Detection of fake accounts on social networks is a challenging process. The previous methods in identification of fake accounts have not considered the strength of the users’ communications, hence reducing their efficiency. In this work, we are going to present a detection method based on the users’ similarities considering the network communications of the users. In the first step, similarity ...

Detecting fake accounts in online social networks (OSNs) protects OSN operators and their users from various malicious activities. Most detection mechanisms attempt to predict and classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attack...

Users increasingly rely on the trustworthiness of the information exposed on Online Social Networks (OSNs). In addition, OSN providers base their business models on the marketability of this information. However, OSNs suffer from abuse in the form of the creation of fake accounts, which do not correspond to real humans. Fakes can introduce spam, manipulate online rating, or exploit knowledge ex...

We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election. Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news, based on a list of news outlets curated ...

Social media is becoming increasingly used as a source of information, including events during warfare. The fake accounts the social are often for variety cyber-attacks, information-psychological operations, and opinion manipulating analysis online research methods carried out, main metrics attributes in Facebook investigated. Each metric assigned to appropriate categories convenience their get...

Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different ki...

Detecting and suspending fake accounts (Sybils) in online social networking (OSN) services protects both OSN operators and OSN users from illegal exploitation. Existing social-graph-based defense schemes effectively bound the accepted Sybils to the total number of social connections between Sybils and non-Sybil users. However, Sybils may still evade the defenses by soliciting many social connec...

Detecting fake accounts in online social networks (OSNs) protects both OSN operators and their users from various malicious activities. Most detection mechanisms attempt to classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing either user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attack...