191 research outputs found

    Cybersecurity: Past, Present and Future

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    The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

    Securely extending and running low-code applications with C#

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    Low-code development platforms provide an accessible infrastructure for the creation of software by domain experts, also called "citizen developers", without the need for formal programming education. Development is facilitated through graphical user interfaces, although traditional programming can still be used to extend low-code applications, for example when external services or complex business logic needs to be implemented that cannot be realized with the features available on a platform. Since citizen developers are usually not specifically trained in software development, they require additional support when writing code, particularly with regard to security and advanced techniques like debugging or versioning. In this thesis, several options to assist developers of low-code applications are investigated and implemented. A framework to quickly build code editor extensions is developed, and an approach to leverage the Roslyn compiler platform to implement custom static code analysis rules for low-code development platforms using the .NET platform is demonstrated. Furthermore, a sample application showing how Roslyn can be used to build a simple, integrated debugging tool, as well as an abstraction of the version control system Git for easier usage by citizen developers, is implemented. Security is a critical aspect when low-code applications are deployed. To provide an overview over possible options to ensure the secure and isolated execution of low-code applications, a threat model is developed and used as the basis for a comparison between OS-level virtualization, sandboxing, and runtime code security implementations

    Facilitating and Enhancing the Performance of Model Selection for Energy Time Series Forecasting in Cluster Computing Environments

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    Applying Machine Learning (ML) manually to a given problem setting is a tedious and time-consuming process which brings many challenges with it, especially in the context of Big Data. In such a context, gaining insightful information, finding patterns, and extracting knowledge from large datasets are quite complex tasks. Additionally, the configurations of the underlying Big Data infrastructure introduce more complexity for configuring and running ML tasks. With the growing interest in ML the last few years, particularly people without extensive ML expertise have a high demand for frameworks assisting people in applying the right ML algorithm to their problem setting. This is especially true in the field of smart energy system applications where more and more ML algorithms are used e.g. for time series forecasting. Generally, two groups of non-expert users are distinguished to perform energy time series forecasting. The first one includes the users who are familiar with statistics and ML but are not able to write the necessary programming code for training and evaluating ML models using the well-known trial-and-error approach. Such an approach is time consuming and wastes resources for constructing multiple models. The second group is even more inexperienced in programming and not knowledgeable in statistics and ML but wants to apply given ML solutions to their problem settings. The goal of this thesis is to scientifically explore, in the context of more concrete use cases in the energy domain, how such non-expert users can be optimally supported in creating and performing ML tasks in practice on cluster computing environments. To support the first group of non-expert users, an easy-to-use modular extendable microservice-based ML solution for instrumenting and evaluating ML algorithms on top of a Big Data technology stack is conceptualized and evaluated. Our proposed solution facilitates applying trial-and-error approach by hiding the low level complexities from the users and introduces the best conditions to efficiently perform ML tasks in cluster computing environments. To support the second group of non-expert users, the first solution is extended to realize meta learning approaches for automated model selection. We evaluate how meta learning technology can be efficiently applied to the problem space of data analytics for smart energy systems to assist energy system experts which are not data analytics experts in applying the right ML algorithms to their data analytics problems. To enhance the predictive performance of meta learning, an efficient characterization of energy time series datasets is required. To this end, Descriptive Statistics Time based Meta Features (DSTMF), a new kind of meta features, is designed to accurately capture the deep characteristics of energy time series datasets. We find that DSTMF outperforms the other state-of-the-art meta feature sets introduced in the literature to characterize energy time series datasets in terms of the accuracy of meta learning models and the time needed to extract them. Further enhancement in the predictive performance of the meta learning classification model is achieved by training the meta learner on new efficient meta examples. To this end, we proposed two new approaches to generate new energy time series datasets to be used as training meta examples by the meta learner depending on the type of time series dataset (i.e. generation or energy consumption time series). We find that extending the original training sets with new meta examples generated by our approaches outperformed the case in which the original is extended by new simulated energy time series datasets

    Towards a circular economy: fabrication and characterization of biodegradable plates from sugarcane waste

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    Bagasse pulp is a promising material to produce biodegradable plates. Bagasse is the fibrous residue that remains after sugarcane stalks are crushed to extract their juice. It is a renewable resource and is widely available in many countries, making it an attractive alternative to traditional plastic plates. Recent research has shown that biodegradable plates made from Bagasse pulp have several advantages over traditional plastic plates. For example, they are more environmentally friendly because they are made from renewable resources and can be composted after use. Additionally, they are safer for human health because they do not contain harmful chemicals that can leach into food. The production process for Bagasse pulp plates is also relatively simple and cost-effective. Bagasse is first collected and then processed to remove impurities and extract the pulp. The pulp is then molded into the desired shape and dried to form a sturdy plate. Overall, biodegradable plates made from Bagasse pulp are a promising alternative to traditional plastic plates. They are environmentally friendly, safe for human health, and cost-effective to produce. As such, they have the potential to play an important role in reducing plastic waste and promoting sustainable practices. Over the years, the world was not paying strict attention to the impact of rapid growth in plastic use. As a result, uncontrollable volumes of plastic garbage have been released into the environment. Half of all plastic garbage generated worldwide is made up of packaging materials. The purpose of this article is to offer an alternative by creating bioplastic goods that can be produced in various shapes and sizes across various sectors, including food packaging, single-use tableware, and crafts. Products made from bagasse help address the issue of plastic pollution. To find the optimum option for creating bagasse-based biodegradable dinnerware in Egypt and throughout the world, researchers tested various scenarios. The findings show that bagasse pulp may replace plastics in biodegradable packaging. As a result of this value-added utilization of natural fibers, less waste and less of it ends up in landfills. The practical significance of this study is to help advance low-carbon economic solutions and to produce secure bioplastic materials that can replace Styrofoam in tableware and food packaging production

    Investigating and mitigating the role of neutralisation techniques on information security policies violation in healthcare organisations

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    Healthcare organisations today rely heavily on Electronic Medical Records systems (EMRs), which have become highly crucial IT assets that require significant security efforts to safeguard patients’ information. Individuals who have legitimate access to an organisation’s assets to perform their day-to-day duties but intentionally or unintentionally violate information security policies can jeopardise their organisation’s information security efforts and cause significant legal and financial losses. In the information security (InfoSec) literature, several studies emphasised the necessity to understand why employees behave in ways that contradict information security requirements but have offered widely different solutions. In an effort to respond to this situation, this thesis addressed the gap in the information security academic research by providing a deep understanding of the problem of medical practitioners’ behavioural justifications to violate information security policies and then determining proper solutions to reduce this undesirable behaviour. Neutralisation theory was used as the theoretical basis for the research. This thesis adopted a mixed-method research approach that comprises four consecutive phases, and each phase represents a research study that was conducted in light of the results from the preceding phase. The first phase of the thesis started by investigating the relationship between medical practitioners’ neutralisation techniques and their intention to violate information security policies that protect a patient’s privacy. A quantitative study was conducted to extend the work of Siponen and Vance [1] through a study of the Saudi Arabia healthcare industry. The data was collected via an online questionnaire from 66 Medical Interns (MIs) working in four academic hospitals. The study found that six neutralisation techniques—(1) appeal to higher loyalties, (2) defence of necessity, (3) the metaphor of ledger, (4) denial of responsibility, (5) denial of injury, and (6) condemnation of condemners—significantly contribute to the justifications of the MIs in hypothetically violating information security policies. The second phase of this research used a series of semi-structured interviews with IT security professionals in one of the largest academic hospitals in Saudi Arabia to explore the environmental factors that motivated the medical practitioners to evoke various neutralisation techniques. The results revealed that social, organisational, and emotional factors all stimulated the behavioural justifications to breach information security policies. During these interviews, it became clear that the IT department needed to ensure that security policies fit the daily tasks of the medical practitioners by providing alternative solutions to ensure the effectiveness of those policies. Based on these interviews, the objective of the following two phases was to improve the effectiveness of InfoSec policies against the use of behavioural justification by engaging the end users in the modification of existing policies via a collaborative writing process. Those two phases were conducted in the UK and Saudi Arabia to determine whether the collaborative writing process could produce a more effective security policy that balanced the security requirements with daily business needs, thus leading to a reduction in the use of neutralisation techniques to violate security policies. The overall result confirmed that the involvement of the end users via a collaborative writing process positively improved the effectiveness of the security policy to mitigate the individual behavioural justifications, showing that the process is a promising one to enhance security compliance

    Principled Flow Tracking in IoT and Low-Level Applications

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    Significant fractions of our lives are spent digitally, connected to and dependent on Internet-based applications, be it through the Web, mobile, or IoT. All such applications have access to and are entrusted with private user data, such as location, photos, browsing habits, private feed from social networks, or bank details.In this thesis, we focus on IoT and Web(Assembly) apps. We demonstrate IoT apps to be vulnerable to attacks by malicious app makers who are able to bypass the sandboxing mechanisms enforced by the platform to stealthy exfiltrate user data. We further give examples of carefully crafted WebAssembly code abusing the semantics to leak user data.We are interested in applying language-based technologies to ensure application security due to the formal guarantees they provide. Such technologies analyze the underlying program and track how the information flows in an application, with the goal of either statically proving its security, or preventing insecurities from happening at runtime. As such, for protecting against the attacks on IoT apps, we develop both static and dynamic methods, while for securing WebAssembly apps we describe a hybrid approach, combining both.While language-based technologies provide strong security guarantees, they are still to see a widespread adoption outside the academic community where they emerged.In this direction, we outline six design principles to assist the developer in choosing the right security characterization and enforcement mechanism for their system.We further investigate the relative expressiveness of two static enforcement mechanisms which pursue fine- and coarse-grained approaches for tracking the flow of sensitive information in a system.\ua0Finally, we provide the developer with an automatic method for reducing the manual burden associated with some of the language-based enforcements

    A sense of self for power side-channel signatures: instruction set disassembly and integrity monitoring of a microcontroller system

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    Cyber-attacks are on the rise, costing billions of dollars in damages, response, and investment annually. Critical United States National Security and Department of Defense weapons systems are no exception, however, the stakes go well beyond financial. Dependence upon a global supply chain without sufficient insight or control poses a significant issue. Additionally, systems are often designed with a presumption of trust, despite their microelectronics and software-foundations being inherently untrustworthy. Achieving cybersecurity requires coordinated and holistic action across disciplines commensurate with the specific systems, mission, and threat. This dissertation explores an existing gap in low-level cybersecurity while proposing a side-channel based security monitor to support attack detection and the establishment of trusted foundations for critical embedded systems. Background on side-channel origins, the more typical side-channel attacks, and microarchitectural exploits are described. A survey of related side-channel efforts is provided through side-channel organizing principles. The organizing principles enable comparison of dissimilar works across the side-channel spectrum. We find that the maturity of existing side-channel security monitors is insufficient, as key transition to practice considerations are often not accounted for or resolved. We then document the development, maturation, and assessment of a power side-channel disassembler, Time-series Side-channel Disassembler (TSD), and extend it for use as a security monitor, TSD-Integrity Monitor (TSD-IM). We also introduce a prototype microcontroller power side-channel collection fixture, with benefits to experimentation and transition to practice. TSD-IM is finally applied to a notional Point of Sale (PoS) application for proof of concept evaluation. We find that TSD and TSD-IM advance state of the art for side-channel disassembly and security monitoring in open literature. In addition to our TSD and TSD-IM research on microcontroller signals, we explore beneficial side-channel measurement abstractions as well as the characterization of the underlying microelectronic circuits through Impulse Signal Analysis (ISA). While some positive results were obtained, we find that further research in these areas is necessary. Although the need for a non-invasive, on-demand microelectronics-integrity capability is supported, other methods may provide suitable near-term alternatives to ISA

    Cyber Security

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    This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification

    Information Leakage Attacks and Countermeasures

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    The scientific community has been consistently working on the pervasive problem of information leakage, uncovering numerous attack vectors, and proposing various countermeasures. Despite these efforts, leakage incidents remain prevalent, as the complexity of systems and protocols increases, and sophisticated modeling methods become more accessible to adversaries. This work studies how information leakages manifest in and impact interconnected systems and their users. We first focus on online communications and investigate leakages in the Transport Layer Security protocol (TLS). Using modern machine learning models, we show that an eavesdropping adversary can efficiently exploit meta-information (e.g., packet size) not protected by the TLS’ encryption to launch fingerprinting attacks at an unprecedented scale even under non-optimal conditions. We then turn our attention to ultrasonic communications, and discuss their security shortcomings and how adversaries could exploit them to compromise anonymity network users (even though they aim to offer a greater level of privacy compared to TLS). Following up on these, we delve into physical layer leakages that concern a wide array of (networked) systems such as servers, embedded nodes, Tor relays, and hardware cryptocurrency wallets. We revisit location-based side-channel attacks and develop an exploitation neural network. Our model demonstrates the capabilities of a modern adversary but also presents an inexpensive tool to be used by auditors for detecting such leakages early on during the development cycle. Subsequently, we investigate techniques that further minimize the impact of leakages found in production components. Our proposed system design distributes both the custody of secrets and the cryptographic operation execution across several components, thus making the exploitation of leaks difficult
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