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Record W2776249600 · doi:10.5937/nabepo22-12060

Internet as a method of trolling offensive intelligence operations in cyberspace

2017· article· en· W2776249600 on OpenAlexaboutno aff
Dragan Djurdjevic, Miroslav Stevanović

Bibliographic record

VenueNauka bezbednost policija · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCyberspaceOffensiveThe InternetCovertNational securityComputer securityMilitary doctrineComputer scienceMilitary intelligencePoliticsInformation OperationsDoctrineContextualizationProfiling (computer programming)Public relationsPolitical scienceEngineeringOperations researchWorld Wide WebLaw

Abstract

fetched live from OpenAlex

The paper analyzes Internet trolling as an operational intelligence activity, and the challenges it presents for national security, as well as the assessment of possible strategic protection of national cyberspace. This problem arises since collecting the data through automated programs eliminates guarantees of ethical grounds for their gathering in terms of clear reason, integrity of motives, proportionality of methods and the relevant authority. The basic thesis is that intelligence gathering on the Internet may be used against the basic values of states. Functionally, due to characteristics of the targets, trolling is conducive for collecting strategic information related to individual and collective attitudes and their contextualization; or the economic entities and critical infrastructure of national crisis management system, as well as for the influence on the political decisions. Also, because of the network properties, it is suitable for identifying, locating of potential sources of information and gaining their cooperation on the basis of motivation to support the objectives. The tasks of cyber data collection include psychological profiling, imposing attitudes, conducting secret surveillance on a massive scale and interception of communications. Internet trolling enables an access to primary data on the territory of other states, and thus it is suitable for secret and covert “installation” in the online community; for organized attack to infiltrate the government systems; for military and political interests; and for sabotaging various national infrastructure, communication and other systems. Structurally, the use of trolling as a mean of collecting data stems from the military development, today applied within the doctrine of “Full Dimension Operations”. It is conducted in an organized manner, with legend and rules of secrecy, so the trolls are agents of authorized agencies. Intelligence systems, like the “Five Eyes” (FVEY - the USA, GB, Australia, Canada and New Zealand) have software tools, available IP addresses and networks of computers which run programs difficult to identify (botnet), which allows them to troll undetectably. The methods and tasks revealed through structural and functional analysis enable the induction of threats and challenges for national security of other states. The principal challenges are the consequence of automatized methods and are democratic in nature. The primary risk for national security is the fact that it involves secret and organized efforts by other states to influence public opinion and dehumanization. Another is due to the fact that agencies of some countries have a capacity to secretly monitor communications in the cyberspace of other countries. Intelligence trolling can have an online operation against a certain state as an immediate goal, like misinformation and disinformation, creating HUMINT networks, or cyber attacks on critical infrastructure. With an aim to master the Internet, the FVEY agencies are trying to invade every possible system on the global net, searching to gain access to further systems. The strategic protection of national security in cyberspace requires a multi-dimensional approach, within the framework of the national security strategy. It must include science and research of the cyber space and social networks, as the preconditions; education for the use of the Internet at all levels, quality education and public information systems, in sense of prevention; and the criminalization of fraudulent messages and training of the judiciary for prosecution, in terms of repression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.403
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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