Hunting Organization-Targeted Socialbots

Paradise, Abigail; Shabtai, Asaf; Puzis, Rami

doi:10.1145/2808797.2809396

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…Honeypots technology is not new and has been popularly used in communication networks as a defensive deception to proactively deal with attackers by luring them to honeypots for preventing them from accessing a target [27]. The existing approaches using social honeypots have mainly focused on detecting social spammers, socialbots [234], or malware [107,108,145,146,147,177,208] as a passive monitoring tool. These works use some profiles of attackers to detect them based on the features collected from the social honeypots placed as fake SNS accounts (e.g., Facebook or Twitter).…”

Section: B Social Honeypotsmentioning

confidence: 99%

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Online Social Deception and Its Countermeasures: A Survey

et al. 2021

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We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building trustworthy SNSs. In this paper, we conduct an extensive survey, covering (i) the multidisciplinary concept of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature. We conclude our survey with in-depth discussions on the limitations of the state-of-the-art and suggest future research directions in OSD research.

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Section: B Social Honeypotsmentioning

confidence: 99%

“…Paradise et al [145,146] simulated defense account monitoring attack strategies in OSNs. The attackers sent friend requests to some community members chosen by different attacker strategies.…”

Section: B Social Honeypotsmentioning

confidence: 99%

Online Social Deception and Its Countermeasures: A Survey

et al. 2021

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“…Online social networks (OSNs) are abused by cyber criminals who exploit the platform to achieve various malicious goalsspreading dubious rumors, misinformation, or propaganda [1], spam and malware distribution [2], harvesting personal information about individuals [3], infiltration of organizations [4][5][6], and performing industrial espionage [7].…”

Section: Introductionmentioning

confidence: 99%

ProfileGen: Generation of Automatic and Realistic Artificial Profiles

Paradise

Cohen

Shabtai

et al. 2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Online social networks (OSNs) are abused by cyber criminals for various malicious activities. One of the most effective approaches for detecting malicious activity in OSNs involves the use of social network honeypotsartificial profiles that are deliberately planted within OSNs in order to attract abusers. Honeypot profiles have been used in detecting spammers, potential cyber attackers, and advanced attackers. Therefore, there is a growing need for the ability to reliably generate realistic artificial honeypot profiles in OSNs. In this research we present 'ProfileGen' -a method for the automated generation of profiles for professional social networks, giving particular attention to producing realistic education and employment records. 'ProfileGen' creates honeypot profiles that are similar to actual data by extrapolating the characteristics and properties of real data items. Evaluation by 70 domain experts confirms the method's ability to generate realistic artificial profiles that are indistinguishable from real profiles, demonstrating that our method can be applied to generate realistic artificial profiles for a wide range of applications.

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“…More recently, the new crawling technique of using socialbots [15,16,17,18,19,1] to friend the users and be able to see their private information. Boshmaf et al [15] proposed to build a large scale Socialbots system to infiltrate Facebook.…”

Section: Introductionmentioning

confidence: 99%

Towards Optimal Strategy for Adaptive Probing in Incomplete Networks

Nguyen,

Dinh

2017

Preprint

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We investigate a graph probing problem in which an agent has only an incomplete view G G of the network and wishes to explore the network with least effort. In each step, the agent selects a node u in G to probe. After probing u, the agent gains the information about u and its neighbors. All the neighbors of u become observed and are probable in the subsequent steps (if they have not been probed). What is the best probing strategy to maximize the number of nodes explored in k probes? This problem serves as a fundamental component for other decision-making problems in incomplete networks such as information harvesting in social networks, network crawling, network security, and viral marketing with incomplete information.While there are a few methods proposed for the problem, none can perform consistently well across different network types. In this paper, we establish a strong (in)approximability for the problem, proving that no algorithm can guarantees finite approximation ratio unless P=NP. On the bright side, we design learning frameworks to capture the best probing strategies for individual network. Our extensive experiments suggest that our framework can learn efficient probing strategies that consistently outperform previous heuristics and metric-based approaches.

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Hunting Organization-Targeted Socialbots

Cited by 13 publications

References 12 publications

Online Social Deception and Its Countermeasures: A Survey

Online Social Deception and Its Countermeasures: A Survey

ProfileGen: Generation of Automatic and Realistic Artificial Profiles

Towards Optimal Strategy for Adaptive Probing in Incomplete Networks

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