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Littérature scientifique sur le sujet « Malicious bots »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Malicious bots"

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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Thèses sur le sujet "Malicious bots"

Ljung, Fredrik, and Oscar Arnflo. "How malicious bots interact with an online contest with gamification : A study in methods for identifying and protecting against bots." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186445.

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Setting up online contests with gamification is an effective marketing method, but which brings security complications. By having rewards with high value, cheaters are attracted to participate with the use of malicious bots. To distinguish bots from humans different methods are used which are divided into Human Interactive Proof (HIP) and Human Observational Proof (HOP). This report aims to look at the effectiveness of the most popular HIPs and HOPs and how an attacker is able to bypass them. From the results, parameters that are of interest when implementing a framework to detect and prevent

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Al-Hammadi, Yousof Ali Abdulla. "Behavioural correlation for malicious bot detection." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11359/.

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Over the past few years, IRC bots, malicious programs which are remotely controlled by the attacker, have become a major threat to the Internet and its users. These bots can be used in different malicious ways such as to launch distributed denial of service (DDoS) attacks to shutdown other networks and services. New bots are implemented with extended features such as keystrokes logging, spamming, traffic sniffing, which cause serious disruption to targeted networks and users. In response to these threats, there is a growing demand for effective techniques to detect the presence of bots/botnets

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Zarras, Apostolos [Verfasser], Thorsten [Akademischer Betreuer] Holz, and Herbert [Akademischer Betreuer] Bos. "Towards detection and prevention of malicious activities against web applications and internet services / Apostolos Zarras. Gutachter: Thorsten Holz ; Herbert Bos." Bochum : Ruhr-Universität Bochum, 2016. http://d-nb.info/1082425583/34.

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Cruz, Paulo Alexandre Patrão da. "Malicious BOT Detection." Master's thesis, 2019. http://hdl.handle.net/10316/87307.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia O presente documento descreve o trabalho desenvolvido ao longo do ano no âmbito do estágio do Mestrado de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de Coimbra (FCTUC) no Departamento de Engenharia Informática (DEI) no ano letivo 2018/2019, com especialização em Engenharia de Sofware, realizado na empresa WIT-Software (WIT). Este contempla as tarefas desenvolvidas no período descrito, desde o equadramento do problema e levantamento do Estado de Arte, a

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Tran, Khoi-Nguyen Dao. "Detecting vandalism on Wikipedia across multiple languages." Phd thesis, 2015. http://hdl.handle.net/1885/14453.

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Vandalism, the malicious modification or editing of articles, is a serious problem for free and open access online encyclopedias such as Wikipedia. Over the 13 year lifetime of Wikipedia, editors have identified and repaired vandalism in 1.6% of more than 500 million revisions of over 9 million English articles, but smaller manually inspected sets of revisions for research show vandalism may appear in 7% to 11% of all revisions of English Wikipedia articles. The persistent threat of vandalism has led to the development of automated programs (bots) and editing assistance programs to help

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Chih-Hao, Su, and 蘇至浩. "Near Optimal Defense Resource Allocation and Information Dividing-and-Allocation Strategies to Minimize Information Leakage Considering both Random Errors and Malicious Attacks." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/19113779985732747071.

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碩士 國立臺灣大學 資訊管理學研究所 96 Information technology has been increasingly progressing, and the storage cost has been reducing. Thus, individuals, enterprises and government organizations are likely to store secret data through electronic way. Moreover, along with the rise of the use of network and the prevalence of e-commerce, the crime of information theft through network has grown in high-speed. Cyber crimes, like phishing or installing Trojan horse in victims’ computers to steal information, will cause serious damage to individuals or enterprises. From the above reasons, to protect sec

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Su, Chih-Hao. "Near Optimal Defense Resource Allocation and Information Dividing-and-Allocation Strategies to Minimize Information Leakage Considering both Random Errors and Malicious Attacks." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200817422600.

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"Discovering and Mitigating Social Data Bias." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.45009.

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abstract: Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As th

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Livres sur le sujet "Malicious bots"

Dunham, Ken. Malicious bots: An inside look into the cyber-criminal underground of the internet. Auerbach Publications, 2008.

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Munsch, Robert N. The boy in the drawer. Annick Press, 1985.

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Munsch, Robert N. The boy in the drawer. Annick Press, 1985.

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Munsch, Robert N. The boy in the drawer. Annick Press, 1992.

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Munsch, Robert N. The boy in the drawer. Annick Press, 1986.

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Munsch, Robert N. Chou ti li di xiao nan hai. Annick Press Ltd., 1992.

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Dunham, Ken, and Jim Melnick. Malicious Bots. Auerbach Publications, 2008. http://dx.doi.org/10.1201/9781420069068.

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Malicious Bots. Taylor and Francis, 2008.

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Dunham, Ken, and Jim Melnick. Malicious Bots. Taylor & Francis Group, 2019.

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Dunham, Ken, and Jim Melnick. Malicious Bots: An Inside Look into the Cyber-Criminal Underground of the Internet. Taylor & Francis Group, 2008.

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Chapitres de livres sur le sujet "Malicious bots"

Satija, Tanu, and Nirmalya Kar. "Detecting Malicious Twitter Bots Using Machine Learning." In Advances in Computational Intelligence, Security and Internet of Things. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3666-3_16.

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Chen, Zhouhan, Rima S. Tanash, Richard Stoll, and Devika Subramanian. "Hunting Malicious Bots on Twitter: An Unsupervised Approach." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67256-4_40.

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Cresci, Stefano. "Detecting Malicious Social Bots: Story of a Never-Ending Clash." In Disinformation in Open Online Media. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39627-5_7.

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Pardo Echevarría, Daniel, Nayma Cepero-Perez, Mailyn Moreno-Espino, Helder J. Chissingui, and Humberto Díaz-Pando. "Detection of Malicious Bots Using a Proactive Supervised Classification Approach." In Progress in Artificial Intelligence and Pattern Recognition. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49552-6_26.

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Matsumoto, Ryutaro, and Shingo Yamaguchi. "On Building a Firewall with White Hat Bots in the Neighborhood of Malicious Bots in Large-Scale Networks." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-5703-9_51.

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Kumar, R. Kiran, G. Ramesh Babu, G. Sai Chaitanya Kumar, and N. Raghavendra Sai. "Malicious Social Bots Detection in the Twitter Network Using Learning Automata with URL Features." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6690-5_43.

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Spyros, Arnolnt, Dimitrios Kavallieros, Theodora Tsikrika, Stefanos Vrochidis, and Ioannis Kompatsiaris. "SECANT: Cyberthreat Intelligence in IoMT Ecosystems." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_37.

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AbstractMedical organisations are at great risk of cyberattacks. Considering the complex infrastructure, the integration of susceptible Internet of Medical Things devices and lack of appropriate cybersecurity training of the staff, both technical and medical, the healthcare domain is a common target for malicious actors. Therefore, to safeguard their infrastructure, medical organisations should implement measures such as threat assessment, encryption and cybersecurity training to address all the challenges. The SECANT platform introduces a holistic approach which includes a collaborative threa

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Senarath, Yasas, Hemant Purohit, and Rajendra Akerkar. "Violence-Inducing Behavior Prevention in Social-Cyber Space." In AI, Data, and Digitalization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53770-7_10.

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AbstractHate speech, radicalization, and polarization in online social environments are some of the leading global societal challenges today. How to respond to online hate speech leading to violence and social threats is a question troubling many democracies – including Norway. Such malicious online behaviors not only impede the universal right to a free and peaceful existence, they also negatively affect response efforts of both public and official agencies during disasters, and thus, local community services require tools to monitor risks to community resilience from the social environment.

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Gallagher, Sean, Ben Gelman, Salma Taoufiq, et al. "Phishing and Social Engineering in the Age of LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_8.

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AbstractThe human factor remains a major vulnerability in cybersecurity. This chapter explores the escalating threats that Large Language Models (LLMs) pose in the field of cybercrime, particularly in phishing and social engineering. Due to their ability to generate highly convincing and individualized content, LLMs enhance the effectiveness and scale of phishing attacks, making them increasingly difficult to detect. The integration of multimodal generative models allows malicious actors to leverage AI-generated text, images, and audio, increasing attack avenues and making attacks more convinc

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"Botnets and the eCrime Cycle: The iSIGHT Partners’ Approach." In Malicious Bots. Auerbach Publications, 2008. http://dx.doi.org/10.1201/9781420069068-10.

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Actes de conférences sur le sujet "Malicious bots"

Bhale, Kanchan, and Thirupurasundari D. R. "Malicious Social Bot Detection in Twitter: A Review." In 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT). IEEE, 2024. https://doi.org/10.1109/c3it60531.2024.10829441.

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Swathi, Palagiri, Mousumi Karmakar, Sumit Kumar Banshal, and Raka Moni. "Malicious Social Bot Detection: RL-RNN Based Hybrid Approach." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866368.

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Shuvo, Amit Mazumder, Md Sami Ul Islam Sami, Md Latifur Rahman, et al. "SYSFID—System-Aware Fault-Injection Attack Detection for System-In-Package Architectures." In ISTFA 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.istfa2024p0332.

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Abstract A system-in-package (SiP) design takes advantage of cutting-edge packaging technology and heterogeneous integration (HI) in response to the growing need for aggressive time-to-market, high-performance, less expensive, and smaller systems. However, aggregating dies with different functionalities introduces new attack vectors with fault-injection attacks (FIA) that can effectively alter a circuit's data and control flow maliciously to cause disruptions of secure communication or sensitive information leakage. Additionally, traditional threat models associated with FIA on a 2D monolithic

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"Bots: Genuine or Malicious." In 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2023. http://dx.doi.org/10.1109/uemcon59035.2023.10316169.

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Yamanoue, Takashi, Kentaro Oda, and Koichi Shimozono. "Capturing malicious bots using a beneficial bot and wiki." In the ACM SIGUCCS 40th annual conference. ACM Press, 2012. http://dx.doi.org/10.1145/2382456.2382477.

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Kolomeets, Maxim, and Andrey Chechulin. "Analysis of the Malicious Bots Market." In 2021 29th Conference of Open Innovations Association (FRUCT). IEEE, 2021. http://dx.doi.org/10.23919/fruct52173.2021.9435421.

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Rao, Paradesi Subba, Farooq Sunar Mahammad, Parumanchala Bhaskar, et al. "Detecting malicious Twitter bots using machine learning." In INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ELECTRONICS AND COMMUNICATION ENGINEERING - 2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0212693.

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Brokate, Cristian, Manon Richard, Lisa Giordani, and Jean Liénard. "SWATTING Spambots: Real-time Detection of Malicious Bots on X." In WWW '24: The ACM Web Conference 2024. ACM, 2024. http://dx.doi.org/10.1145/3589335.3651564.

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Sruthi, Mamidala, Sravanthi Thota, P. Kumaraswamy, and G. Mahesh. "Demonstrate the performance of social bots in identifying the malicious bots based during communication in social media." In INTERNATIONAL CONFERENCE ON RESEARCH IN SCIENCES, ENGINEERING & TECHNOLOGY. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0082276.

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Vashisht, Swati, Sushil Kumar Gupta, Atul Fegade, Shiv Ashish Dhondiyal, Rohit Kumar, and G. Revathy. "Detection of Malicious Social Bots with the Aid of Learning Automata on Twitter." In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2022. http://dx.doi.org/10.1109/ic3i56241.2022.10073346.

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Rapports d'organisations sur le sujet "Malicious bots"

Kelly, Luke. Threats to Civilian Aviation Since 1975. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/k4d.2021.019.

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This literature review finds that the main malicious threats to civilian aviation since 1975 are attacks by terrorist groups, deliberate or accidental damage arising from conflicts, and incidents caused by people who work for airlines or airports. While the sector has responded to hijackings and bombings with increasing security since the 1970s, actors seeking to attack aircraft have modified their tactics, and new threats such as liquid explosives and cyber attacks have emerged. Civilian aviation has seen relatively fewer accidents and deaths over the years, but threats remain. The review foc

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