2,796 research outputs found
A critical review of cyber-physical security for building automation systems
Modern Building Automation Systems (BASs), as the brain that enables the
smartness of a smart building, often require increased connectivity both among
system components as well as with outside entities, such as optimized
automation via outsourced cloud analytics and increased building-grid
integrations. However, increased connectivity and accessibility come with
increased cyber security threats. BASs were historically developed as closed
environments with limited cyber-security considerations. As a result, BASs in
many buildings are vulnerable to cyber-attacks that may cause adverse
consequences, such as occupant discomfort, excessive energy usage, and
unexpected equipment downtime. Therefore, there is a strong need to advance the
state-of-the-art in cyber-physical security for BASs and provide practical
solutions for attack mitigation in buildings. However, an inclusive and
systematic review of BAS vulnerabilities, potential cyber-attacks with impact
assessment, detection & defense approaches, and cyber-secure resilient control
strategies is currently lacking in the literature. This review paper fills the
gap by providing a comprehensive up-to-date review of cyber-physical security
for BASs at three levels in commercial buildings: management level, automation
level, and field level. The general BASs vulnerabilities and protocol-specific
vulnerabilities for the four dominant BAS protocols are reviewed, followed by a
discussion on four attack targets and seven potential attack scenarios. The
impact of cyber-attacks on BASs is summarized as signal corruption, signal
delaying, and signal blocking. The typical cyber-attack detection and defense
approaches are identified at the three levels. Cyber-secure resilient control
strategies for BASs under attack are categorized into passive and active
resilient control schemes. Open challenges and future opportunities are finally
discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro
Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study
This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives
GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems
The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations
Cybersecurity: Past, Present and Future
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-
Toward Reliable, Secure, and Energy-Efficient Multi-Core System Design
Computer hardware researchers have perennially focussed on improving the performance of computers while stipulating the energy consumption under a strict budget. While several innovations over the years have led to high performance and energy efficient computers, more challenges have also emerged as a fallout. For example, smaller transistor devices in modern multi-core systems are afflicted with several reliability and security concerns, which were inconceivable even a decade ago. Tackling these bottlenecks happens to negatively impact the power and performance of the computers. This dissertation explores novel techniques to gracefully solve some of the pressing challenges of the modern computer design. Specifically, the proposed techniques improve the reliability of on-chip communication fabric under a high power supply noise, increase the energy-efficiency of low-power graphics processing units, and demonstrate an unprecedented security loophole of the low-power computing paradigm through rigorous hardware-based experiments
Unintended Memorization and Timing Attacks in Named Entity Recognition Models
Named entity recognition models (NER), are widely used for identifying named
entities (e.g., individuals, locations, and other information) in text
documents. Machine learning based NER models are increasingly being applied in
privacy-sensitive applications that need automatic and scalable identification
of sensitive information to redact text for data sharing. In this paper, we
study the setting when NER models are available as a black-box service for
identifying sensitive information in user documents and show that these models
are vulnerable to membership inference on their training datasets. With updated
pre-trained NER models from spaCy, we demonstrate two distinct membership
attacks on these models. Our first attack capitalizes on unintended
memorization in the NER's underlying neural network, a phenomenon NNs are known
to be vulnerable to. Our second attack leverages a timing side-channel to
target NER models that maintain vocabularies constructed from the training
data. We show that different functional paths of words within the training
dataset in contrast to words not previously seen have measurable differences in
execution time. Revealing membership status of training samples has clear
privacy implications, e.g., in text redaction, sensitive words or phrases to be
found and removed, are at risk of being detected in the training dataset. Our
experimental evaluation includes the redaction of both password and health
data, presenting both security risks and privacy/regulatory issues. This is
exacerbated by results that show memorization with only a single phrase. We
achieved 70% AUC in our first attack on a text redaction use-case. We also show
overwhelming success in the timing attack with 99.23% AUC. Finally we discuss
potential mitigation approaches to realize the safe use of NER models in light
of the privacy and security implications of membership inference attacks.Comment: This is the full version of the paper with the same title accepted
for publication in the Proceedings of the 23rd Privacy Enhancing Technologies
Symposium, PETS 202
Fine-grained reasoning about the security and usability trade-off in modern security tools
Defense techniques detect or prevent attacks based on their ability to model the attacks. A balance between security and usability should always be established in any kind of defense technique. Attacks that exploit the weak points in security tools are very powerful and thus can go undetected. One source of those weak points in security tools comes when security is compromised for usability reasons, where if a security tool completely secures a system against attacks the whole system will not be usable because of the large false alarms or the very restricted policies it will create, or if the security tool decides not to secure a system against certain attacks, those attacks will simply and easily succeed. The key contribution of this dissertation is that it digs deeply into modern security tools and reasons about the inherent security and usability trade-offs based on identifying the low-level, contributing factors to known issues. This is accomplished by implementing full systems and then testing those systems in realistic scenarios. The thesis that this dissertation tests is that we can reason about security and usability trade-offs in fine-grained ways by building and testing full systems. Furthermore, this dissertation provides practical solutions and suggestions to reach a good balance between security and usability. We study two modern security tools, Dynamic Information Flow Tracking (DIFT) and Antivirus (AV) software, for their importance and wide usage. DIFT is a powerful technique that is used in various aspects of security systems. It works by tagging certain inputs and propagating the tags along with the inputs in the target system. However, current DIFT systems do not track implicit information flow because if all DIFT propagation rules are directly applied in a conservative way, the target system will be full of tagged data (a problem called overtagging) and thus useless because the tags tell us very little about the actual information flow of the system. So, current DIFT systems drop some security for usability. In this dissertation, we reason about the sources of the overtagging problem and provide practical ways to deal with it, while previous approaches have focused on abstract descriptions of the main causes of the problem based on limited experiments. The second security tool we consider in this dissertation is antivirus (AV) software. AV is a very important tool that protects systems against worms and viruses by scanning data against a database of signatures. Despite its importance and wide usage, AV has received little attention from the security research community. In this dissertation, we examine the AV internals and reason about the possibility of creating timing channel attacks against AV software. The attacker could infer information about the AV based only on the scanning time the AV spends to scan benign inputs. The other aspect of AV this dissertation explores is the low-level AV performance impact on systems. Even though the performance overhead of AV is a well known issue, the exact reasons behind this overhead are not well-studied. In this dissertation, we design a methodology that utilizes Event Tracing for Windows technology (ETW), a technology that accounts for all OS events, to reason about AV performance impact from the OS point of view. We show that the main performance impact of the AV on a task is the longer waiting time the task spends waiting on events
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