Relevant bibliographies by topics / Malicious domain names

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Malicious domain names'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Malicious domain names"

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Yang, Cheng, Tianliang Lu, Shangyi Yan, Jianling Zhang, and Xingzhan Yu. "N-Trans: Parallel Detection Algorithm for DGA Domain Names." Future Internet 14, no. 7 (2022): 209. http://dx.doi.org/10.3390/fi14070209.

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Domain name generation algorithms are widely used in malware, such as botnet binaries, to generate large sequences of domain names of which some are registered by cybercriminals. Accurate detection of malicious domains can effectively defend against cyber attacks. The detection of such malicious domain names by the use of traditional machine learning algorithms has been explored by many researchers, but still is not perfect. To further improve on this, we propose a novel parallel detection model named N-Trans that is based on the N-gram algorithm with the Transformer model. First, we add flag bits to the first and last positions of the domain name for the parallel combination of the N-gram algorithm and Transformer framework to detect a domain name. The model can effectively extract the letter combination features and capture the position features of letters in the domain name. It can capture features such as the first and last letters in the domain name and the position relationship between letters. In addition, it can accurately distinguish between legitimate and malicious domain names. In the experiment, the dataset is the legal domain name of Alexa and the malicious domain name collected by the 360 Security Lab. The experimental results show that the parallel detection model based on N-gram and Transformer achieves 96.97% accuracy for DGA malicious domain name detection. It can effectively and accurately identify malicious domain names and outperforms the mainstream malicious domain name detection algorithms.
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Yang, Luhui, Guangjie Liu, Weiwei Liu, Huiwen Bai, Jiangtao Zhai, and Yuewei Dai. "Detecting Multielement Algorithmically Generated Domain Names Based on Adaptive Embedding Model." Security and Communication Networks 2021 (May 31, 2021): 1–20. http://dx.doi.org/10.1155/2021/5567635.

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With the development of detection algorithms on malicious dynamic domain names, domain generation algorithms have developed to be more stealthy. The use of multiple elements for generating domains will lead to higher detection difficulty. To effectively improve the detection accuracy of algorithmically generated domain names based on multiple elements, a domain name syntax model is proposed, which analyzes the multiple elements in domain names and their syntactic relationship, and an adaptive embedding method is proposed to achieve effective element parsing of domain names. A parallel convolutional model based on the feature selection module combined with an improved dynamic loss function based on curriculum learning is proposed, which can achieve effective detection on multielement malicious domain names. A series of experiments are designed and the proposed model is compared with five previous algorithms. The experimental results denote that the detection accuracy of the proposed model for multiple-element malicious domain names is significantly higher than that of the comparison algorithms and also has good adaptability to other types of malicious domain names.
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Wagan, Atif Ali, Qianmu Li, Zubair Zaland, et al. "A Unified Learning Approach for Malicious Domain Name Detection." Axioms 12, no. 5 (2023): 458. http://dx.doi.org/10.3390/axioms12050458.

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The DNS firewall plays an important role in network security. It is based on a list of known malicious domain names, and, based on these lists, the firewall blocks communication with these domain names. However, DNS firewalls can only block known malicious domain names, excluding communication with unknown malicious domain names. Prior research has found that machine learning techniques are effective for detecting unknown malicious domain names. However, those methods have limited capabilities to learn from both textual and numerical data. To solve this issue, we present a novel unified learning approach that uses both numerical and textual features of the domain name to classify whether a domain name pair is malicious or not. The experiments were conducted on a benchmark domain names dataset consisting of 90,000 domain names. The experimental results show that the proposed approach performs significantly better than the six comparative methods in terms of accuracy, precision, recall, and F1-Score.
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Zhao, Hong, Zhaobin Chang, Guangbin Bao, and Xiangyan Zeng. "Malicious Domain Names Detection Algorithm Based on N-Gram." Journal of Computer Networks and Communications 2019 (February 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/4612474.

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Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N-Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms.
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Alhogail, Areej, and Isra Al-Turaiki. "Improved Detection of Malicious Domain Names Using Gradient Boosted Machines and Feature Engineering." Information Technology and Control 51, no. 2 (2022): 313–31. http://dx.doi.org/10.5755/j01.itc.51.2.30380.

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Malicious domain names have been commonly used in recent years to launch different cyber-attacks. There are a large number of malicious domains that are registered every day and some of which are only active for brief periods of time. Therefore, the automated malicious domain names detection is needed to provide security for individuals and organisations. As new technologies continue to emerge, the detection of malicious domain names remains a challenging task. In this study, we propose a model to effectively detect malicious domain names. This is done by evaluating the performance of several machine learning algorithms and feature importance measures using a recent DNS dataset. Based on the empirical evaluation, the gradient boosted machines GBM classification with a combination of lexical and host-based features produce the most accurate detection rates of 98.8% accuracy and a low false positive rate of 0.003. In terms of feature importance, measures used in this study agree on the importance of six features, five of which are lexical in nature. Furthermore, to make the best out of these relevant features, we apply automatic feature engineering. Our results show that preprocessing the dataset using deep feature synthesis and then reducing the dimensionality improves the classifications performance as compared to using raw features. The results of this study are then verified using a challenging category of domain names, the domain generation algorithm dataset, and consistent results are obtained.
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Desmet, Lieven, Jan Spooren, Thomas Vissers, Peter Janssen, and Wouter Joosen. "P remadoma." Digital Threats: Research and Practice 2, no. 1 (2021): 1–24. http://dx.doi.org/10.1145/3419476.

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DNS is one of the most essential components of the Internet, mapping domain names to the IP addresses behind almost every online service. Domain names are therefore also a fundamental tool for attackers to quickly locate and relocate their malicious activities on the Internet. In this article, we design and evaluate P remadoma , a solution for DNS registries to predict malicious intent well before a domain name becomes operational. In contrast to blacklists, which only offer protection after some harm has already been done, this system can prevent domain names from being used before they can pose any threats. We advance the state of the art by leveraging recent insights into the ecosystem of malicious domain registrations, focusing explicitly on facilitators employed for bulk registration and similarity patterns in registrant information. We thoroughly evaluate the proposed prediction model’s performance and adaptability on an 11-month testing set and address complex and domain-specific dataset challenges. Moreover, we have successfully deployed P remadoma in the operational environment of the .eu ccTLD registry, resulting in a decline of malicious registrations. Finally, we have identified and quantified three possible evasion patterns and have observed changes in the malicious registration ecosystem since P remadoma has been operationalized.
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Satoh, Akihiro, Yutaka Fukuda, Gen Kitagata, and Yutaka Nakamura. "A Word-Level Analytical Approach for Identifying Malicious Domain Names Caused by Dictionary-Based DGA Malware." Electronics 10, no. 9 (2021): 1039. http://dx.doi.org/10.3390/electronics10091039.

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Computer networks are facing serious threats from the emergence of malware with sophisticated DGAs (Domain Generation Algorithms). This type of DGA malware dynamically generates domain names by concatenating words from dictionaries for evading detection. In this paper, we propose an approach for identifying the callback communications of such dictionary-based DGA malware by analyzing their domain names at the word level. This approach is based on the following observations: These malware families use their own dictionaries and algorithms to generate domain names, and accordingly, the word usages of malware-generated domains are distinctly different from those of human-generated domains. Our evaluation indicates that the proposed approach is capable of achieving accuracy, recall, and precision as high as 0.9989, 0.9977, and 0.9869, respectively, when used with labeled datasets. We also clarify the functional differences between our approach and other published methods via qualitative comparisons. Taken together, these results suggest that malware-infected machines can be identified and removed from networks using DNS queries for detected malicious domain names as triggers. Our approach contributes to dramatically improving network security by providing a technique to address various types of malware encroachment.
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Chiba, Daiki, Mitsuaki Akiyama, Takeshi Yagi, Kunio Hato, Tatsuya Mori, and Shigeki Goto. "DomainChroma: Building actionable threat intelligence from malicious domain names." Computers & Security 77 (August 2018): 138–61. http://dx.doi.org/10.1016/j.cose.2018.03.013.

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Moskvichev, Anton, and Ksenia Moskvicheva. "Using DNS Tunneling to Transfer Malicious Software." Voprosy kiberbezopasnosti, no. 4(50) (2022): 91–99. http://dx.doi.org/10.21681/2311-3456-2022-4-91-99.

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Purpose of the article: to develop a way to increase the level of protection of an information system from an attack using DNS tunneling. Method: using entropy to identify domains and subdomains used when transferring data through a DNS tunnel. The result: a method of data transmission through the DNS protocol bypassing the information security tools is considered. A malicious file was transferred using DNS tunneling, and an analysis was made of the operation of information protection tools during transmission. Information security tools do not detect the transfer of a malicious file via the DNS protocol, but they do if it is transferred in clear text. The concept of information entropy, its role in data processing is given. By calculating the entropy for domain names, the domain used in the transmission of a malicious file through the DNS tunnel was identified. It is concluded that entropy can be used not only to detect data transfer through the DNS tunnel, but also to detect the activity of malicious software that uses random domain and subdomain names in its work. The scientific novelty lies in the fact that malicious activity is detected without using the knowledge base. There is no need to signature check each DNS request, it is enough to calculate the entropy to detect an attack.
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Ho, Hieu Duc, and Huong Van Ho. "Technical research of detection algorithmically generated malicious domain names using machine learning methods." Journal of Science and Technology on Information security 7, no. 1 (2020): 37–43. http://dx.doi.org/10.54654/isj.v7i1.54.

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Abstract— In recent years, many malware use domain generation algorithm for generating a large of domains to maintain their Command and Control (C&C) network infrastructure. In this paper, we present an approach for detecting malicious domain names using machine learning methods. This approach is using Viterbi algorithm and dictionary for constructing feature of domain names. The approach is demonstrated using a range of legitimate domains and a number of malicious algorithmically generated domain names. The numerical results show the efficiency of this method.Tóm tắt— Trong những năm gần đây, nhiều phần mềm độc hại sử dụng thuật toán sinh tên miền tạo ra lượng lớn các tên miền để duy trì cơ sở hạ tầng mạng ra lệnh và điều khiển (C&C). Trong bài báo này, chúng tôi trình bày một cách tiếp cận để phát hiện tên miền độc hại bằng phương pháp học máy. Cách tiếp cận này sử dụng thuật toán Viterbi và tập từ điển để trích xuất các đặc trưng của tên miền. Cách tiếp cận được thể hiện bằng cách sử dụng một lượng lớn các tên miền hợp pháp và một lượng lớn tên miền độc hại được tạo ra bằng thuật toán sinh tên miền. Các kết quả thực nghiệm đã chỉ ra tính hiệu quả của phương pháp.
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Dissertations / Theses on the topic "Malicious domain names"

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Likarish, Peter F. "Early detection of malicious web content with applied machine learning." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/4871.

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This thesis explores the use of applied machine learning techniques to augment traditional methods of identifying and preventing web-based attacks. Several factors complicate the identification of web-based attacks. The first is the scale of the web. The amount of data on the web and the heterogeneous nature of this data complicate efforts to distinguish between benign sites and attack sites. Second, an attacker may duplicate their attack at multiple, unexpected locations (multiple URLs spread across different domains) with ease. Third, attacks can be hosted nearly anonymously; there is little cost or risk associated with hosting or publishing a web-based attack. In combination, these factors lead one to conclude that, currently, the webs threat landscape is unfavorably tilted towards the attacker. To counter these advantages this thesis describes our novel solutions to web se- curity problems. The common theme running through our work is the demonstration that we can detect attacks missed by other security tools as well as detecting attacks sooner than other security responses. To illustrate this, we describe the development of BayeShield, a browser-based tool capable of successfully identifying phishing at- tacks in the wild. Progressing from specific to a more general approach, we next focus on the detection of obfuscated scripts (one of the most commonly used tools in web-based attacks). Finally, we present TopSpector, a system we've designed to forecast malicious activity prior to it's occurrence. We demonstrate that by mining Top-Level DNS data we can produce a candidate set of domains that contains up to 65% of domains that will be blacklisted. Furthermore, on average TopSpector flags malicious domains 32 days before they are blacklisted, allowing the security community ample time to investigate these domains before they host malicious activity.
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Kim, Dae Wook. "Data-Driven Network-Centric Threat Assessment." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495191891086814.

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Lin, Chia Hung, and 林家宏. "A Lexical Identification Model for Detecting Malicious Domain Names Without Vowels." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/22631757717078302432.

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碩士<br>國立臺北大學<br>資訊工程學系<br>102<br>Botnets generally use a domain name rather than actual IP address to connect its subordinated bot programs. The latest “watering hole attack” enables the botnet to propagate itself not only passively but actively. As the victim’s computer visits some webpages which contain malicious files, the victim is redirected to the C&C server and automatically downloads the newest version of malware. A botnet master, the owner and administrator of it, typically registers many a domain name of random string for their C&C server, so it is a low-cost strategy to defense against botnet by way of identifying malicious domains by their names. Once the connections to the C&C server are blocked, the botnet master can no longer control the victim computers remotely. To detect malicious domain names, especially for those without vowels, a new lexical analysis is developed in this essay with an aim to distinguishing malicious domains from normal ones and to establishing a database of lexical features based on the whitelist. Among every combination of those lexical features, the most suitable one for training is filtered out to create a decision tree model, and its effectiveness of identification is evaluated later. The result shows that both the false positive rate and false negative rate are below five percent, indicating the new lexical analysis can effectively detect malicious domain names without vowels.
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Matveichev, Dmitrii, and DmitriiVladimirovichMatveichev. "Detection of algorithmically generated malicious domain names based on lexical features." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ecgxj8.

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碩士<br>國立臺北大學<br>資訊工程學系<br>106<br>The latest threat reports show a notable increase in detected botnets compared to previous years. In fact, the number of IoT botnet C&C controllers alone more than doubled in 2017. Botnet C&C controllers are used by cybercriminals to launch attacks using botnet enslaved devices. As showed success of Mirai botnet, a lot of companies use poorly secured IoT devices, which gives an opportunity for using IoT devices as botnet zombies. To avoid detection, botnets use domain generation algorithms (DGA) to connect to C&C servers via large number of domain names. This work proposes a low-cost strategy to detect domain names generated by DGA. Statistically lexical features of domain names generated algorithmically differ from those generated by humans. Thus, algorithmically generated domain names can be used to detect botnet or malware activity in the network. To justify the choice of lexical features we gathered domain names statistics of 32 botnets that appeared in last 8 years. Lexical features were chosen based on gathered statistics and new lexical features were suggested. Chosen lexical features were used to generate a decision tree by means of C4.5 algorithm. Experimental results show, that suggested new lexical features improve detection accuracy. 93.7% detection accuracy was achieved. Detection algorithm based on the generated decision tree can be used for fast real-time detection of botnet domain names.
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Kara, Abdullah Mert. "Malicious Payload Distribution Channels in Domain Name System." Thesis, 2013. http://spectrum.library.concordia.ca/978079/1/Kara_MASc_S2014.pdf.

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Botmasters are known to use different protocols to hide their activities under the radar. Throughout the past years, several protocols have been abused and recently Domain Name System (DNS) also became a target of such malicious activities. In this dissertation, we analyze the use of DNS as a malicious payload distribution channel. To the best of our knowledge, this is the first comprehensive analysis of these payload distribution channels via DNS. We present a system to characterize such channels in the passive DNS (pDNS) traffic by modelling DNS query and response patterns. Then, we analyze the Resource Record (RR) activities of these channels to build their DNS zone profiles. Finally, we detect and assign levels of intensity for payload distribution channels by using a fuzzy logic theory. Our work is based on an extensive analysis of malware datasets for one year, and a near real-time feed of pDNS traffic. The experimental results reveal few long-running hidden domains used by Morto worm to distribute malicious payloads. We also found that some of these payloads are in cleartext, without any encoding or encryption. Our experiments on pDNS traffic indicate that our system can detect these channels regardless of the payload format. Passive DNS is a useful data source for DNS based research, and it requires to be stored in a database for historical data analysis, such as the work we present in this dissertation. Once this database is established, it can be used for any sort of threat analysis that requires DNS oriented intelligence. Our aim is to create a scalable pDNS database, that contains potentially valuable security intelligence data. We present our pDNS database by discussing the database design, implementation challenges, and the evaluation of the system.
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LIN, HAO-HSIANG, and 林皓翔. "Analyzing Domain Name System Log Data to Detect Suspicious Malicious Websites." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yya8v7.

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碩士<br>國立高雄大學<br>資訊管理學系碩士班<br>106<br>The growth of Internet technology brings a lot of convenience to people. There is an increasing number of people are becoming dependent on Internet. Browsing and getting information from Internet plays an important role in many people’s lives. Due to the Internet’s construction is getting more and more complicated, people are exposed to many security threats. Many victim hosts are infected by malware when users are surfing the web. In order to keep computers safe and secure on the Internet, finding a way to detect and identify the potential malware websites is necessary. In this paper, we propose an approach that uses the DNS query log data and implements the sequential pattern mining method to analyze any suspicious malware websites, suspicious intruded websites and infected victim hosts by import an exist malware domain name list. This approach could detect and identify the malware websites and suspicious intruded websites which are hidden by using the result of sequential pattern mining. By using the result of this research, the local area network administrator could recognize that how safe the users surfing on the local area network.
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Book chapters on the topic "Malicious domain names"

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Zhang, Ying, Yongzheng Zhang, and Jun Xiao. "Detecting the DGA-Based Malicious Domain Names." In Trustworthy Computing and Services. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43908-1_17.

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Rajalakshmi, R., S. Ramraj, and R. Ramesh Kannan. "Transfer Learning Approach for Identification of Malicious Domain Names." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5826-5_51.

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Lasota, Krzysztof, and Adam Kozakiewicz. "Analysis of the Similarities in Malicious DNS Domain Names." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22365-5_1.

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Zeng, Yuwei, Xunxun Chen, Tianning Zang, and Haiwei Tsang. "Winding Path: Characterizing the Malicious Redirection in Squatting Domain Names." In Passive and Active Measurement. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72582-2_6.

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Ghalati, Nastaran Farhadi, Nahid Farhady Ghalaty, and José Barata. "Towards the Detection of Malicious URL and Domain Names Using Machine Learning." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45124-0_10.

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Aarthi, B., N. Jeenath Shafana, Judy Flavia, and Balika J. Chelliah. "A Hybrid Multiclass Classifier Approach for the Detection of Malicious Domain Names Using RNN Model." In Computational Vision and Bio-Inspired Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9573-5_35.

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Haddadi, Fariba, H. Gunes Kayacik, A. Nur Zincir-Heywood, and Malcolm I. Heywood. "Malicious Automatically Generated Domain Name Detection Using Stateful-SBB." In Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37192-9_53.

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Yan, Xiaodan, Baojiang Cui, and Jianbin Li. "Malicious Domain Name Recognition Based on Deep Neural Networks." In Security, Privacy, and Anonymity in Computation, Communication, and Storage. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05345-1_43.

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Ke, Wuping, Desheng Zheng, Cong Zhang, Biying Deng, Hui Yao, and Lulu Tian. "CGFMD: CNN and GRU Based Framework for Malicious Domain Name Detection." In Advances in Artificial Intelligence and Security. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06767-9_47.

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Xiong, Cuiwen, Pengxiao Li, Peng Zhang, Qingyun Liu, and Jianlong Tan. "MIRD: Trigram-Based Malicious URL Detection Implanted with Random Domain Name Recognition." In Applications and Techniques in Information Security. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48683-2_27.

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Conference papers on the topic "Malicious domain names"

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Hu, Hongwei, Tingyu Yang, and Haoyue Sun. "Malicious domain name detection based on deep learning and software-defined networks." In Second International Conference on Big Data, Computational Intelligence and Applications (BDCIA 2024), edited by Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3059691.

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Çolhak, Furkan, Mert İlhan Ecevit, Hasan Dağ, and Reiner Creutzburg. "Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations." In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2024. http://dx.doi.org/10.1109/coins61597.2024.10622643.

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Çolhak, Furkan, Mert İlhan Ecevit, Hasan Dağ, and Reiner Creutzburg. "SecureReg: Combining NLP and MLP for Enhanced Detection of Malicious Domain Name Registrations." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698551.

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Yadav, Sandeep, Ashwath Kumar Krishna Reddy, A. L. Narasimha Reddy, and Supranamaya Ranjan. "Detecting algorithmically generated malicious domain names." In the 10th annual conference. ACM Press, 2010. http://dx.doi.org/10.1145/1879141.1879148.

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Kidmose, Egon, Erwin Lansing, Soren Brandbyge, and Jens Myrup Pedersen. "Detection of Malicious and Abusive Domain Names." In 2018 1st International Conference on Data Intelligence and Security (ICDIS). IEEE, 2018. http://dx.doi.org/10.1109/icdis.2018.00015.

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Yoshida, Kenichi, Kazunori Fujiwara, Akira Sato, and Shuji Sannomiya. "Cardinality Analysis to Classify Malicious Domain Names." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.0-161.

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Chiba, Daiki, Mitsuaki Akiyama, Takeshi Yagi, Takeshi Yada, Tatsuya Mori, and Shigeki Goto. "DomainChroma: Providing Optimal Countermeasures against Malicious Domain Names." In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2017. http://dx.doi.org/10.1109/compsac.2017.112.

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Lv, Pin, Lingling Bai, Tingwen Liu, Zhenhu Ning, Jinqiao Shi, and Binxing Fang. "Detection of Malicious Domain Names Based on Hidden Markov Model." In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). IEEE, 2018. http://dx.doi.org/10.1109/dsc.2018.00105.

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Luo, Suliang, Gang Han, An Li, and Jialiang Peng. "Detecting malicious domain names from domain generation algorithms using bi-directional LSTM network." In International Conference on Signal Processing and Communication Security (ICSPCS 2022), edited by Min Xiao and Lisu Yu. SPIE, 2022. http://dx.doi.org/10.1117/12.2655178.

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Magalhaes, Fernanda, and Joao Paulo Magalhaes. "Adopting Machine Learning to Support the Detection of Malicious Domain Names." In 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). IEEE, 2020. http://dx.doi.org/10.1109/iotsms52051.2020.9340159.

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Reports on the topic "Malicious domain names"

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Spring, Jonathan M. Modeling Malicious Domain Name Take-down Dynamics: Why eCrime Pays. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609796.

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