Abstract:Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker’s personality analysis and reduces the success rate of social engineering attacks. We propose a text transformation method named PerTransGAN using generative adversarial networks (GANs) to protect the personality privacy hidden in text data. Making use of reinforcement learning, we use the output of the discriminator as a reward signal to guide th… Show more
“…Lastly, openness involves using creativity and imagination to gain access. The personality model of social engineering is based on direct communication (Sui et al, 2022), as it involves using persuasive language and tactics to directly interact with the victim and influence their decision-making (Eftimie et al, 2022).…”
Threats based on social engineering in social networks are becoming a more common problem. Social engineering is a type of attack that relies on trickery and exploiting human psychology to gain access to confidential information or resources. It involves deceptive techniques like phishing, pretexting, and baiting, tricking individuals into revealing sensitive information or performing specific actions. These tactics can lead to unauthorized access to user accounts, identity theft, or the distribution of harmful content. This study offers a detailed review of threats related to social engineering on social networks. It explores various social engineering attacks, the methods used to execute these threats, and measures that can be adopted to minimize the risk of becoming a victim. The research aimed to develop a new, broad classification of social engineering attacks and strategies for responding to them. It also examines the challenges that social engineering poses to algorithms on social media platforms and highlights the need for more research. The study concludes by pointing out the shortcomings of current approaches and suggesting future research directions, stressing the importance of standardized protective measures and increasing awareness among potential victims. This thorough examination improves our understanding of social engineering attacks and encourages the development of innovative solutions and ethical practices, contributing to a more secure digital environment.
“…Lastly, openness involves using creativity and imagination to gain access. The personality model of social engineering is based on direct communication (Sui et al, 2022), as it involves using persuasive language and tactics to directly interact with the victim and influence their decision-making (Eftimie et al, 2022).…”
Threats based on social engineering in social networks are becoming a more common problem. Social engineering is a type of attack that relies on trickery and exploiting human psychology to gain access to confidential information or resources. It involves deceptive techniques like phishing, pretexting, and baiting, tricking individuals into revealing sensitive information or performing specific actions. These tactics can lead to unauthorized access to user accounts, identity theft, or the distribution of harmful content. This study offers a detailed review of threats related to social engineering on social networks. It explores various social engineering attacks, the methods used to execute these threats, and measures that can be adopted to minimize the risk of becoming a victim. The research aimed to develop a new, broad classification of social engineering attacks and strategies for responding to them. It also examines the challenges that social engineering poses to algorithms on social media platforms and highlights the need for more research. The study concludes by pointing out the shortcomings of current approaches and suggesting future research directions, stressing the importance of standardized protective measures and increasing awareness among potential victims. This thorough examination improves our understanding of social engineering attacks and encourages the development of innovative solutions and ethical practices, contributing to a more secure digital environment.
“…To verify the performance of the SCRS method, we compared it with the PerTransGAN method proposed in our team's previous work [26]. The SCRS method uses generative adversarial networks (GANs) to protect the privacy of the personalities hidden in text data through text transformation.…”
With the development of science and computer technology, social networks are changing our daily lives. However, this leads to new, often hidden dangers in areas such as cybersecurity. Of these, the most complex and harmful is the Advanced Persistent Threat attack (APT attack). The development of personality analysis and prediction technology provides the APT attack a good opportunity to infiltrate personality privacy. Malicious people can exploit existing personality classifiers to attack social texts and steal users’ personal information. Therefore, it is of high importance to hide personal privacy information in social texts. Based on the personality privacy protection technology of adversarial examples, we proposed a Supervised Character Resemble Substitution personality adversarial method (SCRS) in this paper, which hides personality information in social texts through adversarial examples to realize personality privacy protection. The adversarial examples should be capable of successfully disturbing the personality classifier while maintaining the original semantics without reducing human readability. Therefore, this paper proposes a measure index of “label contribution” to select the words that are important to the label. At the same time, in order to maintain higher readability, this paper uses character-level resemble substitution to generate adversarial examples. Experimental validation shows that our method is able to generate adversarial examples with good attack effect and high readability.
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