Biblio

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.

Cyber-Physical System (CPS) is becoming increasingly complicated and integrated into our daily lives, laying the foundation for advanced infrastructures, commodities, and services. In this regard, operational continuity of the system is the most critical objective, and cyber resilience quantification to evaluate and enhance it has garnered attention. However, understanding of the increasingly critical cyber risks is weak, with the focus being solely on the damage that occurs in the physical domain. To address this gap, this work takes aim at shedding some light on the cyber resilience quantification of CPS. We review the numerous resilience quantification techniques presented to date through several metrics to provide systematization of knowledge (SoK). In addition, we discuss the challenges of current quantification methods and give ideas for future research that will lead to more precise cyber resilience measurements.

As the IPv6 protocol has been rapidly developed and applied, the security of IPv6 networks has become the focus of academic and industrial attention. Despite the fact that the IPv6 protocol is designed with security in mind, due to insufficient defense measures of current firewalls and intrusion detection systems for IPv6 networks, the construction of covert channels using fields not defined or reserved in IPv6 protocols may compromise the information systems. By discussing the possibility of constructing storage covert channels within IPv6 protocol fields, 10 types of IPv6 covert channels are constructed with undefined and reserved fields, including the flow label field, the traffic class field of IPv6 header, the reserved fields of IPv6 extension headers and the code field of ICMPv6 header. An IPv6 covert channel detection method based on field matching (CC-Guard) is proposed, and a typical IPv6 network environment is built for testing. In comparison with existing detection tools, the experimental results show that the CC-Guard not only can detect more covert channels consisting of IPv6 extension headers and ICMPv6 headers, but also achieves real-time detection with a lower detection overhead.

Modern cyber-physical systems that comprise controlled power electronics are becoming more internet-of-things-enabled and vulnerable to cyber-attacks. Therefore, hardening those systems against cyber-attacks becomes an emerging need. In this paper, a model-based deep learning cyber-attack detection to protect electric drive systems from cyber-attacks on the physical level is proposed. The approach combines the model physics with a deep learning-based classifier. The combination of model-based and deep learning will enable more accurate cyber-attack detection results. The proposed cyber-attack detector will be trained and simulated on a PM based electric drive system to detect false data injection attacks on the drive controller command and sensor signals.

Recently, a mechanism that randomly shuffles the data sent and allows securing the communication without the need to encrypt all the information has been proposed. This proposal is ideal for IoT systems with low computational capacity. In this work, we analyze the strength of this proposal from a brute-force attack approach to obtain the original message without knowledge of the applied disordering. It is demonstrated that for a set of 10x10 16-bit data, the processing time and the required memory are unfeasible with current technology. Therefore, it is safe.

Supervisory Control and Data Acquisition (SCADA) systems are used to control and monitor components within the energy grid, playing a significant role in the stability of the system. As a part of critical infrastructures, components in these systems have to fulfill a variety of different requirements regarding their dependability and must also undergo strict audit procedures in order to comply with all relevant standards. This results in a slow adoption of new functionalities. Due to the emerged threat of cyberattacks against critical infrastructures, extensive security measures are needed within these systems to protect them from adversaries and ensure a stable operation. In this work, a solution is proposed to integrate extensive security measures into current systems. By deploying additional security-gateways into the communication path between two nodes, security features can be integrated transparently for the existing components. The developed security-gateway is compliant to all regulatory requirements and features an internal architecture based on the separation-of-concerns principle to increase its security and longevity. The viability of the proposed solution has been verified in different scenarios, consisting of realistic field tests, security penetration tests and various performance evaluations.

In today's era, the smart grid is the carrier of the new energy technology revolution and a very critical development stage for grid intelligence. In the process of smart grid operation, maintenance and maintenance, many heterogeneous and polymorphic data can be formed, that is to say big data. This paper analyzes the power big data prediction technology for smart grid applications, and proposes practical application strategies In this paper, an in-depth analysis of the relationship between cloud computing and big data key technologies and smart grid is carried out, and an overview of the key technologies of electric power big data is carried out.

Current sensors are widely used in power grid for power metering, automation and power equipment monitoring. Since the tradeoff between the sensitivity and the measurement range needs to be made to design a current sensor, it is difficult to deploy one sensor to measure both the small-magnitude and the large-magnitude current. In this research, we design a surface-mount current sensor by using the tunneling magneto-resistance (TMR) devices and show that the tradeoff between the sensitivity and the detection range can be broken. Two TMR devices of different sensitivity degrees were integrated into one current sensor module, and a signal processing algorithm was implemented to fusion the outputs of the two TMR devices. Then, a platform was setup to test the performance of the surface-mount current sensor. The results showed that the designed current sensor could measure the current from 2 mA to 100 A with an approximate 93 dB dynamic range. Besides, the nonintrusive feature of the surface-mount current sensor could make it convenient to be deployed on-site.

Any type of engineered design requires metrics for trading off both desirable and undesirable properties. For integrated circuits, typical properties include circuit size, performance, power, etc., where for example, performance is a desirable property and power consumption is not. Security metrics, on the other hand, are extremely difficult to develop because there are active adversaries that intend to compromise the protected circuitry. This implies metric values may not be static quantities, but instead are measures that degrade depending on attack effectiveness. In order to deal with this dynamic aspect of a security metric, a general attack model is proposed that enables the effectiveness of various security approaches to be directly compared in the context of an attack. Here, we describe, define and demonstrate that the metrics presented are both meaningful and measurable.

This article focuses on analyzing the application characteristics of electric power big data, determining the advantages that electric power big data provides to the development of enterprises, and expounding the power information security protection technology and management measures under the background of big data. Focus on the protection of power information security, and fundamentally control the information security control issues of power enterprises. Then analyzed the types of big data structure and effective measurement modeling, and finally combined with the application status of big data concepts in the construction of electric power information networks, and proposed optimization strategies, aiming to promote the effectiveness of big data concepts in power information network management activities. Applying the creation conditions, the results show that the measurement model is improved by 7.8%

In the era of Big Data, opportunities and challenges are mixed. The data transfer is increasingly frequent and speedy, and the data lifecycle is also extended, bringing more challenges to security and privacy risk governance. Currently, the common measures of risk governance covering the entire data life cycle are the data-related staff management, equipment security management, data encryption codes, data content identification and de-identification processing, etc. With the trend of data globalization, regulations fragmentation and governance technologization, “International standards”, a measure of governance combining technology and regulation, has the potential to become the best practice. However, “voluntary compliance” of international standards derogates the effectiveness of risk governance through this measure. In order to strengthen the enforcement of the international standards, the paper proposes a governance approach which is “the framework regulated by international standards, and regulations and technologies specifically implemented by national legislation.” It aims to implement the security and privacy risk governance of Big Data effectively.

A new methodology to calculate the hysteresis induced power loss of inductor from the measured waveforms of DC-to-DC converter during the voltage conversion is presented. From this study, we find that the duty cycles (D) of the buck and boost converters used till date for inductance current calculation are not exactly equal to VOUT/VIN and 1-VIN/VOUT as the inductance change induced by the hysteresis effect cannot be neglected. Although the increase in the loading currents of the converter increases the remanence magnetization of inductor at the turn-off time (toff), this remanence magnetization is destroyed by the turbulence induced vortex current at the transistor turn-on transient. So, the core power loss of inductor increases with the loading current of the converter and becomes much larger than other power losses and cannot be neglected for the power efficiency calculation during power stage design.

The COVID19 pandemic situation has opened a wide range of opportunities for cyber-criminals, who take advantage of the anxiety generated and the time spent on the Internet, to undertake massive phishing campaigns. Although companies are adopting protective measures, the psychological traits of the victims are still considered from a very generic perspective. In particular, current literature determines that the model proposed in the Big-Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) might play an important role in human behaviour to counter cybercrime. However, results do not provide unanimity regarding the correlation between phishing susceptibility and neuroticism. With the aim to understand this lack of consensus, this article provides a comprehensive literature review of papers extracted from relevant databases (IEEE Xplore, Scopus, ACM Digital Library, and Web of Science). Our results show that there is not a well-established psychological theory explaining the role of neuroticism in the phishing context. We sustain that non-representative samples and the lack of homogeneity amongst the studies might be the culprits behind this lack of consensus on the role of neuroticism on phishing susceptibility.

The Z machine is a pulsed power generator located at Sandia National Laboratories in Albuquerque, New Mexico. It is capable of producing a \textbackslashtextgreater20 MA current pulse that is directed onto an experimental load. While a diverse array of experiments are conducted on the Z machine, including x-ray production and dynamic materials science experiments, the focus of this presentation are the Magnetic Liner Inertial Fusion (MagLIF) experiments. In these experiments, an axial magnetic field is applied to the load region, where a cylindrical, fuel-filled metal liner is imploded. We explore the effects of this field on the ability to efficiently couple the generator current to the load, and the extent to which this field interrupts the magnetic insulation of the inner-most transmission line. We find that at the present-day applied field values, the effects of the applied field on current coupling are negligible. Estimates of the potential impact on current coupling of the larger applied field values planned for future experiments are also given. Shunted current is measured with B-dot probes and flyer velocimetry techniques. Analytical calculations, 2D particle-in-cell simulations, and experimental measurements will be presented.

The metal oxide arrester (MOA, shortly) is installed on the line side of the substation, which is the first line of defense for the overvoltage limitation of lightning intrusion wave. In order to deeply limit the switching overvoltage and cancel the closing resistance of the circuit breaker, the arrester is replaced by the controllable metal oxide arrester (CMOA, shortly) in the new technology. The controllable switch of CMOA can be mechanical switch or thyristor switch. Thyristor switches are sensitive to the current and current change rate (di/dt) under lightning intrusion wave. If the switch cannot withstand, appropriate protective measures must be taken to ensure the safe operation of the controllable switch under this working condition. The 1000kV West Beijing to Shijiazhuang UHV AC transmission and transformation expansion project is the first project of pilot application of CMOA. CMOA were installed at both ends of the outgoing branch of Dingtai line I. In order to study the influence of lightning intrusion wave on the controllable switch of CMOA, this paper selected this project to simulate the lightning stroke on the incoming section of Dingtai line I in Beijing West substation in the process of system air closing or single-phase reclosing, and obtained the current and di/dt of the controllable switch through CMOA under this working condition. Then the performances of mechanical and thyristor control switches were checked respectively. The results showed that the mechanical switch could withstand without protective measures. The tolerance of thyristor switch to i and di/dt exceeded the limit value, and measures should be taken to protect and limit it. In this paper, the protection measures of current limiting reactor were given, and the limiting effect of the protection measures was verified by simulation and test. It could fully meet the requirements and ensure the safe operation of thyristor controllable switch.

As cyber threats continue to grow and expertise resources are limited, organisations need to find ways to evaluate their resilience efficiently and take proactive measures against an attack from a specific adversary before it occurs. Threat modelling is an excellent method of assessing the resilience of ICT systems, forming Attack (Defense) Graphs (ADGs) that illustrate an adversary’s attack vectors. Cyber Threat Intelligence (CTI) is information that helps understand the current cyber threats, but has little integration with ADGs. This paper contributes with an approach that resolves this problem by using CTI feeds of known threat actors to enrich ADGs under multiple reuse. This enables security analysts to take proactive measures and strengthen their ICT systems against current methods used by any threat actor that is believed to pose a threat to them.

Content blocking is an important part of a per-formant, user-serving, privacy respecting web. Current content blockers work by building trust labels over URLs. While useful, this approach has many well understood shortcomings. Attackers may avoid detection by changing URLs or domains, bundling unwanted code with benign code, or inlining code in pages.The common flaw in existing approaches is that they evaluate code based on its delivery mechanism, not its behavior. In this work we address this problem by building a system for generating signatures of the privacy-and-security relevant behavior of executed JavaScript. Our system uses as the unit of analysis each script's behavior during each turn on the JavaScript event loop. Focusing on event loop turns allows us to build highly identifying signatures for JavaScript code that are robust against code obfuscation, code bundling, URL modification, and other common evasions, as well as handle unique aspects of web applications.This work makes the following contributions to the problem of measuring and improving content blocking on the web: First, we design and implement a novel system to build per-event-loop-turn signatures of JavaScript behavior through deep instrumentation of the Blink and V8 runtimes. Second, we apply these signatures to measure how much privacy-and-security harming code is missed by current content blockers, by using EasyList and EasyPrivacy as ground truth and finding scripts that have the same privacy and security harming patterns. We build 1,995,444 signatures of privacy-and-security relevant behaviors from 11,212 unique scripts blocked by filter lists, and find 3,589 unique scripts hosting known harmful code, but missed by filter lists, affecting 12.48% of websites measured. Third, we provide a taxonomy of ways scripts avoid detection and quantify the occurrence of each. Finally, we present defenses against these evasions, in the form of filter list additions where possible, and through a proposed, signature based system in other cases.As part of this work, we share the implementation of our signature-generation system, the data gathered by applying that system to the Alexa 100K, and 586 AdBlock Plus compatible filter list rules to block instances of currently blocked code being moved to new URLs.

Numerous measurement researches have been performed to discover the IPv4 network security issues by leveraging the fast Internet-wide scanning techniques. However, IPv6 brings the 128-bit address space and renders brute-force network scanning impractical. Although significant efforts have been dedicated to enumerating active IPv6 hosts, limited by technique efficiency and probing accuracy, large-scale empirical measurement studies under the increasing IPv6 networks are infeasible now. To fill this research gap, by leveraging the extensively adopted IPv6 address allocation strategy, we propose a novel IPv6 network periphery discovery approach. Specifically, XMap, a fast network scanner, is developed to find the periphery, such as a home router. We evaluate it on twelve prominent Internet service providers and harvest 52M active peripheries. Grounded on these found devices, we explore IPv6 network risks of the unintended exposed security services and the flawed traffic routing strategies. First, we demonstrate the unintended exposed security services in IPv6 networks, such as DNS, and HTTP, have become emerging security risks by analyzing 4.7M peripheries. Second, by inspecting the periphery's packet routing strategies, we present the flawed implementations of IPv6 routing protocol affecting 5.8M router devices. Attackers can exploit this common vulnerability to conduct effective routing loop attacks, inducing DoS to the ISP's and home routers with an amplification factor of \textbackslashtextbackslashgt 200. We responsibly disclose those issues to all involved vendors and ASes and discuss mitigation solutions. Our research results indicate that the security community should revisit IPv6 network strategies immediately.

In Magnetic Josephson Junctions (MJJs) based on Superconductor-Insulator-Superconductor-Ferromagnet-Superconductor (SIS’FS), we provide evidence of an unconventional magnetic field behavior of the critical current characterized by an inverted magnetic hysteresis, i.e., an inverted shift of the whole magnetic field pattern when sweeping the external field. By thermoremanence measurements of S/F/S trilayers, we have ruled out that this uncommon behavior could be related to the F-stray fields. In principle, this finding could have a crucial role in the design and proper functioning of scalable cryogenic memories.

Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understanding, stakeholders can debug performance behavior and make deliberate configuration decisions. Current black-box techniques to build such models combine various sampling and learning strategies, resulting in tradeoffs between measurement effort, accuracy, and interpretability. We present Comprex, a white-box approach to build performance-influence models for configurable systems, combining insights of local measurements, dynamic taint analysis to track options in the implementation, compositionality, and compression of the configuration space, without relying on machine learning to extrapolate incomplete samples. Our evaluation on 4 widely-used, open-source projects demonstrates that Comprex builds similarly accurate performance-influence models to the most accurate and expensive black-box approach, but at a reduced cost and with additional benefits from interpretable and local models.

The threats from side-channel attacks to modern processors has become a serious problem, especially under the enhancement of the microarchitecture characteristics with multicore and resource sharing. Therefore, the research and measurement of the vulnerability of the side-channel attack of the system is of great significance for computer designers. Most of the current evaluation methods proposed by researchers are only for typical cache side-channel attacks. In this paper, we propose a method to measure systems' vulnerability to side-channel attacks caused by port contention called pcSVF. We collected the traces of the victim and attacker and computed the correlation coefficient between them, thus we can measure the vulnerability of the system against side-channel attack. Then we analyzed the effectiveness of the method through the results under different system defense schemes.

Microservice architectures adoption is growing expeditiously in market size and adoption, including in business-critical systems. This is due to agility in development and deployment further increased by containers and their characteristics. Ensuring security is still a major concern due to challenges faced such as resource separation and isolation, as improper access to one service might compromise complete systems. This doctoral work intends to advance the security of microservice systems through research and improvement of methodologies for detection, tolerance and mitigation of security intrusions, while overcoming challenges related to multi-tenancy, heterogeneity, dynamicity of systems and environments. Our preliminary research shows that host-based IDSes are applicable in container environments. This will be extended to dynamic scenarios, serving as a steppingstone to research intrusion tolerance techniques suited to these environments. These methodologies will be demonstrated in realistic microservice systems: complex, dynamic, scalable and elastic.

The emergence of Industrial Cyber-Physical Systems (ICPS) in today's business world is still steadily progressing to new dimensions. Although they bring many new advantages to business processes and enable automation and a wider range of service capability, they also propose a variety of new challenges. One major challenge, which is introduced by such System-of-Systems (SoS), lies in the security aspect. As security may not have had that significant role in traditional embedded system engineering, a generic way to measure the level of security within an ICPS would provide a significant benefit for system engineers and involved stakeholders. Even though many security metrics and frameworks exist, most of them insufficiently consider an SoS context and the challenges of such environments. Therefore, we aim to define a security metric for ICPS, which measures the level of security during the system design, tests, and integration as well as at runtime. For this, we try to focus on a semantic point of view, which on one hand has not been considered in security metric definitions yet, and on the other hand allows us to handle the complexity of SoS architectures. Furthermore, our approach allows combining the critical characteristics of an ICPS, like uncertainty, required reliability, multi-criticality and safety aspects.

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.

The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.