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Articles de revues sur le sujet « Malicious bots »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Geer, D. "Malicious bots threaten network security." Computer 38, no. 1 (2005): 18–20. http://dx.doi.org/10.1109/mc.2005.26.

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Kolomeets, M., and A. Chechulin. "Properties of Malicious Social Bots." Proceedings of Telecommunication Universities 9, no. 1 (2023): 94–104. http://dx.doi.org/10.31854/1813-324x-2023-9-1-94-104.

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The paper considers the ability to describe malicious bots using their characteristics, which can be the basis for building models for recognising bot parameters and qualitatively analysing attack characteristics in social networks. The following metrics are proposed using the characteristics of VKontakte social network bots as an example: trust, survivability, price, seller type, speed, and expert quality. To extract these metrics, an approach is proposed that is based on the methods of test purchases and the Turing test. The main advantage of this approach is that it proposes to extract feat

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Sneha, B. "Detecting Malicious Twitter Bots using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1457–63. http://dx.doi.org/10.22214/ijraset.2024.59085.

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Abstract: Twitter play a significant role in our daily lives, offering a wide range of opportunities to their users. However, Twitter and online social networks (OSNs) in general are increasingly being utilized by automated accounts, commonly known as bots, as they continue to gain immense popularity across various user demographics. Malicious twitter Bots detection is required to detect real users from fraudulent users because it leads to spreading of spam messages and engage in fraudulent activities. To overcome this, we are going to differentiate bots from legitimate users using feature ext

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Sopinti Chaitanya Raj and B.Srinivas.S.P.Kumar. "DETECTING MALICIOUS TWITTER BOTS USING MACHINE LEARNING." international journal of engineering technology and management sciences 6, no. 6 (2022): 382–88. http://dx.doi.org/10.46647/ijetms.2022.v06i06.068.

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In today's world, Twitter is used often & has taken on significance in lives about many individuals, including businessmen, media, politicians, & others. One about most popular social networking sites, Twitter enables users towards share their opinions on a range about subjects, including politics, sports, financial market, entertainment, & more. It is one about fastest methods about information transfer. It significantly influences how individuals think. There are more people on Twitter who mask their identities for malicious reasons. Because it poses a risk towards other users, i

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M Nithin, Meer Eshak Ahammad, Nichenametla Shashank, Shaik Hyder Ali, and K.Mudduswamy. "Detection of Malicious Social Bots Using Learning Automata with URL Features in Twitter Network." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 261–66. https://doi.org/10.32628/cseit2511317.

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With the rapid growth of social media platforms like Twitter, malicious social bots have become a significant threat, capable of manipulating public opinion, spreading misinformation, and launching cyber-attacks. These bots often mimic human behavior, making their detection a challenging task. This project proposes a novel approach to detect malicious social bots on Twitter by leveraging Learning Automata in combination with URL-based features extracted from user-generated content. The methodology involves analyzing embedded URLs in tweets—such as domain reputation, frequency, and redirection

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N. Ezhil Arasi, Dr. G Manikandan, Ms. S. Hemalatha, and Ms. Vilma Veronica. "Malicious Social Bot Using Twitter Network Analysis in Django." International Journal of Scientific Research in Science and Technology 11, no. 2 (2024): 114–13. http://dx.doi.org/10.32628/ijsrst52411222.

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Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and

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Shi, Peining, Zhiyong Zhang, and Kim-Kwang Raymond Choo. "Detecting Malicious Social Bots Based on Clickstream Sequences." IEEE Access 7 (2019): 28855–62. http://dx.doi.org/10.1109/access.2019.2901864.

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Gnanasekar, A. "Detecting Spam Bots on Social Network." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 850–60. http://dx.doi.org/10.47059/revistageintec.v11i2.1719.

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Bots have made an appearance on social media in a variety of ways. Twitter, for instance, has been particularly hard hit, with bots accounting for a shockingly large number of its users. These bots are used for nefarious purposes such as disseminating false information about politicians and inflating celebrity expectations. Furthermore, these bots have the potential to skew the results of conventional social media research. With the multiple increases in the size, speed, and style of user knowledge in online social networks, new methods of grouping and evaluating such massive knowledge are bei

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Molkova, L. U., and E. D. Pasechnic. "PROBLEMS OF NEGATIVE IMPACT AND ECONOMIC HARM CAUSED BY BOTNETS." ECONOMIC VECTOR 2, no. 37 (2024): 200–203. http://dx.doi.org/10.36807/2411-7269-2024-2-37-200-203.

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Currently, malicious bots pose a serious threat in the field of information security due to the fact that they are used for such crimes on the Internet as fraud, extortion, blocking the Internet resources of enterpris-es, which leads to a deterioration in the reputation of enterprises and the inability to fully implement business processes. Thus, at the present stage of development of infor-mation systems and information protection, detection and elimination of malicious bots is one of the most pressing tasks. This article discusses the architecture of malicious bots and possible ways to destr

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He, Yukun, Guangyan Zhang, Jie Wu, and Qiang Li. "Understanding a prospective approach to designing malicious social bots." Security and Communication Networks 9, no. 13 (2016): 2157–72. http://dx.doi.org/10.1002/sec.1475.

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Jyothis, Joseph, Binu Nandhitha, K. B. Vyshnavi, and Santhosh Nandana. "A Survey on Twitter Bot Detection: Comparative Study of Machine Learning and Deep Learning Techniques." Research and Reviews: Advancement in Robotics 8, no. 3 (2025): 1–11. https://doi.org/10.5281/zenodo.15515794.

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<em>X (formerly known as Twitter) has emerged as one of the most prominent social networking platforms, attracting diverse users, including individuals, influencers, businesses, and organizations. It allows users to share their content, opinions, news, and multimedia. Recently, there has been growing concern about the significant rise of malicious bots on social media platforms, especially on X. These bots can manipulate online discussions and spread misinformation, potentially exerting considerable influence on communities.</em> <em> </em> <em>This survey examines and conducts a comparat

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Liu, Xia. "A big data approach to examining social bots on Twitter." Journal of Services Marketing 33, no. 4 (2019): 369–79. http://dx.doi.org/10.1108/jsm-02-2018-0049.

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Purpose Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended. Design/methodology/approach A Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word clo

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Qiao, Boyu, Kun Li, Wei Zhou, Shilong Li, Qianqian Lu, and Songlin Hu. "BotSim: LLM-Powered Malicious Social Botnet Simulation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14377–85. https://doi.org/10.1609/aaai.v39i13.33575.

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Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots with unprecedented intelligence. These bots adeptly simulate human profiles, conversations, and interactions, disseminating large amounts of false information and posing significant challenges to platform regulation. To better understand and counter these threats, we innovatively design BotSim, a malicious social botnet simulation powered by LLM. BotSim mimics the information dissemination patterns of real-world socia

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Chechulin, A., and M. Kolomeets. "Approach to Detecting Malicious Bots in the Vkontakte Social Network and Assessing Their Parameters." Proceedings of Telecommunication Universities 10, no. 2 (2024): 92–101. http://dx.doi.org/10.31854/1813-324x-2024-10-2-92-101.

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The emergence of new varieties of bots in social networks and the improvement of their capabilities to imitate the natural behavior of real users represent a significant problem in the field of protection of social networks and online communities. This paper proposes a new approach to detecting and assessing the parameters of bots within the social network «VKontakte». The basis of the proposed approach is the creation of datasets using the method of «controlled purchase» of bots, which allows one to assess bots’ characteristics such as price, quality, and speed of action of bots, and using th

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Wang, Xiujuan, Qianqian Zheng, Kangfeng Zheng, Yi Sui, Siwei Cao, and Yutong Shi. "Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor." Applied Sciences 11, no. 12 (2021): 5482. http://dx.doi.org/10.3390/app11125482.

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Malicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to correct the defects of high-cost labeling and unbalanced positive and negative samples in the existing methods of social media bot detection, and to reduce the training of abnormal samples in the model, we propose an anomaly detection framework based on a combination of a Variational AutoEncoder and

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Elmas, Tuğrulcan, Rebekah Overdorf, and Karl Aberer. "Characterizing Retweet Bots: The Case of Black Market Accounts." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 171–82. http://dx.doi.org/10.1609/icwsm.v16i1.19282.

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Malicious Twitter bots are detrimental to public discourse on social media. Past studies have looked at spammers, fake followers, and astroturfing bots, but retweet bots, which artificially inflate content, are not well understood. We present the first study focusing exclusively on retweet bots. We characterize retweet bots that have been uncovered by purchasing retweets from the black market. We detect whether they are fake or genuine accounts involved in inauthentic activities and what they do in order to appear legitimate. We also analyze their differences from human-controlled accounts. Fr

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Alipour, Sanaz Adel, Rita Orji, and Nur Zincir-Heywood. "Behaviour and Bot Analysis on Online Social Networks." International Journal of Technology and Human Interaction 19, no. 1 (2023): 1–19. http://dx.doi.org/10.4018/ijthi.327789.

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The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefo

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Latah, Majd. "Detection of malicious social bots: A survey and a refined taxonomy." Expert Systems with Applications 151 (August 2020): 113383. http://dx.doi.org/10.1016/j.eswa.2020.113383.

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S G, Deekshith. "Twitter Bots Detection Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1536–41. http://dx.doi.org/10.22214/ijraset.2021.36637.

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The social network, a crucial part of our life is plagued by online impersonation and fake accounts. Fake profiles are mostly used by the intruders to carry out malicious activities such as harming person , identity theft and privacy intrusion in Online Social Network(OSN). Hence identifying an account is genuine or fake is one of the critical problem in OSN .In this paper we proposed many classification algorithm like Support Vector Machine algorithm ,KNN, and Random Forest algorithm. It also studies the comparison of classification methods on Spam User dataset which is used to select the bes

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Dhanesh, Arya, Jyothika K, Malavika Jayaraj, Nevin Jose Antony, and Anu Treesa George. "Comprehensive Strategies for Identifying X(Twitter) Bots." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43583.

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Twitter is a social network where users interact via text-based posts called tweets, using hashtags, mentions, shortened URLs, and retweets. The growing user base and open nature of Twitter have made it a target for automated programs, known as bots, which can be both beneficial and malicious. This research focuses on detecting and classifying Twitter accounts as human, bot, or cyborg. Given Twitter’s open nature, both helpful and harmful bots are prevalent, necessi- tating effective detection strategies. The study analyzes account behavior, content, and properties, introducing a classificatio

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Mr. S.V Hemanth, S Sneha Reddy, R Nithin, G Keerthi, and Shinde Vinayak Rao Patil. "Automated Bot Detection on Twitter UsingURL Patterns and Learning Automata." international journal of engineering technology and management sciences 8, no. 3 (2024): 205–10. http://dx.doi.org/10.46647/ijetms.2024.v08i03.025.

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The fight against fake news and propaganda on social media becomes increasinglydifficult as malicious bots impersonate real users. These imposters spread misinformation throughcompromised or inauthentic accounts, often tricking users with shortened URLs that contain virusesand lead to malicious websites. Therefore, distinguishing between these bots and genuine Twitterusers is crucial. Analyzing user interactions within the social network can be time-consuming. Thisresearch proposes a more efficient approach: LA-MSBD, an algorithm that relies on learningautomata. LA-MSBD focuses on URL-based fe

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Shevtsov, Alexander, Christos Tzagkarakis, Despoina Antonakaki, and Sotiris Ioannidis. "Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 956–67. http://dx.doi.org/10.1609/icwsm.v16i1.19349.

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Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination a

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Zhang, Weisha, Jiazhong Lu, Yulin Liu, and Xiaojun Liu. "Twitter Bots in Cyber-Physical-Social Systems: Detection and Estimation Based on the SEIR Model." Security and Communication Networks 2023 (May 8, 2023): 1–9. http://dx.doi.org/10.1155/2023/6234030.

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Bots are now part of the social media landscape, and thus, a threat to cyber-physical-social systems (CPSSs). A better understanding of their characteristic behaviors and estimation of their impact on public opinion could help improve the algorithms to identify bots and help develop strategies to reduce their influence. The cosine function-based algorithm is able to compare the similarity between tweets and restore the course of information circulation. Combined with malicious features of an account, our method could effectively detect bots. We implement SEIR model to compute tweets with the h

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Iliou, Christos, Theodoros Kostoulas, Theodora Tsikrika, Vasilis Katos, Stefanos Vrochidis, and Ioannis Kompatsiaris. "Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics." Digital Threats: Research and Practice 2, no. 3 (2021): 1–26. http://dx.doi.org/10.1145/3447815.

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Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework

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SATOH, Akihiro, Yutaka NAKAMURA, Yutaka FUKUDA, Daiki NOBAYASHI, and Takeshi IKENAGA. "An Approach for Identifying Malicious Domain Names Generated by Dictionary-Based DGA Bots." IEICE Transactions on Information and Systems E104.D, no. 5 (2021): 669–72. http://dx.doi.org/10.1587/transinf.2020ntl0001.

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Assenmacher, Dennis, Lena Clever, Lena Frischlich, Thorsten Quandt, Heike Trautmann, and Christian Grimme. "Demystifying Social Bots: On the Intelligence of Automated Social Media Actors." Social Media + Society 6, no. 3 (2020): 205630512093926. http://dx.doi.org/10.1177/2056305120939264.

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Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophist

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Kareem, Rafeef, and Wesam Bhaya. "Fake Profiles Types of Online Social Networks: A Survey." International Journal of Engineering & Technology 7, no. 4.19 (2018): 919. http://dx.doi.org/10.14419/ijet.v7i4.19.28071.

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Today, OSNs (Online Social Networks) considered the most platforms common on the Internet. It plays a substantial role for users of the internet to hold out their everyday actions such as news reading, content sharing, product reviews, messages posting, and events discussing etc. Unfortunately, on the OSNs some new attacks have been recognized. Different types of spammers are existing in these OSNs. These cyber-criminals containing online fraudsters, sexual predators, catfishes, social bots, and advertising campaigners etc.OSNs abuse in different ways especially by creating fake profiles to ca

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Molkova, L. Y., and M. V. Gofman. "Ensuring security in the ecosystem of intelligent transport infrastructure using methods to combat malicious bots." E3S Web of Conferences 549 (2024): 08025. http://dx.doi.org/10.1051/e3sconf/202454908025.

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In the context of the growing threat of cyber attacks on transport systems, it is becoming increasingly important to ensure security in the ecosystem of intelligent transport infrastructure (ITI). This article combines methods of combating malicious bots with methods of ensuring security in ITI, considering botmaster search methods and protection techniques in the context of transport systems. Network traffic analysis, the use of honeypots and data encryption technologies are considered as tools for detecting threats and protecting against cyber attacks, which contributes to the creation of a

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Bazarkina, Darya Yu, and Evgeny N. Pashentsev. "Malicious Use of Artificial Intelligence." Russia in Global Affairs 18, no. 4 (2020): 154–77. http://dx.doi.org/10.31278/1810-6374-2020-18-4-154-177.

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The article identifies the main risks and threats related to national and international psychological security (PS) in BRICS countries (particularly China, India, and Russia) and posed by the malicious use of artificial intelligence (AI). The main methods of research are systemic, scenario, and case analyses. The authors maintain that PS threats, both national and international, created by the malicious use of AI should be considered at three levels. At the first level, a false negative image of AI is spread. The second level of PS threats is directly related to the malicious use of AI (MUAI),

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Rout, Rashmi Ranjan, Greeshma Lingam, and D. V. L. N. Somayajulu. "Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network." IEEE Transactions on Computational Social Systems 7, no. 4 (2020): 1004–18. http://dx.doi.org/10.1109/tcss.2020.2992223.

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Kumar, Sudhir. "Online Defamation in the Digital Age: Issues and Challenges with Particular Reference to Deepfakes and Malicious Bots." International Journal of Law and Policy 2, no. 8 (2024): 32–41. http://dx.doi.org/10.59022/ijlp.200.

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The digital age has ushered in a new era of communication, marked by the proliferation of social media platforms and the lightning-fast spread of information. However, this interconnectedness also presents challenges in the realm of online defamation. The internet has opened up new avenues for defamation, and bad actors are exploiting them with increasingly sophisticated tools. This research paper investigates the issues and challenges associated with online defamation law in the digital age particular with reference to deepfakes and malicious bots.

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Guerar, Meriem, Luca Verderame, Mauro Migliardi, Francesco Palmieri, and Alessio Merlo. "Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma." ACM Computing Surveys 54, no. 9 (2022): 1–33. http://dx.doi.org/10.1145/3477142.

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A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [102]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, and so on. Thus, they represent a serious threat to all businesses in general, but are especially troublesome for e-commerce, travel, and financial services. One of the most common defense mechanisms against bots abusing online services is the introduction of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTC

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S, Varsha, P. S. Prathiba, N. Deepika, Janghel Neha, and V. Ashoka D. "Performance Analysis of Machine Learning Algorithms in SMP: A Case Study of Twitter." Journal of Computer Science Engineering and Software Testing 5, no. 2 (2019): 17–22. https://doi.org/10.5281/zenodo.3268439.

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<em>The number of people using Social Media Platform (SMP) is increasing day by day. A few users may hide their identity with malicious intentions. Previous research has detected fake accounts created by bots using machine learning concepts. These ML concepts used engineered features such as the ‘following-to-followers ratio’ which is generally available in their accounts. In previous studies these similarly clustered features were applied to the machine learning models for detection of fake and real accounts. In the recent research the behavioural features like the sentient of the

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Klinkhammer, Dennis. "Misuse of large language models: Exploiting weaknesses for target-specific outputs." TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 33, no. 2 (2024): 29–34. http://dx.doi.org/10.14512/tatup.33.2.29.

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Prompt engineering in large language models (LLMs) in combination with external context can be misused for jailbreaks in order to generate malicious outputs. In the process, jailbreak prompts are apparently amplified in such a way that LLMs can generate malicious outputs on a large scale despite their initial training. As social bots, these can contribute to the dissemination of misinformation, hate speech, and discriminatory content. Using GPT4-x-Vicuna-13b-4bit from NousResearch, we demonstrate in this article the effectiveness of jailbreak prompts and external contexts via Jupyter Notebook

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Yamaguchi, Shingo. "Botnet Defense System: Observability, Controllability, and Basic Command and Control Strategy." Sensors 22, no. 23 (2022): 9423. http://dx.doi.org/10.3390/s22239423.

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This paper deals with the observability, controllability, and command and control strategy in the Botnet Defense System (BDS) that disinfects malicious botnets with white-hat botnets. The BDS defends an IoT system built over the Internet. The Internet is characterized by openness, but not all nodes are observable and controllable. We incorporated the concept of observability and controllability into the BDS design and theoretically clarified that the BDS can enhance its observability and controllability by utilizing its white-hat botnets. In addition, we proposed a Withdrawal strategy as a bas

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Khan, Rafaqat Alam. "Spammer Detection: A Study of Spam Filter Commentson YouTube Videos." International Journal for Electronic Crime Investigation 2, no. 4 (2018): 5. http://dx.doi.org/10.54692/ijeci.2018.020425.

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 This paper presents a methodology to find out the spam comments on YouTube videos. The purpose of this research is to find out the comments of those spam users, who comment for their own promotional intentions or to detect users whose comments that have no relevancy with the given video.The monetization policy introduced by YouTube for its user's channel and advertisement of different ads on YouTube videos has attracted a large number of users. This increase in a large number of users has also lead to an increase in malicious users whose job is to create automated bots for commenting an

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Khan, Rafaqat Alam. "Spammer Detection: A Study of Spam Filter Commentson YouTube Videos." International Journal for Electronic Crime Investigation 2, no. 4 (2018): 5. http://dx.doi.org/10.54692/ijeci.2019.030125.

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 This paper presents a methodology to find out the spam comments on YouTube videos. The purpose of this research is to find out the comments of those spam users, who comment for their own promotional intentions or to detect users whose comments that have no relevancy with the given video.The monetization policy introduced by YouTube for its user's channel and advertisement of different ads on YouTube videos has attracted a large number of users. This increase in a large number of users has also lead to an increase in malicious users whose job is to create automated bots for commenting an

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M, Vinay Sai. "Detecting and Analyzing the Malicious Social Bots by using Data Mining and Naïve Bayesian Classifier." International Journal of Emerging Trends in Engineering Research 8, no. 7 (2020): 3345–50. http://dx.doi.org/10.30534/ijeter/2020/75872020.

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Woodiss-Field, Ashley, Michael N. Johnstone, and Paul Haskell-Dowland. "Examination of Traditional Botnet Detection on IoT-Based Bots." Sensors 24, no. 3 (2024): 1027. http://dx.doi.org/10.3390/s24031027.

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A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluat

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Saurabh Chauhan and Shreya Kapoor. "Sliding Puzzle CAPTCHA Analysis." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 165–70. http://dx.doi.org/10.46501/ijmtst061232.

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Today a number of everyday activities are done through the Internet. To perform such web services users must register in relation to websites or fill some form. In such websites, some hackers write malicious programs called bots that destroy website resources by creating fake registrations or form submissions. This false registration may adversely affect the performance of websites. Therefore, it is necessary to distinguish between actual human users and Web bots (or computer programs) via tests known as CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). Most

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Jin, Rui, and Yong Liao. "Prevention Is Better than Cure: Exposing the Vulnerabilities of Social Bot Detectors with Realistic Simulations." Applied Sciences 15, no. 11 (2025): 6230. https://doi.org/10.3390/app15116230.

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The evolution of social bots, i.e., accounts on social media platforms controlled by malicious software, is making them increasingly more challenging to discover. A practical solution is to explore the adversarial nature of novel bots and find the vulnerability of bot detectors in simulations in advance. However, current studies fail to realistically simulate the environment and bots’ actions, thus not effectively representing the competition between novel bots and bot detectors. Hence, we propose a new method for modeling the impact of bot actions and develop a new bot strategy to simulate va

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Karthikayini, Thavasimani, and Kasturirangan Srinath Nuggehalli. "Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4031–41. https://doi.org/10.11591/ijece.v12i4.pp4031-4041.

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Hyperparameter optimization is one of the main challenges in deep learning despite its successful exploration in many areas such as image classification, speech recognition, natural language processing, and fraud detections. Hyperparameters are critical as they control the learning rate of a model and should be tuned to improve performance. Tuning the hyperparameters manually with default values is a challenging and time-intensive task. Though the time and efforts spent on tuning the hyperparameters are decreasing, it is always a burden when it comes to a new dataset or solving a new task or i

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Sadeghpour, Shadi, and Natalija Vlajic. "Click Fraud in Digital Advertising: A Comprehensive Survey." Computers 10, no. 12 (2021): 164. http://dx.doi.org/10.3390/computers10120164.

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Recent research has revealed an alarming prevalence of click fraud in online advertising systems. In this article, we present a comprehensive study on the usage and impact of bots in performing click fraud in the realm of digital advertising. Specifically, we first provide an in-depth investigation of different known categories of Web-bots along with their malicious activities and associated threats. We then ask a series of questions to distinguish between the important behavioral characteristics of bots versus humans in conducting click fraud within modern-day ad platforms. Subsequently, we p

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Kadam*, Nitika, and Harish Patidar. "Social Media Fake Profile Detection Technique Based on Attribute Estimation and Content Analysis Method." International Journal of Recent Technology and Engineering (IJRTE) 8, no. 6 (2020): 4534–39. http://dx.doi.org/10.35940/ijrte.f8414.038620.

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Social media is a virtual place where every age group members are available. Members are using this platform to share knowledge and information. But some of them are misuse and abuse the services of social media. In order to perform malicious activities the users and BOTs are creating fake profile, these profiles are used for spreading the unsolicited and malicious contents. Therefore the proposed work is motivated to study about the social media platform and it’s security and privacy challenges. In this context, the recent year’s progress in the domain of social media is explored, and the lit

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Kolomeets, Maxim, Olga Tushkanova, Vasily Desnitsky, Lidia Vitkova, and Andrey Chechulin. "Experimental Evaluation: Can Humans Recognise Social Media Bots?" Big Data and Cognitive Computing 8, no. 3 (2024): 24. http://dx.doi.org/10.3390/bdcc8030024.

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This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to detect them better than random selection. As a result, obtaining a ground-truth dataset with human annotation is not possible, which leads to supervised machine-learning models inheriting annotation errors. To test this hypothesis, we conducted an experiment where humans were tasked with recognizing malicious bots on the V

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Guo, Qinglang, Haiyong Xie, Yangyang Li, Wen Ma, and Chao Zhang. "Social Bots Detection via Fusing BERT and Graph Convolutional Networks." Symmetry 14, no. 1 (2021): 30. http://dx.doi.org/10.3390/sym14010030.

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The online social media ecosystem is becoming more and more confused because of more and more fake information and the social media of malicious users’ fake content; at the same time, unspeakable pain has been brought to mankind. Social robot detection uses supervised classification based on artificial feature extraction. However, user privacy is also involved in using these methods, and the hidden feature information is also ignored, such as semi-supervised algorithms with low utilization rates and graph features. In this work, we symmetrically combine BERT and GCN (Graph Convolutional Networ

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Al-Nawasrah, Ahmad, Ammar Ali Almomani, Samer Atawneh, and Mohammad Alauthman. "A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing." International Journal of Cloud Applications and Computing 10, no. 3 (2020): 17–53. http://dx.doi.org/10.4018/ijcac.2020070102.

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A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increas

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Yamaguchi, Shingo. "Botnet Defense System: Concept, Design, and Basic Strategy." Information 11, no. 11 (2020): 516. http://dx.doi.org/10.3390/info11110516.

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This paper proposes a new kind of cyber-security system, named Botnet Defense System (BDS), which defends an Internet of Things (IoT) system against malicious botnets. The concept of BDS is “Fight fire with fire”. The distinguishing feature is that it uses white-hat botnets to fight malicious botnets. A BDS consists of four components: Monitor, Strategy Planner, Launcher, and Command and Control (C&C) server. The Monitor component watches over a target IoT system. If the component detects a malicious botnet, the Strategy Planner component makes a strategy against the botnet. Based on the p

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Abdul Razak, Siti Fatimah, Ku Yee Fang, Noor Hisham Kamis, Anang Hudaya Muhammad Amin, and Sumendra Yogarayan. "Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks." HighTech and Innovation Journal 4, no. 4 (2023): 854–69. http://dx.doi.org/10.28991/hij-2023-04-04-014.

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Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data

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Zhao, Chensu, Yang Xin, Xuefeng Li, Hongliang Zhu, Yixian Yang, and Yuling Chen. "An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks." Applied Sciences 10, no. 22 (2020): 8160. http://dx.doi.org/10.3390/app10228160.

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With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existing research mainly uses feature-based detection or propagation-based detection, and it applies machine learning or graph mining algorithms to identify anomaly accounts in social networks. However, with the development of technology, spam bots are becoming more advanced, and identifying bots is still an open challenge. This paper proposes a new semi-supervised

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