Behavioral analysis of internet messaging and malicious activity detection

Srividya, Kotagiri; Sowjanya, A. Mary

doi:10.1109/hmi.2016.7449182

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Shahab saquib and Rashid Ali [9] have proposed a technique to investigate malicious behaviour in online social network (OSN). They have analysed the suspicious and unusual behaviour in OSN.…”

Section: Review Of Literaturementioning

confidence: 99%

A Survey on Techniques to Detect Malicious Activites on Web

Sait¹

2019

ijacsa

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The world wide web is more vulnerable for malicious activities. Spam-advertisements, Sybil attacks, Rumour propagation, financial frauds, malware dissemination, and Sql injection are some of the malicious activities on web. Terrorist are using web as a weapon to propaganda false information. Many innocent youths were trapped by web terrorist. It is very difficult to trace the impression of malicious activities on web. Many researches are under development to find a mechanism to protect web users and avoid malicious activities. The aim of the survey is to provide a study on recent techniques to find malicious activities on web.

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Section: Review Of Literaturementioning

confidence: 99%

A Survey on Techniques to Detect Malicious Activites on Web

Sait¹

2019

ijacsa

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Semantic analysis of social media and associated psychotic behavior

Amna

Qamar

Ali

2017

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

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Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

et al. 2020

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The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.

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Behavioral analysis of internet messaging and malicious activity detection

Cited by 3 publications

References 7 publications

A Survey on Techniques to Detect Malicious Activites on Web

A Survey on Techniques to Detect Malicious Activites on Web

Semantic analysis of social media and associated psychotic behavior

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

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