DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot Detection

Fazil, Mohd; Sah, Amit Kumar; Abulaish, Muhammad

doi:10.1109/tifs.2021.3102498

Cited by 47 publications

(31 citation statements)

References 33 publications

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“…e cresci-2017 dataset and the cresci-2015 dataset have been widely used in social bot detection in recent years. Some researchers have used the datasets (completely or partially) to evaluate their approaches for social bot detection and have achieved good detection results [25,34,36,37,[54][55][56].…”

Section: Methodsmentioning

confidence: 99%

“…Considering the differences in main languages, interaction with users, information sharing, and features of social bots in different OSNs, it is difficult to directly apply the existing detection technologies based on other OSNs to Sina Weibo. Moreover, social bots are constantly evolving and developing [24], while features extracted through time-consuming feature engineering may be effective in detecting only a specific category of social bots [25]. Hence, it is difficult to perform quite well in social bot detection only by feature engineering.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with graph-based approaches, machine learning-based approaches are easier to implement and use. However, machine learning-based approaches also suffer from time-consuming feature engineering and need to be undertaken more in-depth [25]. In summary, research on social bot detection mainly faces the following three challenges:…”

Section: Introductionmentioning

confidence: 99%

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A New Joint Approach with Temporal and Profile Information for Social Bot Detection

Yang

Chen

Wang

et al. 2022

Security and Communication Networks

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With the increasing popularity of online social networks (OSNs), a huge number of social bots have emerged. Social bots are involved in various cybercrimes like cyberbullying and rumor dissemination, which have seriously affected the normal order of OSNs. Nowadays, existing studies in this field almost focus on English OSNs like Twitter and Facebook. However, it is difficult to directly apply these detection technologies to Sina Weibo, which is one of the largest Chinese microblogging services in the world. In addition, social bots are evolving rapidly and time-consuming feature engineering may not perform well in detecting newly emerging social bots. In this paper, we propose a new joint approach with Temporal and Profile information for social bot detection (TPBot). The approach includes data collection module, feature extraction module, and detection module. To begin with, data collection module uses a web crawler to obtain user data from Sina Weibo. Next, the feature extraction module regards the user posts as temporal data to extract temporal-semantic and temporal-metadata features. Furthermore, this module extracts features based on users’ profile. Finally, a detection model based on BiGRU and attention mechanism is designed in the detection module. The results show that TPBot performs better than baselines with the F1-score of 0.9837 on the Sina Weibo dataset. Moreover, we have also conducted an experiment on the two datasets collected from Twitter to evaluate the generalization ability of TPBot. It is found that TPBot outperforms baselines on the new datasets and has good generalization ability.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Joint Approach with Temporal and Profile Information for Social Bot Detection

Yang

Chen

Wang

et al. 2022

Security and Communication Networks

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“…Firstly, suppose that the elements of the two sequences are the same, and only the length of the two sequences is compared. e similarity of two sequences in terms of length Sim len is defined as (6). Given the similarity threshold Sim thre , if Sim len is less than the similarity threshold Sim thre , it can be directly recognized that the two sequences are different, and the Levenshtein algorithm is no longer required.…”

Section: Malicious Flow Recognitionmentioning

confidence: 99%

“…e botnet detection technology has always been a research hotspot in the field of network security. Researchers have proposed a large number of methods to detect botnet [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Protocol-Independent Botnet Detection Method Using Flow Similarity

Liang

Zhao

Chen

2022

Security and Communication Networks

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The detection of botnets has always been a hot spot in the field of network security. However, there are still many challenges in botnet detection. Most of the current botnet detection approaches, such as machine learning and blacklists, cannot discover evolving botnet variants. These methods are usually only valid for specific botnet protocols which are not general. Even they may be difficult to deal with encrypted botnet traffic. In this paper, we design a protocol-independent botnet detection method for these challenges. Our detection method takes advantage of the group characteristic of the botnet, which is the inherent characteristics of the botnet. We use the sequence of packet length as the characteristic of a flow. Then, we calculate the similarity between these sequences to detect botnets. Our method has an excellent generality, which is not affected by encrypted traffic and the protocols of the botnet. Experiments on a challenging dataset ISCX show that the proposed method can effectively detect botnets with a high average detection rate and low false alarm, which significantly outperforms the state-of-the-art methods. Therefore, the proposed detection method is robust and has a wide range of adaptability in detecting botnets.

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DeepMUI: A novel method to identify malicious users on online social network platforms

Shah,

Varshney,

Mehrotra

2023

Concurrency and Computation

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SummaryThe use of online social network (OSN) platforms has become an essential component of contemporary society, facilitating global connectivity, and information sharing among individuals. The proliferation of malicious users has emerged as a noteworthy obstacle, exerting a detrimental effect on the authenticity of the data disseminated through these channels. A malicious profile is created with the intention of disseminating false information, manipulating perspectives, and executing harmful actions, including phishing schemes, identity theft, and the propagation of malware. Consequently, the identification of malicious users has emerged as an essential undertaking for both OSN platforms and researchers. The objective of this study is to investigate the issue of identifying malicious users on OSN platforms. The DeepMUI model has been introduced as a new approach to identifying malicious users on OSN platforms, utilizing user profile metadata‐derived characteristics. The DeepMUI architecture is composed of long short‐term memory and convolutional neural network models. Additionally, it integrates alterations to the pooling layer to improve its overall efficacy. The experiments have demonstrated that DeepMUI exhibits promising results in the task of identifying malicious users, with greater accuracy and minimal loss compared to existing methods.

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DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot Detection

Cited by 47 publications

References 33 publications

A New Joint Approach with Temporal and Profile Information for Social Bot Detection

A New Joint Approach with Temporal and Profile Information for Social Bot Detection

A Protocol-Independent Botnet Detection Method Using Flow Similarity

DeepMUI: A novel method to identify malicious users on online social network platforms

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