2020 4th Cyber Security in Networking Conference (CSNet)最新文献

A Decentralized Resource Discovery Using Attribute Based Encryption for Internet of Things 基于属性加密的物联网分散资源发现

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265463

M. Kamel, Yuping Yan, P. Ligeti, C. Reich

{"title":"A Decentralized Resource Discovery Using Attribute Based Encryption for Internet of Things","authors":"M. Kamel, Yuping Yan, P. Ligeti, C. Reich","doi":"10.1109/CSNet50428.2020.9265463","DOIUrl":"https://doi.org/10.1109/CSNet50428.2020.9265463","url":null,"abstract":"The number of devices connected together in the Internet of Things (IoT) are growing and therefore the demand of an efficient and secure method for discovering the resources in IoT is increasing. In most of IoT schemes, the resources are discovered based on their properties (i.e. their location, types, etc.) and the clients are able to discover the resources that are registered in the network. This discovery ability can be restricted to a number of clients by encrypting the discoverable address. However, any client with the key can decrypt and access a private resource regardless of the attributes of the client. In this paper, a decentralized resource discovery model using Cipher Policy Attribute Based Encryption (CP-ABE) and Distributed Hash Table (DHT) is introduced that allows secure and private discovering of the resources in IoT network. We integrated the CP-ABE that allows clients only by users’ inherent attributes, to discover the resources in the network. Our proposed model provides a higher level of security for resource discovery by allowing the resources during registration in addition to their properties to define the attributes of the clients that are able to discover those resources. The model we proposed uses multi-authority ABE, which as a public key encryption can fit the decentralized environment.","PeriodicalId":234911,"journal":{"name":"2020 4th Cyber Security in Networking Conference (CSNet)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121346324","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: 2

[Copyright notice] (版权)

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/csnet50428.2020.9265464

引用次数: 0

Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data 基于现代数据的网络入侵检测无监督机器学习技术

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265461

Miel Verkerken, Laurens D’hooge, T. Wauters, B. Volckaert, F. Turck

{"title":"Unsupervised Machine Learning Techniques for Network Intrusion Detection on Modern Data","authors":"Miel Verkerken, Laurens D’hooge, T. Wauters, B. Volckaert, F. Turck","doi":"10.1109/CSNet50428.2020.9265461","DOIUrl":"https://doi.org/10.1109/CSNet50428.2020.9265461","url":null,"abstract":"The rapid growth of the internet, connecting billions of people and businesses, brings with it an increased risk of misuse. Handling this misuse requires adaptive techniques detecting known as well as unknown, zero-day, attacks. The latter proved most challenging in recent studies, where supervised machine learning techniques excelled at detecting known attacks, but failed to recognize unknown patterns. Therefore, this paper focuses on anomaly-based detection of malicious behavior on the network by using flow-based features. Four unsupervised methods are evaluated of which two employ a self-supervised learning approach. A realistic modern dataset, CIC-IDS-2017, containing multiple different attack types is used to evaluate the proposed models in terms of classification performance and computational complexity. The results show that an autoencoder, obtained from the field of deep-learning, yields the highest area under the Receiver Operating Characteristics (AUROC) of 0.978 while maintaining an acceptable computational complexity, followed by one-class support vector machine, isolation forest and principal components analysis.","PeriodicalId":234911,"journal":{"name":"2020 4th Cyber Security in Networking Conference (CSNet)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377812","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: 9

Simplified Method for Fast and Efficient Incident Detection in Industrial Networks 工业网络中快速高效事件检测的简化方法

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265536

Karel Kuchar, R. Fujdiak, Petr Blazek, Zdenek Martinasek, E. Holasova

引用次数: 0

Cloud Assisted Privacy Preserving Using Homomorphic Encryption 使用同态加密的云辅助隐私保护

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265535

Khalil Hariss, M. Chamoun, A. Samhat

{"title":"Cloud Assisted Privacy Preserving Using Homomorphic Encryption","authors":"Khalil Hariss, M. Chamoun, A. Samhat","doi":"10.1109/CSNet50428.2020.9265535","DOIUrl":"https://doi.org/10.1109/CSNet50428.2020.9265535","url":null,"abstract":"In this paper, privacy preserving at the cloud side is enabled by adopting Homomorphic Encryption (HE) as a major solution for protecting users’ sensitive data. As a Proof Of Concept (POC), we take the case of a university that is adopting the cloud as a practical method for storing and operating over its private data. The main contribution of this application is that different data are stored encrypted at the cloud side. Students’ data such as name, gender, date of birth, etc are encrypted using AES and hash functions. Students’ indexes and grades are encrypted homomorphically using our modified Domingo Ferrer (DF) encryption scheme [1]. The main importance of the usage of HE in this application is allowing the process over encrypted data at the cloud side such as computing encrypted students’ average. Different queries are sent from the university side to the cloud side, after operating and processing over encrypted data all results are shipped back encrypted to the university where the primitive data is recovered. Cloud infrastructure is created using Apache CloudStack and different encryption schemes are implemented under Python using SageMath library.","PeriodicalId":234911,"journal":{"name":"2020 4th Cyber Security in Networking Conference (CSNet)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780385","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: 2

Using Probabilistic Availability Measures for Predicting Targeted Attacks on Network Nodes 基于概率可用性度量的网络节点目标攻击预测

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265459

M. Pióro, M. Mycek, A. Tomaszewski

引用次数: 2

Safe Traffic Adaptation Model in Wireless Mesh Networks 无线Mesh网络中的安全流量自适应模型

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265456

L. Khoukhi, R. Khatoun

引用次数: 0

Specific Anomaly Detection Method in Wireless Communication Networks 无线通信网络中的特殊异常检测方法

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265533

E. Holasova, R. Fujdiak, Karel Kuchar

引用次数: 0

Privacy-Preserving Authentication Framework for UAS Traffic Management Systems 面向UAS流量管理系统的隐私保护认证框架

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265534

Anas Alsoliman, Abdulrahman Bin Rabiah, M. Levorato

{"title":"Privacy-Preserving Authentication Framework for UAS Traffic Management Systems","authors":"Anas Alsoliman, Abdulrahman Bin Rabiah, M. Levorato","doi":"10.1109/CSNet50428.2020.9265534","DOIUrl":"https://doi.org/10.1109/CSNet50428.2020.9265534","url":null,"abstract":"In 2015, the Federal Aviation Administration (FAA) has announced the integration of unmanned aerial vehicles (UAV) into the national airspace via a traffic management system – called UAS Traffic Management (UTM) – dedicated to Unmanned Aircraft Systems (UAS) to support advanced UAV operations such as autonomous and beyond visual line of sight (BVLOS) flight missions. The UTM incorporates an identification framework called Remote ID which mandates all UAS operators to continuously identify themselves while on flight. However, the current version of the framework lacks security features and its design has raised privacy concerns among UAS operators. This paper extends the Remote ID framework to include a Privacy-Preserving Authentication Framework that anonymously verifies the authenticity of flying UAVs. Moreover, the framework authenticates the UAV’s flight permissions without revealing neither the identity of its operator nor its entire flight path, while at the same time keeping any identifying information accessible to the authorities in case of a dispute. To satisfy the proposed security and privacy requirements, a UAV’s flight plan that is represented as a series of waypoints is transformed into localized UAV trajectories which create a set of contiguous flight zones, each with its own flight permission. This framework utilizes the Boneh–Gentry-Lynn–Shacham (BGLS) digital signature scheme to sign and transform each zone information into a flight permission and aggregate a set of signatures into a single signature along with additional attributes used to construct a Remote-ID message that anonymously authenticates flying UAVs.","PeriodicalId":234911,"journal":{"name":"2020 4th Cyber Security in Networking Conference (CSNet)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122743414","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

Detecting abnormal DNS traffic using unsupervised machine learning 使用无监督机器学习检测异常DNS流量

2020 4th Cyber Security in Networking Conference (CSNet) Pub Date : 2020-10-21 DOI: 10.1109/CSNet50428.2020.9265466

Thi Quynh Nguyen, R. Laborde, A. Benzekri, Bruno Qu’hen

引用次数: 2