A New Cyber Security Alert System for Twitter

Erkal, Yigit; Sezgin, Mustafa; Gunduz, Sedef

doi:10.1109/icmla.2015.133

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…Many false information-detection techniques [109], [113], [116] have expanded the tf-idf feature together with other linguistic cues such as phrases, grammar, negatives, and punctuation. SVMs can detect satirical sentiment in sentences that are potentially misleading news [113], whereas with Naïve Bayes, it is possible to classify topics on Twitter to detect spam or phishing [117]. DNNs have shown their ability to detect hate speech in tweets with 93 percent accuracy [116].…”

Section: Application Layermentioning

confidence: 99%

“…Despite recent advances in text classification tasks, detecting cyberattacks at the semantic level is still in its infancy. Studies that employed tf-idf [109], [113]- [115], [117] required human intervention to supply relevant words such as ''dead'' or ''bomb'' to detect threats [109], and ''age,'' ''yr,'' or ''year'' to detect predators [115]. This shows that, despite the use of AI, cyberthreat detection at the current application layer still requires human intelligence intervention.…”

Section: Application Layermentioning

confidence: 99%

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Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

et al. 2020

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Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

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Section: Application Layermentioning

confidence: 99%

Section: Application Layermentioning

confidence: 99%

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

et al. 2020

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“…To this end, we plotted such relationship (see Fig. 7) where x-axis represents affiliation values and y-axis shows the influence metric normalized in the range [0,1]. Results confirm that users with a high degree of affiliation have very low influence, and higher values of influence metric are obtained when the affiliation assumes values lower than 0,1.…”

Section: Methodsmentioning

confidence: 76%

“…As a consequence, it is more and more necessary to introduce new techniques to prevent, detect and limit the cyber-attacks. In addition to traditional malware detection systems, new tools to boost and speed up the whole process have been proposed [1].…”

Section: Introductionmentioning

confidence: 99%

Twitter analysis for real-time malware discovery

Concone

Paola

et al. 2017

2017 AEIT International Annual Conference

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In recent years, the increasing number of cyberattacks has gained the development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of Social Networks to discover the rising of new malicious software. Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. The subscribers can insert messages, called tweet, that are usually related to international news. In this work, we present a system for real-time malware alerting using a set of tweets captured through the Twitter API's, and analyzed by means of a Bayes Naïve classifier. Then, groups of tweets discussing the same topic, e.g, a new malware infection, are summarized in order to produce an alert. Tests have been performed to evaluate the performance of the system and results show the effectiveness of our implementation.

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“…We have been using various contextual scenarios in which live and socially generated data play an important role in creating alerts in humanitarian catastrophes, addressing incidents which affect populations, and recognizing natural and health hazards [10,14,15,17,28]. There is significant evidence that Twitter data is one of the best available OSINT sources [6,9,21,22]. The Twitter platform plays as important a role in detecting cyber security threats as it may have in addressing humanitarian crises and resilience [16,28].…”

Section: Introductionmentioning

confidence: 99%

Detecting Cyber Security Vulnerabilities through Reactive Programming

Moholth¹,

Juric

McClenaghan

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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We propose a software architectural model, which uses reactive programming for collecting and filtering live tweets and interpreting their potential correlation to software vulnerabilities and exploits. We aim to investigate if we could discover the existence of exploits for disclosed vulnerabilities in Twitter data streams. Reactive programming is used for performing filtering and querying of tweets to find potential exploits. The result of processing Twitter data streams with reactive programming could be broadcast, by pointing towards potential exploits, which might create a cyber-attack.

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A New Cyber Security Alert System for Twitter

Cited by 12 publications

References 7 publications

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Twitter analysis for real-time malware discovery

Detecting Cyber Security Vulnerabilities through Reactive Programming

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