Pub Crawl #48
Pub Crawl summarizes, by hard problems, sets of publications that have been peer reviewed and presented at SoS conferences or referenced in current work. The topics are chosen for their usefulness for current researchers. Select the topic name to view the corresponding list of publications. Submissions and suggestions are welcome.
Belief networks are Bayesian models that that represent sets of random variables and their conditional dependencies through a directed acyclic graph (DAG). These networks are used for modelling beliefs in complex physical networks or systems and are important to the Science of Security.
Composability of security processes is one of the five hard problems for the Science of Security.
Compressive Sampling 2020 (all)
Compressive sampling (or compressive sensing) is an important theory in signal processing. It allows efficient acquisition and reconstruction of a signal and may also be the basis for user identification. For the Science of Security, the topic has implications for resilience, cyber-physical systems, privacy, and composability.
Embedded Systems Security aims for a comprehensive view of security across hardware, platform software (including operating systems and hypervisors), software development processes, data protection protocols (both networking and storage), and cryptography. Critics say embedded device manufacturers often lack maturity when it comes to designing secure embedded systems. They say vendors in the embedded device and critical infrastructure market are starting to conduct classic threat modeling and risk analysis on their equipment, but they've not matured to the point of developing formal secure development standards. Research is beginning to bridge the gap between promise and performance, as the articles cited here suggest. For the Science of Security, this research addresses resilience, composability, and metrics.
Encryption audits not only test the validity and effectiveness of protection schemes, they also potentially provide data for developing and improving metrics about data security. For the Science of Security community, this work is relevant to helping solve the hard problems of predictive metrics, compositionality and resilience.
The expansion of a network to more nodes creates security problems. For the Science of Security community, expandability relates to resilience and compositionality.
Expert Systems and Privacy 2020 (all)
Expert systems have potential for efficiency, scalability, and economy in systems security. The research work cited here looks at the problem of privacy. For the Science of Security community, the work is relevant to scalability and human factors.
Expert Systems and Security 2020 (all)
An expert system is an artificial intelligence (AI) application that uses a knowledge base of human expertise for problem solving. Its success is based on the quality of the data and rules obtained from the human expert. Some perform above and some below the level of humans. For the Science of Security, expert systems are relevant to the hard problems of scalability, human behavior, and resilience.
Exponentiation, the mathematical operations that underlie encryption and coding, is important to the Science of Security because complexity adds delay. In creating resilient architectures, for example, slow processing may make a security feature too heavy to include. It is relevant to the hard problems of scalability and resiliency.
Facial recognition tools have long been the stuff of action-adventure films. In the real world, they present opportunities and complex problems being examined by researchers. For the Science of Security community, their work relates to the hard problems of human behavior, metrics, and resilience.
Fuzzy Logic and Security 2020 (all)
Fuzzy logic is being used to develop a number of security solutions for data security. The articles cited here include research into fuzzy logic-based security for software defined networks, industrial controls, intrusion response and recovery, wireless sensor networks, and more. They are relevant to cyber physical systems, resiliency, and metrics.
Game Theoretic Security 2020 (all)
Game theory has historically been the province of social sciences such as economics, political science, and psychology. Game theory has developed into an umbrella term for the logical side of science that includes both human and non-human actors like computers. It has been used extensively in wireless networks research to develop understanding of stable operation points for networks made of autonomous/selfish nodes. The nodes are considered as the players. Utility functions are often chosen to correspond to achieved connection rate or similar technical metrics. In security, the computer game framework is used to anticipate and analyze intruder and administrator concurrent interactions within the network. For the Science of Security community, it is relevant to human factors, predictive metrics, and scalability.
Generative Adversarial Learning 2020 (all)
AI and Machine Learning are being used to develop a wide range of applications including visual, audio, and text. The use of these methods has large security implications. Research into the security aspects is relevant to the Science of Security hard problems of resilience, predictive metrics, and scalability.
Hashing algorithms are used extensively in information security and forensics. Research focuses on new methods and techniques to optimize security. For the Science of Security community, this work is relevant to compositionality and resilience.
Human Behavior and Security 2020 (all)
Human behavior and its impact on cybersecurity is a hard problem in the Science of Security.
ICS Anomaly Detection 2020 (all)
Industrial control systems are a vital part of the critical infrastructure. Anomaly detection in these systems is required to successfully build resilient and scalable systems. The work cited here addresses these two hard problems in the Science of Security.
The term identity management refers to the management of individual identities, their roles, authentication, authorizations and privileges within or across systems. For the Science of Security community, this research is relevant to scalability, resilience, and human behavior.
Intrusion detection systems defend communications, computer, and other information systems against malicious attacks by identifying attacks and attackers. The topic relates to the Science of Security issues of resilience and composability.
Information Assurance 2020 (all)
The term “information Assurance” was adopted in the late 1990’s to cover what is often now referred to generically as “cybersecurity.” Many still use the phrase, particularly in the U.S. government, both for teaching and research. Since it is a rather generic phrase, there is a wide area of coverage under this topic. As such, it touches all of the hard problems in the Science of Security.
Information Centric Networks 2020 (all)
The move from host-centric to information-centric network security has major implications for the Science of Security community relative to scalability and resilience.
Information Forensics 2020 (all)
Forensics is an important tool for tracking and evaluating past attacks and using the information gained to resolve hard problems in the Science of Security related to resilience, metrics, human behavior, and scalability.
Information Reuse and Security 2020 (all)
The objective of information reuse is to maximize the value of information by creating simple, rich, and reusable knowledge representations and integrating it into systems and applications. With reuse comes inherent security risk. For the Science of Security community, this problem is relevant to compositionality and resiliency.
Information Theoretic Security 2020 (all)
A cryptosystem is said to be information-theoretically secure if its security derives purely from information theory and cannot be broken even when the adversary has unlimited computing power. For example, the one-time pad is an information-theoretically secure cryptosystem proven by Claude Shannon, inventor of information theory, to be secure. Information-theoretically secure cryptosystems are often used for the most sensitive communications such as diplomatic cables and high-level military communications, because of the great efforts enemy governments expend toward breaking them. Because of this importance, methods, theory and practice in information theory security also remains high. It is fundamentally related to the concept of Science of Security and all the hard problems.
Insider threats are a difficult problem. The research cited here looks at both intentional and accidental threats, including the effects of social engineering, and methods of identifying potential threats. For the Science of Security, insider threat relates to human behavior, as well as metrics, policy-based governance, and resilience.
Science of Security 2019 (all)
Many more articles and research studies are appearing with “Science of Security” as a keyword. The articles cited here discuss the degree to which security is a science and various issues surrounding its development, ranging from basic approach to essential elements. The articles cited here address the fundamental concepts of the Science of Security.
The ability to conduct automated security audits rapidly and accurately helps to reduce the time between attack and its detection, hopefully reducing the consequences of the attack. Research into security audit methods and techniques supports addressing the hard problem of human behavior, as well as resiliency and scalability.
Searchable Encryption 2020 (all)
Searchable encryption allows one to store encrypted data externally, but still allow for easy data searches that do not require the search to download everything before decrypting and to allow others to search data without having access to plaintext. As an application, it is becoming increasingly important in the Cloud environment. For the Science of Security community, it is an area of research related to cryptography, resilience, and composability.
Security Heuristics 2020 (all)
Heuristic analysis is a method employed by many computer antivirus programs designed to detect “Zero Day” or previously unknown computer viruses and new variants of viruses already “in the wild." It is an expert-based analytic method that uses various decision rules or weighing methods. For the Science of Security community, it is relevant to the hard problems of resilience, scalability, and predictability.
Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests for removal via email of the links or modifications to specific citations. Please include the URL of the specific citation in your correspondence.
Pub Crawl contains bibliographical citations, abstracts if available, links on specific topics, and research problems of interest to the Science of Security community.
How recent are these publications?
These bibliographies include recent scholarly research on topics that have been presented or published within the stated year. Some represent updates from work presented in previous years; others are new topics.
How are topics selected?
The specific topics are selected from materials that have been peer reviewed and presented at SoS conferences or referenced in current work. The topics are also chosen for their usefulness for current researchers.
How can I submit or suggest a publication?
Researchers willing to share their work are welcome to submit a citation, abstract, and URL for consideration and posting, and to identify additional topics of interest to the community. Researchers are also encouraged to share this request with their colleagues and collaborators.
What are the hard problems?
Select a hard problem to retrieve related publications.
- - Scalability and Composability: Develop methods to enable the construction of secure systems with known security properties from components with known security properties, without a requirement to fully re-analyze the constituent components.
- - Policy-Governed Secure Collaboration: Develop methods to express and enforce normative requirements and policies for handling data with differing usage needs and among users in different authority domains.
- - Security Metrics Driven Evaluation, Design, Development, and Deployment: Develop security metrics and models capable of predicting whether or confirming that a given cyber system preserves a given set of security properties (deterministically or probabilistically), in a given context.
- - Resilient Architectures: Develop means to design and analyze system architectures that deliver required service in the face of compromised components.
- - Understanding and Accounting for Human Behavior: Develop models of human behavior (of both users and adversaries) that enable the design, modeling, and analysis of systems with specified security properties.