Biblio

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning-based processing is a promising solution for detecting cyber-threats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

The selection of distribution network faults is of great significance to accurately identify the fault location, quickly restore power and improve the reliability of power supply. This paper mainly studies the fault phase selection method of distribution network based on wavelet singular entropy and deep belief network (DBN). Firstly, the basic principles of wavelet singular entropy and DBN are analyzed, and on this basis, the DBN model of distribution network fault phase selection is proposed. Firstly, the transient fault current data of the distribution network is processed to obtain the wavelet singular entropy of the three phases, which is used as the input of the fault phase selection model; then the DBN network is improved, and an artificial neural network (ANN) is introduced to make it a fault Select the phase classifier, and specify the output label; finally, use Simulink to build a simulation model of the IEEE33 node distribution network system, obtain a large amount of data of various fault types, generate a training sample library and a test sample library, and analyze the neural network. The adjustment of the structure and the training of the parameters complete the construction of the DBN model for the fault phase selection of the distribution network.

Many popular metrics used for the quantification of the quality or complexity of a codebase (e.g. cyclomatic complexity) were developed in the 1970s or 1980s when source code sizes were significantly smaller than they are today, and before a number of modern programming language features were introduced in different languages. Thus, the many thresholds that were suggested by researchers for deciding whether a given function is lacking in a given quality dimension need to be updated. In the pursuit of this goal, we study a number of open-source high-performance codes, each of which has been in development for more than 15 years—a characteristic which we take to imply good design to score them in terms of their source codes' quality and to relax the above-mentioned thresholds. First, we employ the LLVM/Clang compiler infrastructure and introduce a Clang AST tool to gather AST-based metrics, as well as an LLVM IR pass for those based on a source code's static call graph. Second, we perform statistical analysis to identify the reference thresholds of 22 code quality and callgraph-related metrics at a fine grained level.

Cyber-attacks in electrical power system causes serious damages causing breakdown of few equipment to shutdown of the complete power system. Game theory is used as a tool to detect the cyber-attack in the power system recently. Interaction between the attackers and the defenders which is the inherent nature of the game theory is exploited to detect the cyber-attack in the power system. This paper implements the cyber-attack detection on a two-area power system controlled using the Load Frequency controller. Ant Lion Optimization is used to tune the integral controller applied in the Load Frequency Controller. Cyber-attacks that include constant injection, bias injection, overcompensation, and negative compensation are tested on the Game theory-based attack detection algorithm proposed. It is considered that the smart meters are attacked with the attacks by manipulating the original data in the power system. MATLAB based implementation is developed and observed that the defender action is satisfactory in the two-area system considered. Tuning of integral controller in the Load Frequency controller in the two-area system is also observed to be effective.

Modbus is a popular industrial communication protocol supported by most automation devices. Despite its popularity, it is not a secure protocol because when it was developed, security was not a concern due to closed environments of industrial control systems. With the convergence of information technology and operational technology in recent years, the security of industrial control systems has become a serious concern. Due to the high availability requirements, it is not practical or feasible to do security experimentation of production systems. We present an implementation of cyber-physical systems with Modbus/TCP communication for real-time security testing. The proposed architecture consists of a process simulator, an IEC 61131-3 compliant programmable logic controller, and a human-machine interface, all communicating via Modbus/TCP protocol. We use Simulink as the process simulator. It does not have built-in support for the Modbus protocol. A contribution of the proposed work is to extend the functionality of Simulink with a custom block to enable Modbus communication. We use two case studies to demonstrate the utility of the cyber-physical system architecture. We can model complex industrial processes with this architecture, can launch cyber-attacks, and develop protection mechanisms.

Cyber-physical systems are particularly difficult to model and simulate because their components mix many different system modalities. In this paper we address the main technical challenges on system simulation taking into account by new characters of CPS, and provide a comprehensive view of the simulation modeling methods for integration of continuous-discrete model. Regards to UML and Simulink, two most widely accepted modeling methods in industrial designs, we study on three methods to perform the cooperation of these two kinds of heterogeneous models for co-simulation. The solution of an implementation of co-simulation method for CPS was designed under three levels architecture.

With the rapid progress in recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, and video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. These can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes, fake media created through deep learning tools, and on modern data-driven forensic methods to fight them. The analysis will help highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.

Widespread deployment of centralized clouds has changed the way internet services are developed, deployed and operated. Centralized clouds have substantially extended the market opportunities for online services, enabled new entities to create and operate internet-scale services, and changed the way traditional companies run their operations. However, there are types of services that are unsuitable for today's centralized clouds such as highly interactive virtual and augmented reality (VR/AR) applications, high-resolution gaming, virtualized RAN, mass IoT data processing and industrial robot control. They can be broadly categorized as either latency-sensitive network functions, latency-sensitive applications, and/or high-bandwidth services. What these basic functions have in common is the need for a more distributed cloud infrastructure—an infrastructure we call edge clouds. In this paper, we examine the evolution of clouds, and edge clouds especially, and look at the developing market for edge clouds and what developments are required in networking, hardware and software to support them.

Most Web sites rely on resources hosted by third parties such as CDNs. Third parties may be compromised or coerced into misbehaving, e.g. delivering a malicious script or stylesheet. Unexpected changes to resources hosted by third parties can be detected with the Subresource Integrity (SRI) mechanism. The focus of SRI is on scripts and stylesheets. Web fonts cannot be secured with that mechanism under all circumstances. The first contribution of this paper is to evaluates the potential for attacks using malicious fonts. With an instrumented browser we find that (1) more than 95% of the top 50,000 Web sites of the Tranco top list rely on resources hosted by third parties and that (2) only a small fraction employs SRI. Moreover, we find that more than 60% of the sites in our sample use fonts hosted by third parties, most of which are being served by Google. The second contribution of the paper is a proof of concept of a malicious font as well as a tool for automatically generating such a font, which targets security-conscious users who are used to verifying cryptographic fingerprints. Software vendors publish such fingerprints along with their software packages to allow users to verify their integrity. Due to incomplete SRI support for Web fonts, a third party could force a browser to load our malicious font. The font targets a particular cryptographic fingerprint and renders it as a desired different fingerprint. This allows attackers to fool users into believing that they download a genuine software package although they are actually downloading a maliciously modified version. Finally, we propose countermeasures that could be deployed to protect the integrity of Web fonts.

Quantum information exchange computer emulator is presented, which takes into consideration imperfections of real quantum channel such as noise and attenuation resulting in the necessity to increase number of photons in the impulse. The Qt Creator C++ program package provides evaluation of the ability to detect unauthorized access as well as an amount of information intercepted by intruder.

We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques.

Software development for Cyber-Physical Systems (CPS), e.g., autonomous vehicles, requires both functional and non-functional quality assurance to guarantee that the CPS operates safely and effectively. EAST-ADL is a domain specific architectural language dedicated to safety-critical automotive embedded system design. We have previously modified EAST-ADL to include energy constraints and transformed energy-aware real-time (ERT) behaviors modeled in EAST-ADL/Stateflow into UPPAAL models amenable to formal verification. Previous work is extended in this paper by including support for Simulink and an integration of Simulink/Stateflow (S/S) within the same too lchain. S/S models are transformed, based on the extended ERT constraints with probability parameters, into verifiable UPPAAL-SMC models and integrate the translation with formal statistical analysis techniques: Probabilistic extension of EAST-ADL constraints is defined as a semantics denotation. A set of mapping rules is proposed to facilitate the guarantee of translation. Formal analysis on both functional- and non-functional properties is performed using Simulink Design Verifier and UPPAAL-SMC. Our approach is demonstrated on the autonomous traffic sign recognition vehicle case study.

Clone-and-own approach is a natural way of source code reuse for software developers. To assess how known bugs and security vulnerabilities of a cloned component affect an application, developers and security analysts need to identify an original version of the component and understand how the cloned component is different from the original one. Although developers may record the original version information in a version control system and/or directory names, such information is often either unavailable or incomplete. In this research, we propose a code search method that takes as input a set of source files and extracts all the components including similar files from a software ecosystem (i.e., a collection of existing versions of software packages). Our method employs an efficient file similarity computation using b-bit minwise hashing technique. We use an aggregated file similarity for ranking components. To evaluate the effectiveness of this tool, we analyzed 75 cloned components in Firefox and Android source code. The tool took about two hours to report the original components from 10 million files in Debian GNU/Linux packages. Recall of the top-five components in the extracted lists is 0.907, while recall of a baseline using SHA-1 file hash is 0.773, according to the ground truth recorded in the source code repositories.

Software discovery is a key management function to ensure that systems are free of vulnerabilities, comply with licensing requirements, and support advanced search for systems containing given software. Today, software is predominantly discovered through querying package management tools, or using rules that check for file metadata or contents. These approaches are inadequate as not every software is installed through package managers, and agile development practices lead to frequent deployment of software. Other approaches to software discovery use machine learning methods requiring training phase, or require maintaining knowledge bases. Columbus uses the knowledge of the software packaging practices that evolved over time, and uses the information embedded in the file system impression created by a software package to discover it. Columbus is able to discover software in 92% of all official Docker images. Further, Columbus can be used in problem diagnosis and drift detection situations to compare two different systems, or to determine the evolution of a system overtime.

South Africa's lead-users predilections to tinker and innovate mobile banking services is driven by various constructs. Advanced technologies have made mobile banking services easy to use, attractive and beneficial. While this is welcome news to many, there are concerns that when lead-users tinker with these services, information security risks are exacerbated. The aim of this article is to present an insightful understanding of the demand-side predilections of South Africa's lead-users in such contexts. We assimilate the theories of Usage Control, (UCON), the Theory of Technology Acceptance Model (TAM), and the Theory of Perceived Risk (TPP) to explain predilections over technology. We demonstrate that constructs derived from these theories can explain the general demand-side predilection to tinker with mobile banking services. A quantitative approach was used to test this. From a sample of South African banking lead-users operating in Gauteng province of South Africa, data was collected and analysed with the help of a software package. We found unexpectedly that, lead-users predilections to tinker with mobile banking services was inhibited by perceived risk. Moreover, male lead-users were more domineering in the tinkering process than female lead-users. The implication for this is discussed and explained in the main body of work.