided. This method will improve the distribution grid resiliency and ensure the continuous supply of power to the loads.

A source code static analysis became an industrial standard for program source code issues early detection. As one of requirements to such kind of analysis is high performance to provide response of automatic code checking tool as early as possible as far as such kind of tools integrates to Continuous testing and Integration systems. In this paper we propose a source code static analysis algorithm for solving performance issue of source code static analysis tool in general way.

Nowadays the use of Wi-Fi has been widely used in public places, such as in restaurants. The use of Wi-Fi in public places has a very large security vulnerability because it is used by a wide variety of visitors. Therefore, this study was conducted to evaluate the security of the WLAN network in restaurants. The methods used are Vulnerability Assessment and Penetration Testing. Penetration Testing is done by conducting several attack tests such as Deauthentication Attack, Evil Twin Attack with Captive Portal, Evil Twin Attack with Sniffing and SSL stripping, and Unauthorized Access.