2021 IEEE International Conference on Cyber Security and Resilience (CSR)最新文献_第4页

Insider Threat Detection using Deep Autoencoder and Variational Autoencoder Neural Networks 利用深度自编码器和变分自编码器神经网络进行内部威胁检测

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527925

Efthimios Pantelidis, G. Bendiab, S. Shiaeles, N. Kolokotronis

引用次数: 2

Rapid Ransomware Detection through Side Channel Exploitation 利用侧信道快速检测勒索软件

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527943

Michael A. Taylor, Eric C. Larson, Mitchell A. Thornton

{"title":"Rapid Ransomware Detection through Side Channel Exploitation","authors":"Michael A. Taylor, Eric C. Larson, Mitchell A. Thornton","doi":"10.1109/CSR51186.2021.9527943","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527943","url":null,"abstract":"A new method for the detection of ransomware in an infected host is described and evaluated. The method utilizes data streams from on-board sensors to fingerprint the initiation of a ransomware infection. These sensor streams, which are common in modern computing systems, are used as a side channel for understanding the state of the system. It is shown that ransomware detection can be achieved in a rapid manner and that the use of slight, yet distinguishable changes in the physical state of a system as derived from a machine learning predictive model is an effective technique. A feature vector, consisting of various sensor outputs, is coupled with a detection criteria to predict the binary state of ransomware present versus normal operation. An advantage of this approach is that previously unknown or zero-day version s of ransomware are vulnerable to this detection method since no apriori knowledge of the malware characteristics are required. Experiments are carried out with a variety of different system loads and with different encryption methods used during a ransomware attack. Two test systems were utilized with one having a relatively low amount of available sensor data and the other having a relatively high amount of available sensor data. The average time for attack detection in the \"sensor-rich\" system was 7.79 seconds with an average Matthews correlation coefficient of 0.8905 for binary system state predictions regardless of encryption method and system load. The model flagged all attacks tested.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133188781","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}

引用次数: 3

Detecting Attacks on IoT Devices using Featureless 1D-CNN 使用无特征1D-CNN检测对物联网设备的攻击

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527910

Arshiya Khan, Chase Cotton

引用次数: 6

Frankenstack: Real-time Cyberattack Detection and Feedback System for Technical Cyber Exercises 科学怪人:用于网络技术演习的实时网络攻击检测和反馈系统

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527923

Mauno Pihelgas, Markus Kont

引用次数: 2

Open Source and Commercial Capture The Flag Cyber Security Learning Platforms - A Case Study 开源和商业捕获标志网络安全学习平台-案例研究

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527941

Matt Swann, Joseph R. Rose, G. Bendiab, S. Shiaeles, Fudong Li

引用次数: 6

Towards Automated Matching of Cyber Threat Intelligence Reports based on Cluster Analysis in an Internet-of-Vehicles Environment 车联网环境下基于聚类分析的网络威胁情报报告自动匹配研究

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527983

G. Raptis, C. Katsini, C. Alexakos

{"title":"Towards Automated Matching of Cyber Threat Intelligence Reports based on Cluster Analysis in an Internet-of-Vehicles Environment","authors":"G. Raptis, C. Katsini, C. Alexakos","doi":"10.1109/CSR51186.2021.9527983","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527983","url":null,"abstract":"Connected and automated vehicles are a transformative technology that is getting closer to maturity and offers many benefits to the Internet-of-Vehicles ecosystem. Considering their multi-diverse nature and the vast amount of data they collect, process, and exchange, they attract varying malicious activities that jeopardize security and safety aspects. Therefore, the successful confrontation of such activities is crucial. When detecting such activities, information about the incoming threat is collected and analyzed during and after the incident. Organizations and security experts use cyber threat intelligence to organize such information. Considering that threats can be related to each other, it is important to provide the security experts with tools that would help them identify and attribute the threats. Towards this direction, in this paper, we present a tool that automatically matches cyber threat intelligence reports based on cluster analysis. Through this tool, the security experts can correlate an incoming attack with previously reported ones and follow similar methods to analyze it, aiming to speed up the attack attribution process.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128876835","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}

引用次数: 4

Named Entity Recognition in Cyber Threat Intelligence Using Transformer-based Models 基于变压器模型的网络威胁情报命名实体识别

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527981

Pavlos Evangelatos, Christos Iliou, T. Mavropoulos, Konstantinos Apostolou, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris

{"title":"Named Entity Recognition in Cyber Threat Intelligence Using Transformer-based Models","authors":"Pavlos Evangelatos, Christos Iliou, T. Mavropoulos, Konstantinos Apostolou, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris","doi":"10.1109/CSR51186.2021.9527981","DOIUrl":"https://doi.org/10.1109/CSR51186.2021.9527981","url":null,"abstract":"The continuous increase in sophistication of threat actors over the years has made the use of actionable threat intelligence a critical part of the defence against them. Such Cyber Threat Intelligence is published daily on several online sources, including vulnerability databases, CERT feeds, and social media, as well as on forums and web pages from the Surface and the Dark Web. Named Entity Recognition (NER) techniques can be used to extract the aforementioned information in an actionable form from such sources. In this paper we investigate how the latest advances in the NER domain, and in particular transformer-based models, can facilitate this process. To this end, the dataset for NER in Threat Intelligence (DNRTI) containing more than 300 pieces of threat intelligence reports from open source threat intelligence websites is used. Our experimental results demonstrate that transformer-based techniques are very effective in extracting cybersecurity-related named entities, by considerably outperforming the previous state- of-the-art approaches tested with DNRTI.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115607907","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}

引用次数: 6

Semi-Automatic Bug Generation Using Test Case Negation 使用测试用例否定的半自动Bug生成

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527992

Tyler Westland, Nan Niu, R. Jha, David Kapp, T. Kebede

引用次数: 0

Increasing resilience of power systems using intentional islanding; a comparison of Binary genetic algorithm and deep learning based method 利用有意孤岛提高电力系统的恢复能力;二值遗传算法与基于深度学习方法的比较

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527918

Pol Paradell, Yannis Spyridis, Alba Colet, A. Ivanova, J. Domínguez-García, Achilleas Sesis, G. Efstathopoulos

引用次数: 1

Real-time, Simulation-based Identification of Cyber-Security Attacks of Industrial Plants 基于仿真的工业厂房网络安全攻击实时识别

2021 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2021-07-26 DOI: 10.1109/CSR51186.2021.9527938

A. Patel, T. Schenk, S. Knorn, H. Patzlaff, D. Obradovic, Andrés Botero Halblaub

引用次数: 4