Employee profiling via aspect-based sentiment and network for insider threats detection

Soh, Chee Kiong; Yu, Sicheng; Narayanan, Annamalai; Duraisamy, Santhiya; Chen, Lihui

doi:10.1016/j.eswa.2019.05.043

Cited by 41 publications

(15 citation statements)

References 25 publications

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“…Chi et al [123], focused on the detection of insider threats by combining linguistic analysis and K-means algorithm to analyze communications, such as emails, to ascertain whether an employee meets certain personality criteria and to deduce the risk level for each employee. Soh et al [124] focused on an aspect-based sentiment analysis that can provide more detailed information. Moreover, they presented a novel employee profiling framework equipped with deep learning models for insider threat detection, which is based on aspect-based sentiment and social network information.…”

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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“…Sentiment analysis can be understood as reflecting specific corpus to different sentiment categories. After giving corpus and tags, sentiment analysis is performed as the following operations [35]:…”

Section: Framework Of Concept-bertmentioning

confidence: 99%

A Novel Method for Reducing Overhead of Training Sentiment Analysis Network

Lei

Zhang

Wang

et al. 2021

Preprint

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Sentiment analysis based on statistics has rapidly developed in deep-learning. Bilateral attention neural network (BANN), especially Bidirectional Encoder Representations from Transformers (BERT), has reached high accuracy. However, with the increase of network depth and large-scale corpus, the computational overhead of BANN increases geometrically. How to reduce training corpus scale has correspondingly become an important research focus. This paper proposes a reduced corpus scale method called Concept-BERT, which consists of the following steps: firstly, using Formal Concept Analysis (FCA), Concept-BERT mines the association rules among corpus and reduces corpus attributes, and hence reducing corpus scale; secondly, reduced-corpus is inputed to BERT and the result is obtained; finally, the attention of Concept-BERT is analyzed. Concept-BERT is experimented for sentiment analysis on CoLA, SST-2, Dianping and Blogsenti, and its accuracy reaches 81.1, 92.9, 77.9 and 86.7 respectively. Our experimental results show that the proposed method has the same accuracy as BERT, using low-scale corpus and low overhead, and low-scale corpus doesn't affect model attention.

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“…Tabash and Happa [183] combined computer emergency response team (CERT) logs with the knowledge of security experts in their system by having the analysts classify detected anomalies. Soh et al [184] used email, personality traits, and implicit motives to profile employees using a gated recurrent unit and Skip-gram. Nithiyanandam et al [185] proposed a layered defense based on data monitoring, activity monitoring, user authentication, resource monitoring, and an overarching defense manager.…”

Section: Other Behaviorsmentioning

confidence: 99%

“…First, insiders with trusted access can perform unauthorized activities. Thus, external network security tools, such as firewalls, IDS, and antivirus software, cannot detect a malicious insider [93,147,184,189]. Second, insider attacks manifest in many forms.…”

Section: Complexity Of Insider Threat Detectionmentioning

confidence: 99%

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A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

et al. 2020

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Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

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Employee profiling via aspect-based sentiment and network for insider threats detection

Cited by 41 publications

References 25 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

A Novel Method for Reducing Overhead of Training Sentiment Analysis Network

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

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