Threat Detection and Response in Linux Endpoints

Associations between collectivism and perception of social threat: a signal detection approach

10.31234/osf.io/das69 ◽

2019 ◽

Author(s):

Tuong-Van Vu ◽

Catrin Finkenauer ◽

Lydia Krabbendam

Keyword(s):

Signal Detection ◽

Response Bias ◽

Social Adjustment ◽

Emotional Expressions ◽

Social Threat ◽

Threat Detection ◽

Perceptual Sensitivity ◽

Detection Approach ◽

Self Construal ◽

The Individual

Collectivistic orientation, which entails interdependent self-construal and concern for interpersonal harmony and social adjustment, has been suggested to be associated with detecting emotional expressions that signal social threat than individualistic orientation, which entails independent self-construal. The present research tested if this detection is a result of enhanced perceptual sensitivity or of response bias. We used country as proxy of individualism and collectivism (Country IC), measured IC of individuals with a questionnaire (Individual IC) and manipulated IC with culture priming (Situational IC). Dutch participants in the Netherlands (n = 143) and Chinese participants in China (n = 151) performed a social threat detection task where they had to categorize ambiguous facial expressions as “angry” or “not angry”. As the stimuli varied in degrees of scowling and frequency of presentation, we were able to measure the participants' perceptual sensitivity and response bias following the principles of the Signal Detection Theory. On the Country IC level, the results indicated that individualism-representative Dutch participants had higher perceptual sensitivity than collectivism-representative Chinese participants; whereas, Chinese participants were more biased towards categorizing a scowling face as “angry” than the Dutch (i.e. stronger liberal bias). In both groups, collectivism on the Individual IC was associated with a bias towards recognizing a scowling face as “not angry” (i.e. stronger conservative bias). Culture priming (Situational IC) affected neither perceptual sensitivity nor response bias. Our data suggested that cultural differences were in the form of behavioral tendency and IC entails multiple constructs linked to different outcomes in social threat detection.

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Cyber Peacekeeping: Critical Evaluation of Digital Blue Helmets Program

NUST Journal of International Peace and Stability ◽

10.37540/njips.v3i2.53 ◽

2020 ◽

pp. 17-27

Author(s):

Fahad Nabeel

Keyword(s):

United Nations ◽

Critical Evaluation ◽

Threat Detection ◽

Information And Communications Technologies ◽

The Past ◽

Research Article ◽

Un Peacekeeping ◽

Starting Point ◽

Communications Technologies ◽

Future Potential

In 2016, the United Nations (UN) launched the Digital Blue Helmets (DBH) program under its Office of Information and Communications Technologies (OICT). The launching of DBH was a continuation of a series of steps that the UN and its related agencies and departments have undertaken over the past decade to incorporate cyberspace within their working methodologies. At the time of inception, DBH was envisioned as a team capacitated to act as a replica of a physical peacekeeping force but for the sole purpose of overseeing cyberspace(s). Several research studies have been published in the past few years, which have conceptualized cyber peacekeeping in various ways. Some scholars have mentioned DBH as a starting point of cyber peacekeeping while some have proposed models for integration of cyber peacekeeping within the current UN peacekeeping architecture. However, no significant study has attempted to look at how DBH has evolved since its inception. This research article aims to examine the progress of DBH since its formation. It argues that despite four years since its formation, DBH is still far away from materializing its declared objectives. The article also discusses the future potential roles of DBH, including its collaboration with UN Global Pulse for cyber threat detection and prevention, and embedding the team along with physical peacekeepers.

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Harbor Threat Detection, Classification, and Identification

10.21236/ada612415 ◽

2006 ◽

Author(s):

Brian H. Houston ◽

Tim Yoder ◽

Larry Carin

Keyword(s):

Threat Detection

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Systematic Government Access to Private-Sector Data in the United States I

10.1093/oso/9780190685515.003.0008 ◽

2017 ◽

Author(s):

Stephanie K. Pell

Keyword(s):

Private Sector ◽

Public Information ◽

The United States ◽

Third Party ◽

Threat Detection ◽

Detection And Identification ◽

Potential Applications ◽

September 11 Attacks ◽

Access To Data ◽

Sector Data

After the September 11 attacks, law enforcement's mission expanded to include, at times even prioritize, the general “prevention, deterrence and disruption” of terrorist attacks, which presumed a new emphasis upon threat detection and identification by analyzing patterns in larger, less specific bodies of information. Indeed, the unprecedented level of “third-party” possession of information inevitably makes the private sector the most reliable and comprehensive source of information available to law enforcement and intelligence agencies alike. This chapter explores the potential applications of systematic government access to data held by third-party private-sector intermediaries that would not be considered public information sources but, rather, data generated based on the role these intermediaries play in facilitating economic and business transactions (including personal business, such as buying groceries or staying at a hotel on vacation).

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