Relevant bibliographies by topics / Low interaction honeypots

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

Academic literature on the topic 'Low interaction honeypots'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 January 2023

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Journal articles on the topic "Low interaction honeypots"

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Touch, Sereysethy, and Jean-Noël Colin. "A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots." Applied Sciences 12, no. 10 (May 21, 2022): 5224. http://dx.doi.org/10.3390/app12105224.

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To proactively defend computer systems against cyber-attacks, a honeypot system—purposely designed to be prone to attacks—is commonly used to detect attacks, discover new vulnerabilities, exploits or malware before they actually do real damage to real systems. Its usefulness lies in being able to operate without being identified as a trap by adversaries; otherwise, its values are significantly reduced. A honeypot is commonly classified by the degree of interactions that they provide to the attacker: low, medium and high-interaction honeypots. However, these systems have some shortcomings of their own. First, the low and medium-interaction honeypots can be easily detected due to their limited and simulated functions of a system. Second, the usage of real systems in high-interaction honeypots has a high risk of security being compromised due to its unlimited functions. To address these problems, we developed Asgard an adaptive self-guarded honeypot, which leverages reinforcement learning to learn and record attacker’s tools and behaviour while protecting itself from being deeply compromised. In this paper, we compare Asgard and its variant Midgard with two conventional SSH honeypots: Cowrie and a real Linux system. The goal of the paper is (1) to demonstrate the effectiveness of the adaptive honeypot that can learn to compromise between collecting attack data and keeping the honeypot safe, and (2) the benefit of coupling of the environment state and the action in reinforcement learning to define the reward function to effectively learn its objectives. The experimental results show that Asgard could collect higher-quality attacker data compared to Cowrie while evading the detection and could also protect the system for as long as it can through blocking or substituting the malicious programs and some other commands, which is the major problem of the high-interaction honeypot.
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Portokalidis, Georgios, and Herbert Bos. "SweetBait: Zero-hour worm detection and containment using low- and high-interaction honeypots." Computer Networks 51, no. 5 (April 2007): 1256–74. http://dx.doi.org/10.1016/j.comnet.2006.09.005.

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Arkaan, Naufal, and Dolly Virgian Shaka Yudha Sakti. "Implementasi Low Interaction Honeypot Untuk Analisa Serangan Pada Protokol SSH." Jurnal Nasional Teknologi dan Sistem Informasi 5, no. 2 (September 19, 2019): 112. http://dx.doi.org/10.25077/teknosi.v5i2.2019.112.

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Faktor keamanan pada teknologi informasi saat ini sangatlah penting, dikarenakan pada zaman yang semakin berkembang data merupakan segalanya. Ancaman serangan terhadap jaringan dan server juga ikut berkembang, maka diperlukan adanya sebuah penanganan terhadap ancaman yang dapat memantau dan menganalisis ancaman serangan yang sedang berlangsung tanpa menyentuh dan merusak server. Honeypot merupakan salah satu solusi yang dapat diberikan karena merupakan sebuah sistem umpan atau aplikasi simulasi yang dapat digunakan untuk memikat penyerang dengan menyamarkan diri sebagai sistem yang rentan. Honeypot dapat digunakan untuk memantau dan menganalisis kegiatan penyerang yang tertangkap di honeypot. Honeypot ini berjenis low interaction yang dibuat menggunakan bahasa pemrograman python yang memanfaatkan konsep network programming. Aplikasi honeypot berjalan di server nantinya menyembunyikan service port pada protokol SSH asli yang biasa diakses dan diserang oleh penyerang dan juga membuat service protokol SSH palsu yang dapat menipu, menganalisis, dan memantau penyerang yang mengancam pada server. Tujuan dari penelitian ini adalah untuk menganalisis perilaku apa yang dilakukan penyerang di dalam server dan juga kemungkinan username dan password yang digunakan oleh penyerang, dengan begitu hasil dari serangan sebagai pembelajaran agar server lebih aman.
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Arkaan, Naufal, and Dolly Virgian Shaka Yudha Sakti. "Implementasi Low Interaction Honeypot Untuk Analisa Serangan Pada Protokol SSH." Jurnal Nasional Teknologi dan Sistem Informasi 5, no. 2 (September 19, 2019): 112–20. http://dx.doi.org/10.25077/teknosi.v5i2.2019.112-120.

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Faktor keamanan pada teknologi informasi saat ini sangatlah penting, dikarenakan pada zaman yang semakin berkembang data merupakan segalanya. Ancaman serangan terhadap jaringan dan server juga ikut berkembang, maka diperlukan adanya sebuah penanganan terhadap ancaman yang dapat memantau dan menganalisis ancaman serangan yang sedang berlangsung tanpa menyentuh dan merusak server. Honeypot merupakan salah satu solusi yang dapat diberikan karena merupakan sebuah sistem umpan atau aplikasi simulasi yang dapat digunakan untuk memikat penyerang dengan menyamarkan diri sebagai sistem yang rentan. Honeypot dapat digunakan untuk memantau dan menganalisis kegiatan penyerang yang tertangkap di honeypot. Honeypot ini berjenis low interaction yang dibuat menggunakan bahasa pemrograman python yang memanfaatkan konsep network programming. Aplikasi honeypot berjalan di server nantinya menyembunyikan service port pada protokol SSH asli yang biasa diakses dan diserang oleh penyerang dan juga membuat service protokol SSH palsu yang dapat menipu, menganalisis, dan memantau penyerang yang mengancam pada server. Tujuan dari penelitian ini adalah untuk menganalisis perilaku apa yang dilakukan penyerang di dalam server dan juga kemungkinan username dan password yang digunakan oleh penyerang, dengan begitu hasil dari serangan sebagai pembelajaran agar server lebih aman.
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Rizvi, Syed Khurram Jah, Warda Aslam, Muhammad Shahzad, Shahzad Saleem, and Muhammad Moazam Fraz. "PROUD-MAL: static analysis-based progressive framework for deep unsupervised malware classification of windows portable executable." Complex & Intelligent Systems 8, no. 1 (October 12, 2021): 673–85. http://dx.doi.org/10.1007/s40747-021-00560-1.

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AbstractEnterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet, instant detection using static analysis has become very difficult due to the exponential rise in volume and variety of malware. The compelling need of early stage detection of malware-based attacks significantly motivates research inclination towards automated malware detection. The recent machine learning aided malware detection approaches using static analysis are mostly supervised. Supervised malware detection using static analysis requires manual labelling and human feedback; therefore, it is less effective in rapidly evolutionary and dynamic threat space. To this end, we propose a progressive deep unsupervised framework with feature attention block for static analysis-based malware detection (PROUD-MAL). The framework is based on cascading blocks of unsupervised clustering and features attention-based deep neural network. The proposed deep neural network embedded with feature attention block is trained on the pseudo labels. To evaluate the proposed unsupervised framework, we collected a real-time malware dataset by deploying low and high interaction honeypots on an enterprise organizational network. Moreover, endpoint security solution is also deployed on an enterprise organizational network to collect malware samples. After post processing and cleaning, the novel dataset consists of 15,457 PE samples comprising 8775 malicious and 6681 benign ones. The proposed PROUD-MAL framework achieved an accuracy of more than 98.09% with better quantitative performance in standard evaluation parameters on collected dataset and outperformed other conventional machine learning algorithms. The implementation and dataset are available at https://bit.ly/35Sne3a.
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NURILAHI, DESI KURNIA, RIZAL MUNADI, SYAHRIAL SYAHRIAL, and AL BAHRI. "Penerapan Metode Naïve Bayes pada Honeypot Dionaea dalam Mendeteksi Serangan Port Scanning." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, no. 2 (April 12, 2022): 309. http://dx.doi.org/10.26760/elkomika.v10i2.309.

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ABSTRAKPeningkatan serangan terhadap jaringan komputer terus terjadi setiap tahunnya dan dampaknya membuat layanan menjadi terganggu. Pada Penelitian ini Dionaea Honeypot yang merupakan jenis Low Interaction Honeypot, diterapkan untuk mengevaluasi serangan yang terjadi berdasarkan teknik serangan Port Scanning. Data Log yang diperoleh dari pengujian, dianalisis dengan metode Naïve Bayes. Lebih lanjut, data pemetaan Port Scanning dengan menggunakan perangkat lunak Nmap, ditemukan port yang terbuka sebanyak 359 data. Hasil uji klasifikasi dengan menggunakan perangkat lunak WEKA dan penerapan metode Naïve Bayes. Hasil uji klasifikasi diperoleh nilai akurasi sebesar 86,2% dengan nilai rata-rata Precision sebesar 0,885%, Recall sebesar 0,862% dan F-measure sebesar 0,849%. Hasil ini menunjukkan penerapan metode Naïve Bayes berhasil mengklasifikasikan potensi serangan yang dilakukan berdasarkan teknik Port Scanning.Kata kunci: Jaringan Komputer, Low Interaction Honeypot, Port Scanning, Uji Klasifikasi, Akurasi ABSTRACTIncreasing attacks on computer networks continue to occur every year, and the impact makes services disrupted. In this study, Dionaea Honeypot, a type of Low Interaction Honeypot, is applied to evaluate attacks based on the Port Scanning attack technique. Log data obtained from the test were analyzed using the Naïve Bayes method. Furthermore, Port Scanning mapping data using Nmap software on the network found 359 open ports data. The results of the classification test using WEKA software and the application of the Naïve Bayes method. The classification test results obtained are accuracy value, 86.2% with an average value of 0.885% Precision, 0.862% Recall and 0.849% F-measure. This result shows that the application of the Naïve Bayes method has succeeded in classifying potential attacks based on the Port Scanning technique.Keywords: Computer Network, Low Interaction Honeypot, Port Scanning, Classification Test, Accuracy
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Marzuqon, Mokhamad Wildan, and Agus Prihanto. "Analisis Perbandingan Behavior User Menggunakan Low Interaction Honeypot dan IDS pada Sistem Edge Computing." Journal of Informatics and Computer Science (JINACS) 3, no. 04 (June 29, 2022): 471–80. http://dx.doi.org/10.26740/jinacs.v3n04.p471-480.

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Saat ini perkembangan Edge computing semakin pesat, perkembangan ini juga disertai dengan ancaman yang begitu besar. Egde server merupakan sistem yang rentan terkena serangan. Serangan tersebut dapat berupa serangan DoS, Port Scanning, Web Service Intrusion, dan lain sebagainya. Maka dari itu diperlukan upaya pencegahan untuk meminimalisir risiko yang diakibatkan oleh serangan tersebut dengan cara menganalisis aktivitas user saat mengakses edge server. Penelitian ini bertujuan untuk mengetahui dan menganalisis aktivitas user saat mengakses edge server menggunakan Low Interaction Honeypot dan IDS. Pengujian yang dilakukan yaitu dengan dua skenario yaitu saat honeypot dinyalakan dan dimatikan. Hasil pengujian menunjukkan pada skenario honeypot dinyalakan, beban edge server menjadi berat, ditunjukkan dengan rata rata latensi sebesar 0,0085s. Selain itu, port layanan server yang terbuka juga lebih banyak sehingga meningkatkan peluang intruder untuk melakukan penyerangan terhadap edge server. Sedangkan pengujian dengan skenario honeypot dimatikan, beban edge server menjadi berkurang, hal ini ditunjukkan dengan rata rata latensi sebesar 0,0055s. Selain itu port layanan server yang terbuka hanya layanan yang berasal dari Windows dan XAMPP, sehingga aktivitas intruder yang dilakukan menjadi terbatas. Pengujian tersebut menunjukkan semakin banyak port dan layanan server yang terbuka, semakin tinggi risiko penyerangannya, dan dengan adanya honeypot risiko tersebut dapat dikurangi dengan menganalisis aktivitas intruder dengan menentukan rules yang tepat untuk pencegahan serangan.
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Durairajan, M. S., R. Saravanan, and S. Sibi Chakkaravarthy. "Low Interaction Honeypot: A Defense Against Cyber Attacks." Journal of Computational and Theoretical Nanoscience 13, no. 8 (August 1, 2016): 5446–53. http://dx.doi.org/10.1166/jctn.2016.5437.

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Nyamugudza, Tendai, Venkatesh Rajasekar, Prasad Sen, M. Nirmala, and V. Madhu Viswanatham. "Network traffic intelligence using a low interaction honeypot." IOP Conference Series: Materials Science and Engineering 263 (November 2017): 042096. http://dx.doi.org/10.1088/1757-899x/263/4/042096.

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Mispriatin, Melia, Jaffaruddin Gusti Amri Ginting, and Bongga Arifwidodo. "Analisis Kinerja Honeypot Dionaea Dan Cowrie Dalam Mendeteksi Serangan." Prosiding Seminar Nasional Teknoka 6 (January 1, 2022): 170–78. http://dx.doi.org/10.22236/teknoka.v6i1.448.

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Pada era digital, kerentanan sistem menjadi poin utama untuk masuknya dari berbagai serangan yang dapat membuat sistem dikendalikan pihak asing bahkan lumpuh. Maka, diperlukan penanganan untuk masalah tersebut. Honeypot merupakan sistem keamanan untuk memantau dan menganalisis setiap aktivitas serangan yang masuk ke sistem. Low interaction honeypot (dionaea) dan medium interaction honeypot (cowrie) dipilih untuk mengamankan server. Serangan yang dipilih adalah Port scanning, Bruteforce SSH dan DoS dengan 2 skenario yaitu tanpa honeypot dan dengan honeypot. Hasil penelitian menunjukan Dionaea dapat menangkap lebih banyak serangan dibanding Cowrie. Sedangkan dari segi interaksi antara server dengan penyerang, Cowrie lebih unggul. Hasil QoS serangan DoS, baik sebelum maupun setelah terpasang honeypot nilai throughput, packet loss dan delay masih dalam kategori sangat bagus dengan index 4 sedangkan nilai jitter setelah terpasang honeypot naik dari index 2 menjadi 3, kategori sedang menjadi baik. Sedangkan penggunaan CPU naik 6,66% menjadi 49% dan Memory naik 12,48% menjadi 37,6%.
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Dissertations / Theses on the topic "Low interaction honeypots"

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Chairetakis, Eleftherios, Bassam Alkudhir, and Panagiotis Mystridis. "Deployment of Low Interaction Honeypots in University Campus Network." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-22141.

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Large scale networks face daily thousands of network attacks. No matter the strength of the existing security defending mechanisms, these networks remain vulnerable, as new tools and techniques are being constantly developed by hackers. A new promising technology that lures the attackers in order to monitor their malicious activities and divulge their intentions is emerging with Virtual Honeypots. In the present thesis, we examine an extensive security mechanism based on three different open source low interaction honeypots. We implement this mechanism at our university campus network in an attempt to identify the potential threats and methods used against our network. The data gathered by our honeypots reveal valuable information regarding the types of attacks, the vulnerable network services within the network and the malicious activities launched by attackers.
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Ponten, Austin. "Evaluation of Low-Interaction Honeypots on the University Network." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-66885.

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This project studies the three honeypot solutions Honeyd,Dionaea, and Kippo. Eval-uating the solutions themselves, and observing their implementation into the university campus network. The investigation begins with the understanding of how a honeypot works and is useful as an extra security layer, following with an implementation of said three honeypot solutions and the results that follow after a period of days. After the data has been collected, it shows that the majority of malicious activity surrounded communication services, and an evaluation of the three honeypot solutions showed Honeyd as the best with its scalability and reconfigurability.
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Almotairi, Saleh Ibrahim Bakr. "Using honeypots to analyse anomalous Internet activities." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/31833/1/Saleh_Almotairi_Thesis.pdf.

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Monitoring Internet traffic is critical in order to acquire a good understanding of threats to computer and network security and in designing efficient computer security systems. Researchers and network administrators have applied several approaches to monitoring traffic for malicious content. These techniques include monitoring network components, aggregating IDS alerts, and monitoring unused IP address spaces. Another method for monitoring and analyzing malicious traffic, which has been widely tried and accepted, is the use of honeypots. Honeypots are very valuable security resources for gathering artefacts associated with a variety of Internet attack activities. As honeypots run no production services, any contact with them is considered potentially malicious or suspicious by definition. This unique characteristic of the honeypot reduces the amount of collected traffic and makes it a more valuable source of information than other existing techniques. Currently, there is insufficient research in the honeypot data analysis field. To date, most of the work on honeypots has been devoted to the design of new honeypots or optimizing the current ones. Approaches for analyzing data collected from honeypots, especially low-interaction honeypots, are presently immature, while analysis techniques are manual and focus mainly on identifying existing attacks. This research addresses the need for developing more advanced techniques for analyzing Internet traffic data collected from low-interaction honeypots. We believe that characterizing honeypot traffic will improve the security of networks and, if the honeypot data is handled in time, give early signs of new vulnerabilities or breakouts of new automated malicious codes, such as worms. The outcomes of this research include: • Identification of repeated use of attack tools and attack processes through grouping activities that exhibit similar packet inter-arrival time distributions using the cliquing algorithm; • Application of principal component analysis to detect the structure of attackers’ activities present in low-interaction honeypots and to visualize attackers’ behaviors; • Detection of new attacks in low-interaction honeypot traffic through the use of the principal component’s residual space and the square prediction error statistic; • Real-time detection of new attacks using recursive principal component analysis; • A proof of concept implementation for honeypot traffic analysis and real time monitoring.
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Frederick, Erwin E. "Testing a low-interaction honeypot against live cyber attackers." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5600.

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Approved for public release; distribution is unlimited.
The development of honeypots as decoys designed to detect, investigate, and counterattack unauthorized use of information systems has produced an "arms race" between honeypots (computers designed solely to receive cyber attacks) and anti-honeypot technology. To test the current state of this race, we performed experiments in which we ran a small group of honeypots, using the low-interaction honeypot software Honeyd, on a network outside campus firewall protection. For 15 weeks, we ran different configurations of ports and service scripts, and simulated operating systems to check which configurations were most useful as a research honeypot and which were most useful as decoys to protect other network users. We analyzed results in order to improve the results for both purposes in subsequent weeks. We did find promising configurations for both purposes; however, good configurations for one purpose were not necessarily good for the other. We also tested the limits of Honeyd software and identified aspects of it that need to be improved. We also identified the most common attacks, most common ports used by attackers, and degree of success of decoy service scripts.
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Book chapters on the topic "Low interaction honeypots"

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Vidal-González, Sergio, Isaías García-Rodríguez, Héctor Aláiz-Moretón, Carmen Benavides-Cuéllar, José Alberto Benítez-Andrades, María Teresa García-Ordás, and Paulo Novais. "Analyzing IoT-Based Botnet Malware Activity with Distributed Low Interaction Honeypots." In Trends and Innovations in Information Systems and Technologies, 329–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45691-7_30.

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Sokol, Pavol, Matej Zuzčák, and Tomáš Sochor. "Definition of Attack in the Context of Low-Level Interaction Server Honeypots." In Computer Science and its Applications, 499–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45402-2_74.

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Zakari, Abubakar, Abdulmalik Ahmad Lawan, and Girish Bekaroo. "Towards Improving the Security of Low-Interaction Honeypots: Insights from a Comparative Analysis." In Emerging Trends in Electrical, Electronic and Communications Engineering, 314–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52171-8_28.

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Shi, Jing, Mingyang Chen, and Jiazheng Jiao. "Thoughts on the Application of Low-Interactive Honeypot Based on Raspberry Pi in Public Security Actual Combat, LIHRP." In Lecture Notes in Computer Science, 144–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06791-4_12.

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"Distributed Low-Interaction Honeypot System to Detect Botnets." In International Conference on Computer Engineering and Technology, 3rd (ICCET 2011), 413–20. ASME Press, 2011. http://dx.doi.org/10.1115/1.859735.paper66.

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Jorquera Valero, José María, Manuel Gil Pérez, Alberto Huertas Celdrán, and Gregorio Martínez Pérez. "Identification and Classification of Cyber Threats Through SSH Honeypot Systems." In Handbook of Research on Intrusion Detection Systems, 105–29. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch006.

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As the number and sophistication of cyber threats increases year after year, security systems such as antivirus, firewalls, or Intrusion Detection Systems based on misuse detection techniques are improved in detection capabilities. However, these traditional systems are usually limited to detect potential threats, since they are inadequate to spot zero-day attacks or mutations in behaviour. Authors propose using honeypot systems as a further security layer able to provide an intelligence holistic level in detecting unknown threats, or well-known attacks with new behaviour patterns. Since brute-force attacks are increasing in recent years, authors opted for an SSH medium-interaction honeypot to acquire a log set from attacker's interactions. The proposed system is able to acquire behaviour patterns of each attacker and link them with future sessions for early detection. Authors also generate a feature set to feed Machine Learning algorithms with the main goal of identifying and classifying attacker's sessions, and thus be able to learn malicious intentions in executing cyber threats.
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Conference papers on the topic "Low interaction honeypots"

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Badrul Anuar, Nor, Omar Zakaria, and Chong Wei Yao. "Honeypot through Web (Honeyd@WEB): The Emerging of Security Application Integration." In InSITE 2006: Informing Science + IT Education Conference. Informing Science Institute, 2006. http://dx.doi.org/10.28945/2955.

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This paper discusses on the development of the Honeyd@WEB. Honeyd@WEB is a system that can deploy low-interaction, production, dynamic and manageable virtual honeypots via a web interface. It runs open source programs, such as P0f (a passive fingerprinting tool) and Honeyd (a low-interaction honeypot). Honeyd@WEB can automatically determine; how many honeypots to deploy, how to deploy them, and what they should look like to blend in with the environment. The first part of this paper highlights the basic security concepts of honeypot and honeynet. The second part of this paper explains the Honeyd@WEB system. Finally, the strengths and the weaknesses of the system are discussed.
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Mukkamala, S., K. Yendrapalli, R. Basnet, M. K. Shankarapani, and A. H. Sung. "Detection of Virtual Environments and Low Interaction Honeypots." In 2007 IEEE SMC Information Assurance and Security Workshop. IEEE, 2007. http://dx.doi.org/10.1109/iaw.2007.381919.

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Musch, Marius, Martin Härterich, and Martin Johns. "Towards an Automatic Generation of Low-Interaction Web Application Honeypots." In ARES 2018: International Conference on Availability, Reliability and Security. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3230833.3230839.

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Naik, Nitin, Paul Jenkins, Roger Cooke, and Longzhi Yang. "Honeypots That Bite Back: A Fuzzy Technique for Identifying and Inhibiting Fingerprinting Attacks on Low Interaction Honeypots." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491456.

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Fronimos, D., E. Magkos, and V. Chrissikopoulos. "Evaluating Low Interaction Honeypots and On their Use against Advanced Persistent Threats." In the 18th Panhellenic Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2645791.2645850.

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Andrew, Daniel, and Hongmei Chi. "An empirical study of botnets on university networks using low-interaction honeypots." In the 51st ACM Southeast Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2498328.2500094.

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Ferretti, Pietro, Marcello Pogliani, and Stefano Zanero. "Characterizing Background Noise in ICS Traffic Through a Set of Low Interaction Honeypots." In the ACM Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3338499.3357361.

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Akiyoshi, Ryoh, Daisuke Kotani, and Yasuo Okabe. "Detecting Emerging Large-Scale Vulnerability Scanning Activities by Correlating Low-Interaction Honeypots with Darknet." In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2018. http://dx.doi.org/10.1109/compsac.2018.10314.

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Naik, Nitin, and Paul Jenkins. "A Fuzzy Approach for Detecting and Defending Against Spoofing Attacks on Low Interaction Honeypots." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455555.

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"HoneydV6: A Low-interaction IPv6 Honeypot." In International Conference on Security and Cryptography. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004515100860097.

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