Arash Bozorgchenani, Charilaos C. Zarakovitis, S. Chien, Heng-Siong Lim, Q. Ni, Antonios Gouglidis, Wissam Mallouli
The advent of 5G technology introduces new - and potentially undiscovered - cybersecurity challenges, with unforeseen impacts on our economy, society, and environment. Interestingly, Intrusion Detection Mechanisms (IDMs) can provide the necessary network monitoring to ensure - to a big extent - the detection of 5G-related cyberattacks. Yet, how to realize the attack surface of 5G networks with respect to the detected risks, and, consequently, how to optimize the cybersecurity levels of the network, remains an open critical challenge. In respect, this work focuses on deploying multiple distributed Security Agents (SAs) that can run different IDMs over various network components and proposes a cybersecurity mechanism for optimizing the network’s attack surface with respect to the Quality of Service (QoS). The proposed approach relies on a new closed-form utility function to describe the trade-off between cybersecurity and QoS and uses multi-objective optimization to improve the selection of each SA detection level. We demonstrate via simulations that before optimization, an increase in the detection level of SAs brings a direct decrease in QoS as more computational, bandwidth and monetary resources are utilized for IDM processing. Thereby, after optimization, we demonstrate that our mechanism can strike a balance between cybersecurity and QoS while showcasing the impact of the importance of different objectives of the joint optimization.
{"title":"Joint Security-vs-QoS Framework: Optimizing the Selection of Intrusion Detection Mechanisms in 5G networks","authors":"Arash Bozorgchenani, Charilaos C. Zarakovitis, S. Chien, Heng-Siong Lim, Q. Ni, Antonios Gouglidis, Wissam Mallouli","doi":"10.1145/3538969.3544480","DOIUrl":"https://doi.org/10.1145/3538969.3544480","url":null,"abstract":"The advent of 5G technology introduces new - and potentially undiscovered - cybersecurity challenges, with unforeseen impacts on our economy, society, and environment. Interestingly, Intrusion Detection Mechanisms (IDMs) can provide the necessary network monitoring to ensure - to a big extent - the detection of 5G-related cyberattacks. Yet, how to realize the attack surface of 5G networks with respect to the detected risks, and, consequently, how to optimize the cybersecurity levels of the network, remains an open critical challenge. In respect, this work focuses on deploying multiple distributed Security Agents (SAs) that can run different IDMs over various network components and proposes a cybersecurity mechanism for optimizing the network’s attack surface with respect to the Quality of Service (QoS). The proposed approach relies on a new closed-form utility function to describe the trade-off between cybersecurity and QoS and uses multi-objective optimization to improve the selection of each SA detection level. We demonstrate via simulations that before optimization, an increase in the detection level of SAs brings a direct decrease in QoS as more computational, bandwidth and monetary resources are utilized for IDM processing. Thereby, after optimization, we demonstrate that our mechanism can strike a balance between cybersecurity and QoS while showcasing the impact of the importance of different objectives of the joint optimization.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124693534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian Oliva, Ignacio Palacio Marín, L. F. Lago-Fernández, David Arroyo
In this article we address the challenge of detecting the generation and spreading of misleading information in the specific scenario of clickbait. Our contribution consists of a methodology that combines a deep neural network and an information divergence measure to overcome the limitations of deep learning techniques in this scenario. This analysis is conducted by considering a clickbait challenge dataset. We realise that the construction of the dataset used to study this kind of problems dramatically affects the performance of the model and, thus, its selection. Since clickbait is a result of the inconsistency between headlines and content, we integrate a divergence measure as a layer of a deep learning model. The resulting model overcomes the limitations of conventional machine learning and deep learning models in clickbait detection.
{"title":"Rumor and clickbait detection by combining information divergence measures and deep learning techniques","authors":"Christian Oliva, Ignacio Palacio Marín, L. F. Lago-Fernández, David Arroyo","doi":"10.1145/3538969.3543791","DOIUrl":"https://doi.org/10.1145/3538969.3543791","url":null,"abstract":"In this article we address the challenge of detecting the generation and spreading of misleading information in the specific scenario of clickbait. Our contribution consists of a methodology that combines a deep neural network and an information divergence measure to overcome the limitations of deep learning techniques in this scenario. This analysis is conducted by considering a clickbait challenge dataset. We realise that the construction of the dataset used to study this kind of problems dramatically affects the performance of the model and, thus, its selection. Since clickbait is a result of the inconsistency between headlines and content, we integrate a divergence measure as a layer of a deep learning model. The resulting model overcomes the limitations of conventional machine learning and deep learning models in clickbait detection.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128605785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increase in the number of Internet users has also led to an increase in activities leading to a cyber threat and fraud intelligence. These activities include the use of the Dark Web for coordination and virtual currencies for funding. This article will present the main methods of cyber-attacks and crimes used nowadays, and how they can be prevented by using tools specialized in monitoring transactions with virtual currencies and detecting web pages that pose a threat to users. The tools that will be described in this article are Graphsense used to analyze virtual currency activities and SpiderFoot used to identify Cyber Threat, Attack Surfaces, Security Assessments and Asset Discovery.
{"title":"Fraudulent Activities in the Cyber Realm: DEFRAUDify Project: Fraudulent Activities in the Cyber Realm: DEFRAUDify Project","authors":"Razvan-Alexandru Bratulescu, Robert-Ionut Vatasoiu, Sorina-Andreea Mitroi, G. Suciu, Mari-Anais Sachian, Daniel-Marian Dutu, Serban-Emanuel Calescu","doi":"10.1145/3538969.3544434","DOIUrl":"https://doi.org/10.1145/3538969.3544434","url":null,"abstract":"The increase in the number of Internet users has also led to an increase in activities leading to a cyber threat and fraud intelligence. These activities include the use of the Dark Web for coordination and virtual currencies for funding. This article will present the main methods of cyber-attacks and crimes used nowadays, and how they can be prevented by using tools specialized in monitoring transactions with virtual currencies and detecting web pages that pose a threat to users. The tools that will be described in this article are Graphsense used to analyze virtual currency activities and SpiderFoot used to identify Cyber Threat, Attack Surfaces, Security Assessments and Asset Discovery.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130843696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Security incidents in blockchain-based systems are frequent nowadays, which calls for more structured efforts in incident reporting and response. To improve the current status quo of reporting incidents on blogs and social media, we propose a decentralized incident reporting and discussion system. Our approach guides users (security novices) towards a classification of their observations using a tiered taxonomy of blockchain incidents. Questions based on previous incidents interactively support the classification. Post submission a security incident response committee then discusses these observations on our decentralized platform to decide on an appropriate response. For evaluation, we implement our model as a decentralized application and demonstrate its practical suitability in a preliminary user study.
{"title":"BISCUIT - Blockchain Security Incident Reporting based on Human Observations","authors":"B. Putz, Manfred Vielberth, G. Pernul","doi":"10.1145/3538969.3538984","DOIUrl":"https://doi.org/10.1145/3538969.3538984","url":null,"abstract":"Security incidents in blockchain-based systems are frequent nowadays, which calls for more structured efforts in incident reporting and response. To improve the current status quo of reporting incidents on blogs and social media, we propose a decentralized incident reporting and discussion system. Our approach guides users (security novices) towards a classification of their observations using a tiered taxonomy of blockchain incidents. Questions based on previous incidents interactively support the classification. Post submission a security incident response committee then discusses these observations on our decentralized platform to decide on an appropriate response. For evaluation, we implement our model as a decentralized application and demonstrate its practical suitability in a preliminary user study.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giovanni Ciaramella, Giacomo Iadarola, F. Mercaldo, Marco Storto, A. Santone, Fabio Martinelli
Mobile malware are increasing their complexity to be able to evade the current detection mechanism by gathering our sensitive and private information. For this reason, an active research field is represented by malware detection, with a great effort in the development of deep learning models starting from a set of malicious and legitimate applications. The recent introduction of quantum computing made possible quantum machine learning i.e., the integration of quantum algorithms within machine learning algorithms. In this paper, we propose a comparison between several deep learning models, by taking into account also a hybrid quantum malware detector. We explore the effectiveness of different architectures for malicious family detection in the Android environment: LeNet, AlexNet, a Convolutional Neural Network model designed by authors, VGG16 and a Hybrid Quantum Convolutional Neural Network i.e., a model where the first layer is a quantum convolution that uses transformations in circuits to simulate the behavior of a quantum computer. Experiments performed on a real-world dataset composed of 8446 Android malicious and legitimate applications allow us to compare the various models, with particular regard to the quantum model concerning the other ones.
{"title":"Introducing Quantum Computing in Mobile Malware Detection","authors":"Giovanni Ciaramella, Giacomo Iadarola, F. Mercaldo, Marco Storto, A. Santone, Fabio Martinelli","doi":"10.1145/3538969.3543816","DOIUrl":"https://doi.org/10.1145/3538969.3543816","url":null,"abstract":"Mobile malware are increasing their complexity to be able to evade the current detection mechanism by gathering our sensitive and private information. For this reason, an active research field is represented by malware detection, with a great effort in the development of deep learning models starting from a set of malicious and legitimate applications. The recent introduction of quantum computing made possible quantum machine learning i.e., the integration of quantum algorithms within machine learning algorithms. In this paper, we propose a comparison between several deep learning models, by taking into account also a hybrid quantum malware detector. We explore the effectiveness of different architectures for malicious family detection in the Android environment: LeNet, AlexNet, a Convolutional Neural Network model designed by authors, VGG16 and a Hybrid Quantum Convolutional Neural Network i.e., a model where the first layer is a quantum convolution that uses transformations in circuits to simulate the behavior of a quantum computer. Experiments performed on a real-world dataset composed of 8446 Android malicious and legitimate applications allow us to compare the various models, with particular regard to the quantum model concerning the other ones.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121501655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgios Agrafiotis, Eftychia Makri, Ioannis Flionis, Antonios Lalas, K. Votis, D. Tzovaras
Traffic categorization is considered of paramount importance in the network security sector, as well as the first stage in network anomaly detection, or in a network-based intrusion detection system (IDS). This paper introduces an artificial intelligence (AI) network traffic classification pipeline, including the employment of state-of-the-art image-based neural network models, namely Vision Transformers (ViT) and Convolutional Neural Networks (CNN), whereas the primary element of this pipeline is the transformation of raw traffic data into grayscale pictures introducing a properly developed IDS-Vision Toolkit as well. This approach extracts characteristics from network traffic data without requiring domain expertise and could be easily adapted to new network protocols and technologies (i.e. 5G). Furthermore, the proposed method was tested on the CIC-IDS-2017 dataset and compared to a well-known feature extraction strategy on the same dataset. Finally, it surpasses all suggested binary classification algorithms for the CIC-IDS-2017 dataset to the best of our knowledge, paving the path for further exploitation in the 5G domain to successfully address related cybersecurity challenges.
{"title":"Image-based Neural Network Models for Malware Traffic Classification using PCAP to Picture Conversion","authors":"Georgios Agrafiotis, Eftychia Makri, Ioannis Flionis, Antonios Lalas, K. Votis, D. Tzovaras","doi":"10.1145/3538969.3544473","DOIUrl":"https://doi.org/10.1145/3538969.3544473","url":null,"abstract":"Traffic categorization is considered of paramount importance in the network security sector, as well as the first stage in network anomaly detection, or in a network-based intrusion detection system (IDS). This paper introduces an artificial intelligence (AI) network traffic classification pipeline, including the employment of state-of-the-art image-based neural network models, namely Vision Transformers (ViT) and Convolutional Neural Networks (CNN), whereas the primary element of this pipeline is the transformation of raw traffic data into grayscale pictures introducing a properly developed IDS-Vision Toolkit as well. This approach extracts characteristics from network traffic data without requiring domain expertise and could be easily adapted to new network protocols and technologies (i.e. 5G). Furthermore, the proposed method was tested on the CIC-IDS-2017 dataset and compared to a well-known feature extraction strategy on the same dataset. Finally, it surpasses all suggested binary classification algorithms for the CIC-IDS-2017 dataset to the best of our knowledge, paving the path for further exploitation in the 5G domain to successfully address related cybersecurity challenges.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131469899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The evolution of Information and Communications Technology and Cloud Computing, combined with the advent of novel telecommunication frameworks such as 5G, have introduced the notion of ubiquitous connectivity combined with a seemingly vast pool of resources, storage and services. This immense transformation introduced new types of security threats mostly due to the significant increase of the attack surface, which can now be compromised by malicious users. Despite the fact that malicious attacks constantly become more and more sophisticated, SMEs and public administrations remain reluctant to invest in cybersecurity since they operate on a limited budget and are mostly focused in time to market and cost minimization. The purpose of this book chapter is to provide an overview on how the most common network-related cybersecurity attacks are orchestrated, which are the systems and services they affect the most as well as present specific design principles and guidelines for crafting platforms and frameworks capable of mitigating such attacks and ensure a certain level of secure operation.
{"title":"Improving Network, Data and Application Security for SMEs","authors":"C. Tselios, Ilias Politis, C.K. Xenakis","doi":"10.1145/3538969.3544426","DOIUrl":"https://doi.org/10.1145/3538969.3544426","url":null,"abstract":"The evolution of Information and Communications Technology and Cloud Computing, combined with the advent of novel telecommunication frameworks such as 5G, have introduced the notion of ubiquitous connectivity combined with a seemingly vast pool of resources, storage and services. This immense transformation introduced new types of security threats mostly due to the significant increase of the attack surface, which can now be compromised by malicious users. Despite the fact that malicious attacks constantly become more and more sophisticated, SMEs and public administrations remain reluctant to invest in cybersecurity since they operate on a limited budget and are mostly focused in time to market and cost minimization. The purpose of this book chapter is to provide an overview on how the most common network-related cybersecurity attacks are orchestrated, which are the systems and services they affect the most as well as present specific design principles and guidelines for crafting platforms and frameworks capable of mitigating such attacks and ensure a certain level of secure operation.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131594601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mandana Falahi, A. Vasilățeanu, N. Goga, G. Suciu, Mari-Anais Sachian, Robert Florescu, Ștefan-Daniel Stanciu
In the current industrial century, smart grid is one of the technologies that has been proposed for efficient and quality distribution of electricity. However, this technology is exposed to many security threats and vulnerabilities. These challenges have led to the development of advanced technologies and sustainable solutions to make smart grids more secure and reliable. Blockchain is one of the recent technologies that has attracted a lot of attention in various applications, including smart grids. SealedGRID is a project designed, analyzed and implemented with the aim of providing a scalable and reliable Smart Grid security platform based on blockchain. In this paper, we present a scalable and secure solution for smart grids using Hyperledger Fabric and MQTT.
{"title":"Improving Security and Scalability in Smart Grids using Blockchain Technologies","authors":"Mandana Falahi, A. Vasilățeanu, N. Goga, G. Suciu, Mari-Anais Sachian, Robert Florescu, Ștefan-Daniel Stanciu","doi":"10.1145/3538969.3544441","DOIUrl":"https://doi.org/10.1145/3538969.3544441","url":null,"abstract":"In the current industrial century, smart grid is one of the technologies that has been proposed for efficient and quality distribution of electricity. However, this technology is exposed to many security threats and vulnerabilities. These challenges have led to the development of advanced technologies and sustainable solutions to make smart grids more secure and reliable. Blockchain is one of the recent technologies that has attracted a lot of attention in various applications, including smart grids. SealedGRID is a project designed, analyzed and implemented with the aim of providing a scalable and reliable Smart Grid security platform based on blockchain. In this paper, we present a scalable and secure solution for smart grids using Hyperledger Fabric and MQTT.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132800298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aleksandra Pawlicka, M. Pawlicki, R. Renk, R. Kozik, M. Choraś
Nowadays, cloud technology is assuming immense significance, being treated as a critical infrastructure, and is also a buzzword. Nevertheless, the technology has also brought about a number of new adverse phenomena and threats; it has attracted criminals, as well. Whenever the questions of “good” and “bad” arise, the ethical issues arise alongside them; the cybersecurity of cloud technology is no exception. This paper deals with the ethical dilemmas of cloud technology. It discusses a collection of the ethical issues of the cloud technology presented from the perspective of cybersecurity, based on the state-of-the-art literature. The main contribution of this work is that it gathers, synthesizes and organises the cybersecurity-related ethical dilemmas of cloud technology, thus offering the most extensive collection thereof. In addition, the work presents a comprehensive list of recommendations and suggestions which may help solve or prevent these ethical issues, and are a good starting point for anyone designing an ethical cybersecurity strategy.
{"title":"The cybersecurity-related ethical issues of cloud technology and how to avoid them","authors":"Aleksandra Pawlicka, M. Pawlicki, R. Renk, R. Kozik, M. Choraś","doi":"10.1145/3538969.3544456","DOIUrl":"https://doi.org/10.1145/3538969.3544456","url":null,"abstract":"Nowadays, cloud technology is assuming immense significance, being treated as a critical infrastructure, and is also a buzzword. Nevertheless, the technology has also brought about a number of new adverse phenomena and threats; it has attracted criminals, as well. Whenever the questions of “good” and “bad” arise, the ethical issues arise alongside them; the cybersecurity of cloud technology is no exception. This paper deals with the ethical dilemmas of cloud technology. It discusses a collection of the ethical issues of the cloud technology presented from the perspective of cybersecurity, based on the state-of-the-art literature. The main contribution of this work is that it gathers, synthesizes and organises the cybersecurity-related ethical dilemmas of cloud technology, thus offering the most extensive collection thereof. In addition, the work presents a comprehensive list of recommendations and suggestions which may help solve or prevent these ethical issues, and are a good starting point for anyone designing an ethical cybersecurity strategy.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114957237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mikołaj Komisarek, M. Pawlicki, Marian Mihailescu, Darius Mihai, M. Cărăbaş, R. Kozik, M. Choraś
In this day and age of widespread Internet access, more and more aspects of the economy are becoming dependent on various aspects of network technologies. Cybercrimes are on the rise and massive numbers of network security breaches occur every year. This paper presents network data collected in the Netflow format and its application to detect network attacks. The paper proposes a refined, real-world dataset collected from an academic network. The dataset is a direct result from the experience gained by working on and with the SIMARGL2021 dataset. The applicability of the new dataset is demonstrated on several machine learning algorithms. This novel dataset is open-sourced for researchers to download and use in scientific work.
{"title":"A novel, refined dataset for real-time Network Intrusion Detection","authors":"Mikołaj Komisarek, M. Pawlicki, Marian Mihailescu, Darius Mihai, M. Cărăbaş, R. Kozik, M. Choraś","doi":"10.1145/3538969.3544486","DOIUrl":"https://doi.org/10.1145/3538969.3544486","url":null,"abstract":"In this day and age of widespread Internet access, more and more aspects of the economy are becoming dependent on various aspects of network technologies. Cybercrimes are on the rise and massive numbers of network security breaches occur every year. This paper presents network data collected in the Netflow format and its application to detect network attacks. The paper proposes a refined, real-world dataset collected from an academic network. The dataset is a direct result from the experience gained by working on and with the SIMARGL2021 dataset. The applicability of the new dataset is demonstrated on several machine learning algorithms. This novel dataset is open-sourced for researchers to download and use in scientific work.","PeriodicalId":306813,"journal":{"name":"Proceedings of the 17th International Conference on Availability, Reliability and Security","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116859511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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