From physical security to cybersecurity

Sinha, Arunesh; Nguyen, Thanh H.; Kar, Debarun; Brown, Matthew; Tambe, Milind; Jiang, Albert Xin

doi:10.1093/cybsec/tyv007

Cited by 24 publications

(24 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is a model for defender-adversary interaction where the defender lays out her strategy (in this paper the classifier) and the adversary responds optimally (choose a least cost evasion). Stackelberg games also allow for scalability compared to the corresponding simultaneous move game [90], [91].…”

Section: A Robustness Of Models To Distribution Driftsmentioning

confidence: 99%

SoK: Security and Privacy in Machine Learning

Papernot

McDaniel

Sinha

et al. 2018

2018 IEEE European Symposium on Security and Privacy (EuroS&P)

Self Cite

563

471

View full text Add to dashboard Cite

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive-new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used.

show abstract

Section: A Robustness Of Models To Distribution Driftsmentioning

confidence: 99%

SoK: Security and Privacy in Machine Learning

Papernot

McDaniel

Sinha

et al. 2018

2018 IEEE European Symposium on Security and Privacy (EuroS&P)

Self Cite

563

471

View full text Add to dashboard Cite

show abstract

“…These methods are similar to patrolling methods that try to identify security violation in physical security domains like airports, wildlife sanctuaries, etc. [83].…”

Section: A What To Switch? ≈ What Actions To Play?mentioning

confidence: 99%

A Survey of Moving Target Defenses for Network Security

Sengupta

Chowdhary

Sabur

et al. 2020

IEEE Commun. Surv. Tutorials

182

View full text Add to dashboard Cite

Network defense techniques based on traditional tools, techniques, and procedures (TTP) fail to account for the attacker's inherent advantage present due to static nature of network services and configurations. Moving Target Defense (MTD), on the other hand, provides an intelligent countermeasure by dynamically re-configuring the underlying systems, thereby reducing the effectiveness of cyber attacks. In this survey, we analyze the recent advancements made in the development of MTD tools, techniques and procedures (TTP) and highlight how these defenses can be made more effective with the use of artificial intelligence techniques for decision making. We first define a unified formal notation for MTDs that can capture different aspects of such defenses. We then categorize these defenses into different sub-classes depending on how they answer the three questions-what to move, when to move and how to moveshowcasing how game theoretic strategies can effectively answer the latter question. To understand the usefulness of these defense methods, we study the implementation of such MTD techniques. We find that (relatively) new networking technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) provide effective means for implementing these dynamic defense methods. To encourage researchers and industry experts in using such defenses, we highlight industry use-cases and discuss the practicality and maturity of these defenses. To aid readers who want to test or deploy MTD techniques, we highlight existing MTD test-beds. Our survey then performs both a qualitative and quantitative analysis to better understand the effectiveness of these MTDs in terms of security and performance. To that extent, we use well-defined metrics such as risk analysis, performance costs for qualitative evaluation and metrics based on Confidentiality, Integrity, Availability (CIA), attack representation, QoS impact, targeted threat models and defense cost for quantitative evaluation. Finally, we conclude by summarizing research opportunities that our survey elucidates.

show abstract

“…At the same time, the specific is revealed by the condition that is connected with a special environment of this negative phenomenon functioning that is represented by cyberspace and involved technologies, the use of which should be as safe as possible for the whole county and every member of its society. Security is a critical concern around the world that manifests in problems such as protecting our cyber infrastructure from attacks by criminals and other nation-states; protecting our ports, airports, public transportation, and other critical national infrastructure from terrorists; protecting our wildlife and forests from poachers and smugglers; and curtailing the illegal flow of weapons, drugs, and money across international borders (Sinha et al, 2015).…”

Section: Role and Significance Of Corruption As A Cybersecurity Threatmentioning

confidence: 99%

Corruption as a cybersecurity threat in conditions of the new world's order

2021

View full text Add to dashboard Cite

The relevance of the research topic is stipulated by the need to conduct the scientific research of a state and issues of modern cybersecurity ensuring under the fight against corruption as its threat to suggest significant directions for the further development and improvement of the cybersecurity state in the context of global challenges and the pandemic and post-pandemic world’s development. The research purpose of the paper is to determine current issues and prospective of ensuring cybersecurity under the fight against corruption as its threat in pandemic and post-pandemic world conditions. General-scientific and special-legal methods of cognition have been used. By using a dialectical method, theoretical background and current issues of cybersecurity ensuring under the fight against corruption as its threat has been investigated and the modern challenges concerning this issue have been outlined. Formal-legal and comparative methods allowed suggesting the directions of cybersecurity ensuring in conditions of the fight against corruption as its threat under the global changes, digitalization and social transformations under the innovative cyber technological development of pandemic and post-pandemic reality. Discussion concerning a concept, types, challenges, issues, and prospects of cybersecurity improvement in conditions of the modern world under the fight against corruption as its threat.

show abstract

From physical security to cybersecurity

Cited by 24 publications

References 25 publications

SoK: Security and Privacy in Machine Learning

SoK: Security and Privacy in Machine Learning

A Survey of Moving Target Defenses for Network Security

Corruption as a cybersecurity threat in conditions of the new world's order

Contact Info

Product

Resources

About