Reset
Showing 20 of 1,215 academic articles

The right type of trust for distributed systems

Audun Jøsang • New Security Paradigms Workshop (1996)

Research in information security has traditionally focused on where to place or how to propagate trust.In that sense, a cyptographic algorithm or protocol is simply a mechanism to transfer trust from where it exists to where it is needed.Th...

OPENALEX Open Access DOI: https://doi.org/10.1145/304851.304877

Distributed Systems: An Algorithmic Approach

Shrisha Rao • IEEE Distributed Systems Online (2008)

Distributed systems: an algorithmic approach is an important addition to the distributed computing literature. The book offers a broad overview of important distributed computing topics, and, where relevant, a touch of networking topics as ...

OPENALEX DOI: https://doi.org/10.1109/mdso.2008.30

Machine learning in cybersecurity: a comprehensive survey

Dipankar Dasgupta, Zahid Akhtar, Sajib Sen • The Journal of Defense Modeling and Simulation Applications Methodology Technology (2020)

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...

OPENALEX DOI: https://doi.org/10.1177/1548512920951275

Quantum Computing for Cybersecurity

Mohammad Alauthman, Ammar Almomani, Ahmad Al–Qerem, Mohammad Ali Al Khaldy et al. • Advances in information security, privacy, and ethics book series (2024)

This chapter explores the impact of quantum computing on cybersecurity, comparing classical and quantum techniques. It provides an overview of the current quantum landscape, focusing on the threats posed by quantum algorithms to traditional...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-5330-1.ch004

Anomaly Detection for Cybersecurity of the Substations

Chee‐Wooi Ten, Junho Hong, Chen‐Ching Liu • IEEE Transactions on Smart Grid (2011)

Cybersecurity of the substations in a power system is a major issue as the substations become increasingly dependent on computer and communication networks. This paper is concerned with anomaly detection in the computer network environment ...

OPENALEX DOI: https://doi.org/10.1109/tsg.2011.2159406

Hacking the Human: The Prevalence Paradox in Cybersecurity

Ben D. Sawyer, Peter A. Hancock • Human Factors The Journal of the Human Factors and Ergonomics Society (2018)

OBJECTIVE: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. BACKGROUND: Under the prevalence effect, rare signals are more difficult to detect, even when t...

OPENALEX Open Access DOI: https://doi.org/10.1177/0018720818780472

From physical security to cybersecurity

Arunesh Sinha, Thanh H. Nguyen, Debarun Kar, Matthew Brown et al. • Journal of Cybersecurity (2015)

Security is a critical concern around the world. In many domains from cybersecurity to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or alloca...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1093/cybsec/tyv007

Machine learning in cybersecurity: A review

Anand Handa, Ashu Sharma, Sandeep K. Shukla • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2019)

Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat a...

OPENALEX DOI: https://doi.org/10.1002/widm.1306

Explainable artificial intelligence for cybersecurity: a literature survey

Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications (2022)

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12243-022-00926-7

Automation of a problem list using natural language processing

Stéphane M. Meystre, Peter John Haug • BMC Medical Informatics and Decision Making (2005)

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However,...

OPENALEX cc-by DOI: https://doi.org/10.1186/1472-6947-5-30 PMC: 1208893

Natural Language Processing

Joydeep Bhattacharjee • Apress eBooks (2019)

In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or cat...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-5121-8_5

Suicide Note Classification Using Natural Language Processing: A Content Analysis

John P Pestian, Henry A. Nasrallah, Paweł Matykiewicz, Aurora J. Bennett et al. • Biomedical Informatics Insights (2010)

Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of r...

OPENALEX cc-by-nc DOI: https://doi.org/10.4137/bii.s4706 PMC: 3107011

NATURAL LANGUAGE PROCESSING

V Ranjit, Suchitra Naik • International Journal of Computing Algorithm (2023)

A branch of computer science and AI called natural language processing is focused on how computers and human languages interact.In order for computers to interpret written and spoken human language without the use of computer-driven languag...

OPENALEX DOI: https://doi.org/10.20894/ijcoa.101.012.002.001

Weight Uncertainty in Neural Networks

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra • arXiv (Cornell University) (2015)

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1505.05424