Biblio

Power system automation and communication standards are spearheading the power system transition towards a smart grid. IEC 61850 is one such standard, which is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation and protection devices within digital substations. However, IEC 61850 is not cyber secure. In this paper, we demonstrate the dangerous implications of not securing IEC 61850 standard. Cyber attacks may exploit the vulnerabilities of the Sampled Values (SV) and Generic Object-Oriented Substation Event (GOOSE) protocols of IEC 61850. The cyber attacks may be realised by injecting spoofed SV and GOOSE data frames into the substation communication network at the bay level. We demonstrate that such cyber attacks may lead to obstruction or tripping of multiple protective relays. Coordinated cyber attacks against the protection system in digital substations may cause generation and line disconnections, triggering cascading failures in the power grid. This may eventually result in a partial or complete blackout. The attack model, impact on system dynamics and cascading failures are veri ed experimentally through a proposed cyber-physical experimental framework that closely resembles real-world conditions within a digital substation, including Intelligent Electronic Devices (IEDs) and protection schemes. It is implemented through Hardware-in-the-Loop (HIL) simulations of commercial relays with a Real-Time Digital Simulator (RTDS).

With the application of virtualization and multi-core processor in embedded system, the computing capacity of embedded system has been improved comprehensively, but it is also faced with malicious attacks against virtualization technology. First, it was analyzed the security requirements of each layer of embedded virtualization computing platform. Aiming at the security requirements, it was proposed the security architecture of embedded virtualization computing platform based on trusted computing module. It was designed the hardware trusted root on the hardware layer, the virtualization trusted root on the virtual machine manager layer, trusted computing component and security function component on guest operation system layer. Based on the trusted roots, it was built the static extension of the trusted chain on the platform. This security architecture can improve the active security protection capability of embedded virtualization computing platform.

With the continuous growth of Internet technology and the increasingly broadening applications of The Internet, network security incidents as well as cyber-attacks are also showing a growing trend. Consequently, computer network security is becoming increasingly important. TCP firewall is a computer network security system, and it allows or denies the transmission of data according to specific rules for providing security for the computer network. Traditional firewalls rely on network administrators to set security rules for them, and network administrators sometimes need to choose to allow and deny packets to keep computer networks secure. However, due to the huge amount of data on the Internet, network administrators have a huge task. Therefore, it is particularly important to solve this problem by using the machine learning method of computer technology. This study aims to predict TCP security action based on the TCP transmission characteristics dataset provided by UCI machine learning repository by implementing machine learning models such as neural network, support vector machine (SVM), AdaBoost, and Logistic regression. Processes including evaluating various models and interpretability analysis. By utilizing the idea of ensemble-learning, the final result has an accuracy score of over 98%.

The following topics are dealt with: security of data; distributed power generation; power engineering computing; power grids; power system security; computer network security; voltage control; risk management; power system measurement; critical infrastructures.

The classical key distribution systems used for data transmission in networked microgrids (NMGs) rely on mathematical assumptions, which however can be broken by attacks from quantum computers. This paper addresses this quantum-era challenge by using quantum key distribution (QKD). Specifically, the novelty of this paper includes 1) a QKD-enabled communication architecture it devises for NMGs, 2) a real-time QKD- enabled NMGs testbed it builds in an RTDS environment, and 3) a novel two-level key pool sharing (TLKPS) strategy it designs to improve the system resilience against cyberattacks. Test results validate the effectiveness of the presented strategy, and provide insightful resources for building quantum-secure NMGs.

Alongside the rapid expansion of Internet of Things (IoT), and network evolution (5G, 6G technologies), comes the need for security of higher level and less hardware demanding modules. New cryptographic systems are developed, in order to satisfy the special needs of security, that have emerged in modern applications. In this paper, a novel lightweight data streaming system, is proposed, which operates in alternative modes. Each one of them, performs efficiently as one of three in total, stream ciphering modules. The operation of the proposed system, is based on reconfigurable logic. It aims at a lower hardware utilization and good performance, at the same time. In addition, in order to have a fair and detailed comparison, a second one design is also integrated and introduced. This one proposes a conventional architecture, consisting of the same three stream ciphering modes, implemented on the same device, as separate operation modules. The FPGA synthesis results prove that the proposed reconfigurable design achieves to minimize the area resources, from 18% to 30%, compared to the conventional one, while maintaining high performance values, for the supported modes.

The dynamic and adaptable characteristics of mobile ad hoc networks have made it a significant field for deploying various applications in wireless sensor networks. Increasing popularity of the portable devices is the main reason for the development of mobile ad hoc networks. Furthermore, the network does not require a fixed architecture and it is easy to deploy. This type of network is highly vulnerable to cyber-attacks as the nodes communicate with each other through a Wireless medium. The most critical attack in ad hoc network is jellyfish attack. In this research we have proposed a Direct Trust-based Detection Algorithm to detect and prevent jellyfish attack in MANET.

Artificial Intelligence also often referred to as machine learning is being labelled to as the future has been into light since more than a decade. Artificial Intelligence designated by the acronym AI has a vast scope of development and the developers have been working on with it constantly. AI is being associated with the existing objects in the world as well as with the ones that are about to arrive to improve them and make them more reliable. AI as it states in its name is intelligence, intelligence shown by the machines to work similar to humans and work on achieving the goals they are being provided with. Another application of AI could be to provide defenses against the present cyber threats, vehicle overrides etc. Also, AI might be intelligence but, in the end, it's still a bunch of codes, hence it is prone to be corrupted or misused by the world. To prevent the misuse of the technologies, it is necessary to deploy them with a sustainable defensive system as well. Obviously, there is going to be a default defense system but it is prone to be corrupted by the hackers or malfunctioning of the intelligence in certain scenarios which can result disastrous especially in case of Robotics. A proposal referred to as the “Guard Masking” has been offered in the following paper, to provide an alternative for securing Artificial Intelligence.

Intelligent connected vehicle cyber security has attracted widespread attention this year. The safety of GNSS information is related to the safety of cars and has become a key technology. This paper researches the cyber security characteristics of intelligent connected vehicle navigation and positioning by analyzing the signal receiving mode of navigation and positioning on the vehicle terminal. The article expounds the principles of deceiving and interfering cyber security that lead to the safety of GNSS information. This paper studies the key causes of cyber security. Based on key causes, the article constructs a GNSS cyber security test method by combining a navigation signal simulator and an interference signal generator. The results shows that the method can realize the security test of the GNSS information of the vehicle terminal. This method provides a test method for the navigation terminal defense cyber security capability for a vehicle terminal, and fills a gap in the industry for the vehicle terminal information security test.

Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments' strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.

Recent cyberattacks targeting Supervisory Control and Data Acquisition (SCADA)/Industrial Control System(ICS) exploit weaknesses of host system software environment and take over the control of host processes in the host of the station network. We analyze the attack path of these attacks, which features how the attack hijacks the host in the network and compromises the operations of field device controllers. The paper proposes a host-based protection method, which can prevent malware penetration into the process memory by code injection attacks. The method consists of two protection schemes. One is to prevent file-based code injection such as DLL injection. The other is to prevent fileless code injection. The method traces changes in memory regions and determine whether the newly allocated memory is written with malicious codes. For this method, we show how a machine learning method can be adopted.

Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.

Many important security informatics problems require consideration of dynamical phenomena for their solution; examples include predicting the behavior of individuals in social networks and distinguishing malicious and innocent computer network activities based on activity traces. While information theory offers powerful tools for analyzing dynamical processes, to date the application of information-theoretic methods in security domains has focused on static analyses (e.g., cryptography, natural language processing). This paper leverages information-theoretic concepts and measures to quantify the similarity of pairs of stochastic dynamical systems, and shows that this capability can be used to solve important problems which arise in security applications. We begin by presenting a concise review of the information theory required for our development, and then address two challenging tasks: 1.) characterizing the way influence propagates through social networks, and 2.) distinguishing malware from legitimate software based on the instruction sequences of the disassembled programs. In each application, case studies involving real-world datasets demonstrate that the proposed techniques outperform standard methods.

Internet of Things (IoT) systems are becoming widely used, which makes them to be a high-value target for both hackers and crackers. From gaining access to sensitive information to using them as bots for complex attacks, the variety of advantages after exploiting different security vulnerabilities makes the security of IoT devices to be one of the most challenging desideratum for cyber security experts. In this paper, we will propose a new IoT system, designed to ensure five data principles: confidentiality, integrity, availability, authentication and authorization. The innovative aspects are both the usage of a web-based communication and a custom dynamic data request structure.

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of smart and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are attractive targets for security attackers. Hence, with their integration into both the industry and consumer devices, they added a new surface for cybersecurity attacks. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. The design of secure systems is a complex task, especially if they must adhere to other constraints, such as performance, power consumption, and others. A range of design space exploration tools have been proposed in academics, which aim to support system designers in their task of finding the optimal selection of hardware components and task mappings. Said tools offer a limited way of modeling attack scenarios as constraints for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early design phase. It offers designers to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and security risk. The framework's feasibility and performance is demonstrated by revisiting a potential system design of an industry partner.

The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.

Wide integration of information and communication technology (ICT) in modern power grids has brought many benefits as well as the risk of cyber attacks. A critical step towards defending grid cyber security is to understand the cyber-physical causal chain, which describes the progression of intrusion in cyber-space leading to the formation of consequences on the physical power grid. In this paper, we develop an attack vector for a time delay attack at load frequency control in the power grid. Distinct from existing works, which are separately focused on cyber intrusion, grid response, or testbed validation, the proposed attack vector for the first time provides a full cyber-physical causal chain. It targets specific vulnerabilities in the protocols, performs a denial-of-service (DoS) attack, induces the delays in control loop, and destabilizes grid frequency. The proposed attack vector is proved in theory, presented as an attack tree, and validated in an experimental environment. The results will provide valuable insights to develop security measures and robust controls against time delay attacks.

The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.

Cybersecurity community is slowly leveraging Machine Learning (ML) to combat ever evolving threats. One of the biggest drivers for successful adoption of these models is how well domain experts and users are able to understand and trust their functionality. As these black-box models are being employed to make important predictions, the demand for transparency and explainability is increasing from the stakeholders.Explanations supporting the output of ML models are crucial in cyber security, where experts require far more information from the model than a simple binary output for their analysis. Recent approaches in the literature have focused on three different areas: (a) creating and improving explainability methods which help users better understand the internal workings of ML models and their outputs; (b) attacks on interpreters in white box setting; (c) defining the exact properties and metrics of the explanations generated by models. However, they have not covered, the security properties and threat models relevant to cybersecurity domain, and attacks on explainable models in black box settings.In this paper, we bridge this gap by proposing a taxonomy for Explainable Artificial Intelligence (XAI) methods, covering various security properties and threat models relevant to cyber security domain. We design a novel black box attack for analyzing the consistency, correctness and confidence security properties of gradient based XAI methods. We validate our proposed system on 3 security-relevant data-sets and models, and demonstrate that the method achieves attacker's goal of misleading both the classifier and explanation report and, only explainability method without affecting the classifier output. Our evaluation of the proposed approach shows promising results and can help in designing secure and robust XAI methods.

With the rapid development of Internet, the issue of cyber security has increasingly gained more attention. An intrusion Detection System (IDS) is an effective technique to defend cyber-attacks and reduce security losses. However, the challenge of IDS lies in the diversity of cyber-attackers and the frequently-changing data requiring a flexible and efficient solution. To address this problem, machine learning approaches are being applied in the IDS field. In this paper, we propose an efficient scalable neural-network-based hybrid IDS framework with the combination of Host-level IDS (HIDS) and Network-level IDS (NIDS). We applied the autoencoders (AE) to NIDS and designed HIDS using word embedding and convolutional neural network. To evaluate the IDS, many experiments are performed on the public datasets NSL-KDD and ADFA. It can detect many attacks and reduce the security risk with high efficiency and excellent scalability.

Social engineering attacks are well known attacks in the cyberspace and relatively easy to try and implement because no technical knowledge is required. In various online environments such as business domains where customers talk through a chat service with employees or in social networks potential hackers can try to manipulate other people by employing social attacks against them to gain information that will benefit them in future attacks. Thus, we have used a number of natural language processing steps and a machine learning algorithm to identify potential attacks. The proposed method has been tested on a semi-synthetic dataset and it is shown to be both practical and effective.

DDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN), C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.

Cloud-hosted applications are prone to targeted attacks such as DDoS, advanced persistent threats, cryptojacking which threaten service availability. Recently, methods for threat information sharing and defense require co-operation and trust between multiple domains/entities. There is a need for mechanisms that establish distributed trust to allow for such a collective defense. In this paper, we present a novel threat intelligence sharing and defense system, namely “DefenseChain”, to allow organizations to have incentive-based and trustworthy co-operation to mitigate the impact of cyber attacks. Our solution approach features a consortium Blockchain platform to obtain threat data and select suitable peers to help with attack detection and mitigation. We propose an economic model for creation and sustenance of the consortium with peers through a reputation estimation scheme that uses `Quality of Detection' and `Quality of Mitigation' metrics. Our evaluation experiments with DefenseChain implementation are performed on an Open Cloud testbed with Hyperledger Composer and in a simulation environment. Our results show that the DefenseChain system overall performs better than state-of-the-art decision making schemes in choosing the most appropriate detector and mitigator peers. In addition, we show that our DefenseChain achieves better performance trade-offs in terms of metrics such as detection time, mitigation time and attack reoccurence rate. Lastly, our validation results demonstrate that our DefenseChain can effectively identify rational/irrational service providers.