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Twitter Tweetleri Üzerinden Açık Kaynak İstihbaratı Tabanlı YarıOtomatik Siber Güvenlik Modeli
2019
Journal:  
Düzce Üniversitesi Bilim ve Teknoloji Dergisi
Author:  
Abstract:

Açık kaynak istihbaratı (Open Source Intelligence, OSINT), kamuya açık bilgilerin sistematik olarak toplanması, işlenmesi ve analiz edilmesi sonucu elde edilen bilgiden istihbarat üretme disiplinidir. Açık kaynak istihbaratı çıkarmak stratejik bir zekâ olarak görülmektedir. Siber saldırılar için açık kaynak istihbaratı çıkarmak, saldırıların önlenmesi için yeni bir zekâ seviyesi oluşturacaktır. Sosyal medya, istihbarat için hızlı ve etkili bir kaynak oluşturmaktadır. Yapılan araştırmalar sıfırıncı gün (zero-day) atakların, saldırı gerçekleşmeden ya da henüz çok yayılmadan sosyal medya üzerinden tespit edilebildiğini göstermektedir. Bu nedenle OSINT tabanlı saldırı önleme sistemlerinin, siber ataklara karşı etkili bir önleme sağlayacağı düşünülmektedir. Bu çalışmada OSINT tabanlı saldırı tespit çalışmaları ile ilgili literatür taraması yapılmış aynı zamanda sosyal medya üzerinden saldırı istihbaratı sağlandığı anda devreye giren yarı-otomatik OSINT tabanlı saldırı önleme modeli önerilmiştir. Bu model, tweetler içerisinden istihbarat dataların çıkartılabildiği durumunda (BlackIP, virüs imzası vb.) sisteme otomatik eklenmesini, çıkartılamadığı durumlarda saldırıyı analiz edecek uzmana en kısa sürede maksimum bilgi sağlayacak şekilde oluşturulmuştur. 

Keywords:

Open Source Information on Twitter Tweets Half-Automatic Cyber Security Model
2019
Author:  
Abstract:

Open Source Intelligence (OSINT) is the discipline of generating information from information obtained from the systematic collection, processing and analysis of public information. Open source intelligence is considered a strategic intelligence. Releasing open source intelligence for cyber attacks will create a new level of intelligence to prevent attacks. Social media creates a fast and effective source of information. Research shows that zero-day attacks can be detected on social media before the attack occurred or not spread much. Therefore, OSINT-based attack prevention systems are believed to provide an effective prevention against cyber attacks. In this study, a literary scan of OSINT-based attack detection studies was carried out and a semi-automatic OSINT-based attack prevention model was proposed, which was activated at the time the attack information was provided via social media. This model, if it is possible to extract intelligence data from the tweets (BlackIP, viral signature, etc.) It is created to automatically add to the system in such a way that the expert will analyze the attack in cases where it cannot be removed will provide the maximum information as soon as possible.

Keywords:

Cyber Security Model Based On Open Source Intelligence Via Twitter Tweets
2019
Author:  
Abstract:

Open Source Intelligence (OSINT) is a system of intelligence generation from information gathered by systematic collection, processing and analysis of publicly available information. Open source intelligence is seen as a strategic intelligence. Open source intelligence for cyber attacks will create a new level of intelligence to prevent attacks. Social media is a fast and effective resource for intelligence. Research shows that zero-day attacks can be detected through social media without attack or spread. Therefore, it is thought that OSINT-based intrusion prevention systems will provide effective prevention against cyber attacks. In this study, a literature review about OSINTbased intrusion detection studies and a semi-automatic OSINT-based intrusion prevention model that was introduced when attack intelligence was provided through social media was proposed. This model is created in such a way that the intelligence data can be automatically added to the system (BlackIP, virus signature, etc.) from within the tweets, and to provide maximum information as soon as possible to the expert to analyze the attack.

Keywords:

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Düzce Üniversitesi Bilim ve Teknoloji Dergisi

Field :   Fen Bilimleri ve Matematik

Journal Type :   Ulusal

Metrics
Article : 1.636
Cite : 3.108
2023 Impact : 0.134
Düzce Üniversitesi Bilim ve Teknoloji Dergisi