Cyberthreat Detection from Twitter using Deep Neural Networks

Dionísio, Nuno; Alves, Fernando de Brito; Ferreira, Pedro; Bessani, Alysson

doi:10.48550/arxiv.1904.01127

Cited by 2 publications

(10 citation statements)

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“…In the last few years, research on using Cyber threat-relevant information available on Twitter for security purposes has gained significant attention. To automatically collect Cyber threat intelligence from Twitter, several methods have been used [7], [8], [10]- [14].…”

Section: Related Workmentioning

confidence: 99%

“…al. [10] use word embeddings such as GloVE [15] and Word2Vec [16] for feature extraction and use the binary classification model Convolutional Neural Network (CNN) [17] for classifying Cyber threat tweets. The collection method for Cyber threat tweets based on binary classification requires the classifiers to be trained with both positive and negative samples, or Cyber threat-relevant and Cyber threat-irrelevant tweets.…”

Section: Related Workmentioning

confidence: 99%

“…NOVELTY CLASSIFICATION As previously reported, our work focuses only on the collection method of Cyber threat tweets instead of a complete system with functional requirements such as scalability, realtime processing and security alert generation as in some previous work [10], [13], [14]. The key idea of our method is that we formulate the task of detecting Cyber threat tweets as a novelty classification task [18].…”

Section: Gathering Cyber Threat Tweets Usingmentioning

confidence: 99%

“…We also compared our classifier with other baselines including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) [10], [14]. These baselines are binary classification models which require to be trained with both positive and negative samples.…”

Section: A Experiments Setting A) Training and Testing Data Setsmentioning

confidence: 99%

“…However, using our knowledge about Cyber security, we can associate these two tweets with the CVE identifiers 'CVE-2018-20714' 1 and 'CVE-2017-11882' 2 respectively. Collecting these tweets with the associated CVE identifiers is useful for Cyber threat-related applications such as exploit prediction [7] and Indicators of Compromise (IoCs) generation [10].…”

Section: Introductionmentioning

confidence: 99%

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Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

Wang

Nasim

et al. 2019

2019 International Conference on Cyberworlds (CW)

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Preventing organizations from Cyber exploits needs timely intelligence about Cyber vulnerabilities and attacks, referred to as threats. Cyber threat intelligence can be extracted from various sources including social media platforms where users publish the threat information in real-time. Gathering Cyber threat intelligence from social media sites is a timeconsuming task for security analysts that can delay timely response to emerging Cyber threats. We propose a framework for automatically gathering Cyber threat intelligence from Twitter by using a novelty detection model. Our model learns the features of Cyber threat intelligence from the threat descriptions published in public repositories such as Common Vulnerabilities and Exposures (CVE) and classifies a new unseen tweet as either normal or anomalous to Cyber threat intelligence. We evaluate our framework using a purpose-built data set of tweets from 50 influential Cyber security-related accounts over twelve months (in 2018). Our classifier achieves the F1-score of 0.643 for classifying Cyber threat tweets and outperforms several baselines including binary classification models. Analysis of the classification results suggests that Cyber threat-relevant tweets on Twitter do not often include the CVE identifier of the related threats. Hence, it would be valuable to collect these tweets and associate them with the related CVE identifier for Cyber security applications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Gathering Cyber Threat Tweets Usingmentioning

confidence: 99%

Section: A Experiments Setting A) Training and Testing Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

Wang

Nasim

et al. 2019

2019 International Conference on Cyberworlds (CW)

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Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter

Nandita¹,

Li²,

Nurse³

2022

Preprint

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Current research on users' perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such as mobile and smart IoT devices in a home context. In addition, most were based on smaller-scale empirical studies such as online surveys and interviews. We endeavour to fill these research gaps by conducting a larger-scale study based on a real-world dataset of 413,985 tweets posted by non-expert users on Twitter in six months of three consecutive years (January and February in 2019, 2020 and 2021). Two machine learning-based classifiers were developed to identify the 413,985 tweets. We analysed this dataset to understand non-expert users' cyber security and privacy perspectives, including the yearly trend and the impact of the COVID-19 pandemic. We applied topic modelling, sentiment analysis and qualitative analysis of selected tweets in the dataset, leading to various interesting findings. For instance, we observed a 54% increase in nonexpert users' tweets on cyber security and/or privacy related topics in 2021, compared to before the start of global COVID-19 lockdowns (January 2019 to February 2020). We also observed an increased level of help-seeking tweets during the COVID-19 pandemic. Our analysis revealed a diverse range of topics discussed by non-expert users across the three years, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, and security and privacy issues involving different stakeholders. Overall negative sentiment was observed across almost all topics non-expert users discussed on Twitter in all the three years. Our results confirm the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on different facets of such perspectives.

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Cyberthreat Detection from Twitter using Deep Neural Networks

Cited by 2 publications

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Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter

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