An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks
نویسندگان
چکیده
منابع مشابه
An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network
In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecomm...
متن کاملDeep Neural Networks for Bot Detection
The problem of detecting bots, automated social media accounts governed by software but disguising as human users, has strong implications. For example, bots have been used to sway political elections by distorting online discourse, to manipulate the stock market, or to push anti-vaccine conspiracy theories that caused health epidemics. Most techniques proposed to date detect bots at the accoun...
متن کاملAn Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks
The structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of community detection is to separate groups or communities that are linked more closely. In fact, community detection is the clustering...
متن کاملNeural Network Model for Email-Spam Detection
Email spam is a word that we come across in our daily life. The word spam means junk mails. The unsolicited emails that are received by any person in his/her mailbox are called spam. These junk mails are usually sent in bulk for advertising and marketing some products. This work presents a neural network approach to intrusion detection. A Multi-Layer Perceptron using Back Propagation Algorithm ...
متن کاملAn Effective Method for Utility Preserving Social Network Graph Anonymization Based on Mathematical Modeling
In recent years, privacy concerns about social network graph data publishing has increased due to the widespread use of such data for research purposes. This paper addresses the problem of identity disclosure risk of a node assuming that the adversary identifies one of its immediate neighbors in the published data. The related anonymity level of a graph is formulated and a mathematical model is...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Sciences
سال: 2020
ISSN: 2076-3417
DOI: 10.3390/app10228160