Guided socialbots: Infiltrating the social networks of specific organizations’ employees

Elyashar, Aviad; Fire, Michael; Kagan, Dima; Elovici, Yuval

doi:10.3233/aic-140650

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…Social botnets are used in political disagreements [12]. Furthermore, bots of an organization's penetration act apparently like friends [13]. Finally, cyberbullying bots entail the deliberate and aggressive use of information and communication technology by an individual or group with the intent to harm others [14,15].…”

Section: Inteligencia Artificialmentioning

confidence: 99%

Feature extractions and selection of bot detection on Twitter A systematic literature review

Al-azawi

AL-mamory²

2022

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Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issues in feature extraction and selection that have a significant impact on the accuracy of bot detection algorithms, and highlighting the weaknesses in training time and dimensionality reduction. To the best of our knowledge, this study is the first systematic literature review based on a preset search-strategy that encompasses literature published between 2018 and 2021 which are concerned with Twitter features (attributes). The key findings of this research are threefold. First, the paper provides an improved taxonomy of feature extraction and selection approaches. Second, it includes a comprehensive overview of approaches for detecting bots in the Twitter platform, particularly machine learning techniques. The percentage was calculated using the proposed taxonomy, with metadata, tweet text, and merging (meta and tweet text) accounting for 37%, 31%, and 32%, respectively. Third, some gaps are also highlighted for further research. The first is that public datasets are not precise or suitable in size. Second, the use of integrated systems and real-time detection is uncommon. Third, detecting each bots category identified separately is needed, rather than detecting all categories of bots using one generic model and the same features' values. Finally, extracting influential features that assist machine learning algorithms in detecting Twitter bots with high accuracy is critical, especially if the type of bot is pre-determined.

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Section: Inteligencia Artificialmentioning

confidence: 99%

Feature extractions and selection of bot detection on Twitter A systematic literature review

Al-azawi

AL-mamory²

2022

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“…It has been suggested that this movement was meant to intentionally alter the perception of social media to benefit groups that desired public mistrust in social media and to further their political goals [66]. Third, a study using social bots and Facebook to infiltrate specific organizations through friend requests revealed that most social bots succeeded in gathering private information from high-ranking organization officials [67]. Thus, organizational security may also be at risk due to the malicious use of social bots.…”

Section: Iv2 Financial and Economic Threatsmentioning

confidence: 99%

Attribution Across Cyber Attack Types: Network Intrusions and Information Operations

Goel

Nussbaum

2021

IEEE Open J. Commun. Soc.

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The objective of this paper is to articulate the problem of attribution in cyber warfare incidents, including, surveillance, data theft, espionage, and misinformation campaigns. As the stakes increase, concerted efforts are being made by intelligence and law enforcement agencies to identify the perpetrators with much painstaking effort. Attribution tools and techniques for malicious activities on the internet are still nascent, relying mainly on technical measurements, the provenance of malicious code, and nontechnical assessments of attack and attacker characteristics to link attack activities to individuals or groups. Attribution of attacks is typically done through a burdensome manual process that relies on both technical analysis and ground intelligence. As a result, this cumbersome and laborious process of attribution is primarily reserved for the most egregious cyber attack cases and those conducted against well resourced organizations. Over time, our attribution abilities have improved, however, this improvement is a two-edged sword: as attribution capabilities improve, internet privacy is increasingly diluted. This paper discusses attribution for two vastly different types of attacks that are central to cyber conflict today: network intrusions and social bot-led misinformation campaigns. The paper discusses the state of the art regarding attribution abilities across both types of attack, provides recommendations for improved attribution, and lays out future research directions.

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“…Their work proved the feasibility of privacy breach by exploiting social bots. Elyashar et al 30 studied the infiltration of social bots in organizations related to computer technology, whose employees should theoretically be more security-conscious. However, their experiments showed that the infiltration of computer-related workers also had a high success rate and found that the more mutual friends the user had, the more likely he or she was to accept a friend request from a social bot.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, it can provide a reference for the social bots detections as well. The social bots in Facebook or Twitter have been studied by some researchers [28][29][30] . However, due to differences of users, social network structure, active time, regulatory requirements and other aspects, the strategies to construct social bots of Sina Microblog need to be different.…”

mentioning

confidence: 99%

Exploring the construction and infiltration strategies of social bots in sina microblog

Wang

Chen

Jiang

et al. 2020

Sci Rep

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Nowadays, millions of people use Online Social Networks (OSNs) like Twitter, Facebook and Sina Microblog, to express opinions on current events. The widespread use of these OSNs has also led to the emergence of social bots. What is more, the existence of social bots is so powerful that some of them can turn into influential users. In this paper, we studied the automated construction technology and infiltration strategies of social bots in Sina Microblog, aiming at building friendly and influential social bots to resist malicious interpretations. Firstly, we studied the critical technology of Sina Microblog data collection, which indicates that the defense mechanism of that is vulnerable. Then, we constructed 96 social bots in Sina Microblog and researched the influence of different infiltration strategies, like different attribute settings and various types of interactions. Finally, our social bots gained 5546 followers in the 42-day infiltration period with a 100% survival rate. The results show that the infiltration strategies we proposed are effective and can help social bots escape detection of Sina Microblog defense mechanism as well. The study in this paper sounds an alarm for Sina Microblog defense mechanism and provides a valuable reference for social bots detection.

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Guided socialbots: Infiltrating the social networks of specific organizations’ employees

Cited by 13 publications

References 38 publications

Feature extractions and selection of bot detection on Twitter A systematic literature review

Feature extractions and selection of bot detection on Twitter A systematic literature review

Attribution Across Cyber Attack Types: Network Intrusions and Information Operations

Exploring the construction and infiltration strategies of social bots in sina microblog

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