Abstract:Recent studies have revealed that cyber criminals tend to exchange knowledge about cyber attacks in online social networks (OSNs). Cyber security experts are another set of information providers on OSNs who frequently share information about cyber security incidents and their personal opinions and analyses. Therefore, in order to improve our knowledge about evolving cyber attacks and the underlying human behavior for diferent purposes (e.g., crime investigation, understanding career development and business mo… Show more
“…Surprisingly, although a lot of work has been done on using OSN data to analyze activities and behaviors of hackers, cyber criminals and other types of cyber attackers [8]- [15], [29], [30], there has been very limited research on using OSN data to study defacement attacks and defacers. The only work we are aware of was done by Maimon et al in 2017 [31].…”
Section: Romagna Et Al's Work Depended On Tags Onmentioning
“…Surprisingly, although a lot of work has been done on using OSN data to analyze activities and behaviors of hackers, cyber criminals and other types of cyber attackers [8]- [15], [29], [30], there has been very limited research on using OSN data to study defacement attacks and defacers. The only work we are aware of was done by Maimon et al in 2017 [31].…”
Section: Romagna Et Al's Work Depended On Tags Onmentioning
“…The process of real-time cybersecurity account detection on Twitter is based on three different feature sets and three different methods of machine learning, which are decision trees, random forests, and SVM [10]. The DeepScan model splits the activity data of each user into many continuous-time intervals.…”
“…The spotlight is a 5 x 5 pixel space. In the wake of the sparkling super-left territory, the spotlight shifts with the final goal of slipping through all the information picture / content territories [34]. This spotlight is known as a platform or part of CNN [35].…”
Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.
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