Measuring Early Detection of Anomalies

López-Vizcaíno, Manuel; Nóvoa, Francisco J.; Fernández, Daniel Méndez; Cacheda, Fidel

doi:10.1109/access.2022.3224467

Cited by 4 publications

(11 citation statements)

References 27 publications

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“…

will be considered as the evaluation metric, as presented in [ 43 ] for cybersecurity evaluation purposes.…”

Section: Methodsmentioning

confidence: 99%

“…The output of the metric

for a set of entities E is obtained by the aggregation of individual entities

by means of an average score, as shown next:

where

is the decay parameter to define how harsh the penalisation applied is, and k corresponds with the amount of items being processed at that specific point. Moreover, as previously studied in [ 43 ] but not included here for sake of simplicity, an

parameter is included to allow for the balance of cases in different problems.…”

Section: Methodsmentioning

confidence: 99%

“…In this section, we present the baseline results using the

metric, as introduced in [ 43 ] for all three models used on the experiments: the fixed model, threshold model, and dual model.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

Self Cite

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The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeawareprecision (TaP) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.

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“…

will be considered as the evaluation metric, as presented in [ 43 ] for cybersecurity evaluation purposes.…”

Section: Methodsmentioning

confidence: 99%

“…The output of the metric

for a set of entities E is obtained by the aggregation of individual entities

by means of an average score, as shown next:

where

parameter is included to allow for the balance of cases in different problems.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although, a thorough study was proposed there was limited research on its evaluation metrics and methodologies. Best efforts as evaluation metric definition for the early detection problem was presented by Early Risk Detection Error (ERDE) [1], F-latency [2] an also by [3] where an alternative, Time aware Precision (TaP) non-dataset-dependent metric was proposed.…”

Section: Related Workmentioning

confidence: 99%

Time Aware F-Score for Cybersecurity Early Detection Evaluation

López-Vizcaíno,

Nóvoa,

Fernández

et al. 2024

Applied Sciences

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With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon as possible, decreasing the risk of consequences derived from those actions. In this paper, a new metric for early detection system evaluation that takes into account the delay in detection is defined. Time aware F-score (TaF) takes into account the number of items or individual elements processed to determine if an element is an anomaly or if it is not relevant to be detected. These results are validated by means of a dual approach to cybersecurity, Operative System (OS) scan attack as part of systems and network security and the detection of depression in social media networks as part of the protection of users. Also, different approaches, oriented towards studying the impact of single item selection, are applied to final decisions. This study allows to establish that nitems selection method is usually the best option for early detection systems. TaF metric provides, as well, an adequate alternative for time sensitive detection evaluation.

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“…When the malevolent agents were present, even though the change in belief value happened slowly, it had a significant impact since a large number of ordinary agents began to believe false information. Using the Soil Framework, a multi-agent system that simulates the social media network Manuel F, et.al,… [4] The suggested early detection is becoming more and more important in a variety of fields, including network security, health issues on social media, and disaster forecasting related to weather. Making an excellent judgement alone is insufficient; it must also be made promptly.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Enabled Social Network for Detecting Fake Accounts

B. Deepika,

S. Sneka,

S. Susila

et al. 2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Front end technology with permanent accounts is employed in online social networks to help people get to know one another. In an effort to maintain connection with everyone, Facebook and Twitter are changing along with the users. OSNs are used by them for planning events, news exchange, interpersonal communication, and even running their own online enterprises. Because of OSNs' quick growth and the abundance of personal data that its users have shared, attackers and imposters have been lured to them with the intention of stealing personal information, spreading disruptive activities, and publishing false information. A further rationale for creating fictitious profiles for hateful accounts. In an attempt to harm the reputation of the person they detest, these users make profiles using their usernames and publish photographs and articles that aren't related to them. to use blockchain technology, Aadhar number verification, and SVM profile classification to create a reliable solution for detecting phoney social network profiles. By analysing several aspects of social media profiles, the SVM classification uses machine learning to discern between real and fraudulent accounts. By verifying Aadhar numbers through legitimate channels, Aadhar number verification is integrated into the registration process, guaranteeing the authenticity of user identities. A decentralised and secure identity management system is made possible by the use of blockchain technology, with smart contracts providing immutable storage for verified user data.

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Measuring Early Detection of Anomalies

Cited by 4 publications

References 27 publications

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Time Aware F-Score for Cybersecurity Early Detection Evaluation

Blockchain Enabled Social Network for Detecting Fake Accounts

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