M. Buinevich, K. Izrailov, Igor Kotenko, I. Ushakov, D. Vlasov
The paper deals with the problem of internal intruders (insiders) in the organization. It presents Top-7 methods of insider detection and substantiates the necessity of their joint usage. A technique to combine different methods of insider detection is proposed. A combination of methods means using the results of only one of them, union or/and intersecting it with the results of others. The technique formalization and graphic interpretation are given, as well as expressions for completeness, precision, accuracy, error and F-measure. Visualization of the third method combination is provided as an example. The results of experiments on insider detection at the real corporate network using human and machine-based methods are presented.
{"title":"Approach to combining different methods for detecting insiders","authors":"M. Buinevich, K. Izrailov, Igor Kotenko, I. Ushakov, D. Vlasov","doi":"10.1145/3440749.3442619","DOIUrl":"https://doi.org/10.1145/3440749.3442619","url":null,"abstract":"The paper deals with the problem of internal intruders (insiders) in the organization. It presents Top-7 methods of insider detection and substantiates the necessity of their joint usage. A technique to combine different methods of insider detection is proposed. A combination of methods means using the results of only one of them, union or/and intersecting it with the results of others. The technique formalization and graphic interpretation are given, as well as expressions for completeness, precision, accuracy, error and F-measure. Visualization of the third method combination is provided as an example. The results of experiments on insider detection at the real corporate network using human and machine-based methods are presented.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124419466","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}
A. Katasonov, Aleksandr Tcvetkov, Anna Polyanicheva, A. Krasov
Nowadays, the ways of obtaining unauthorized access to devices and are growing due to the increase in the volume of transmitted information. The topic of the article "Investigation of operating system security mechanisms for vulnerabilities" is to study the operating system vulnerability to attacks of unauthorized access. This article discusses the various possibilities for implementing internal attacks to gain unauthorized access. A criterion was selected, and a comparative analysis of operating system vulnerabilities to these attacks was carried out, as well as recommendations for improving resistance to these attacks are proposed.
{"title":"Investigation of operating system security mechanisms for vulnerabilities","authors":"A. Katasonov, Aleksandr Tcvetkov, Anna Polyanicheva, A. Krasov","doi":"10.1145/3440749.3442611","DOIUrl":"https://doi.org/10.1145/3440749.3442611","url":null,"abstract":"Nowadays, the ways of obtaining unauthorized access to devices and are growing due to the increase in the volume of transmitted information. The topic of the article \"Investigation of operating system security mechanisms for vulnerabilities\" is to study the operating system vulnerability to attacks of unauthorized access. This article discusses the various possibilities for implementing internal attacks to gain unauthorized access. A criterion was selected, and a comparative analysis of operating system vulnerabilities to these attacks was carried out, as well as recommendations for improving resistance to these attacks are proposed.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128836291","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}
T. Tran, Ha Hoang Thi Thanh, Phuong Hoai Dang, M. Riveill
Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model. Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods.
{"title":"Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model.","authors":"T. Tran, Ha Hoang Thi Thanh, Phuong Hoai Dang, M. Riveill","doi":"10.1145/3440749.3442656","DOIUrl":"https://doi.org/10.1145/3440749.3442656","url":null,"abstract":"Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model. Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods.","PeriodicalId":344578,"journal":{"name":"Proceedings of the 4th International Conference on Future Networks and Distributed Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624546","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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