Recently, machine learning (ML) classifiers to distinguish between real and bot accounts have proven advances. Such behavior needs investigation aiming to mitigate its effects. One of the methods to carry out these abuses is the use of bots on Twitter. However, its success has attracted interest in attacking and exploiting through a wide range of unethical activities, such as malicious actions to manipulate users. Online social networks like Twitter provide a novel channel to allow interaction between human beings. Without revealing any sensitive or real data, the paper analyses some findings of this experiment and addresses further plans for research in this area. In the final two weeks, our experiment showed that 437 unique users could have been phished, 33 of which visited our website through the network of an organization.
For a period of four weeks, each bot published tweets about its subject and followed people with similar interests. It then describes our experimental development, in which we created and deployed eight social bots on Twitter, each associated with one specific subject. This paper first provides a review of current work. We address the serious organizational threats and security risks caused by phishing through online social media, specifically through Twitter. Current literature mostly focuses on traditional phishing methods (through e-mail, phone calls, and USB sticks). This work investigates how social bots can phish employees of organizations, and thus endanger corporate network security.
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