2020 17th International ISC Conference on Information Security and Cryptology (ISCISC)最新文献_第2页

An Optimized Structure of State Channel Network to Improve Scalability of Blockchain Algorithms 一种优化的状态通道网络结构以提高区块链算法的可扩展性

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/ISCISC51277.2020.9261916

A. Ajorlou, A. Abbasfar

{"title":"An Optimized Structure of State Channel Network to Improve Scalability of Blockchain Algorithms","authors":"A. Ajorlou, A. Abbasfar","doi":"10.1109/ISCISC51277.2020.9261916","DOIUrl":"https://doi.org/10.1109/ISCISC51277.2020.9261916","url":null,"abstract":"Nowadays, blockchain is very common and widely used in various fields. The properties of blockchain-based algorithms such as being decentralized and uncontrolled by institutions and governments, are the main reasons that has attracted many applications. The security and the scalability limitations are the main challenges for the development of these systems. Using second layer network is one of the various methods proposed to improve the scalability of these systems. This network can increase the total number of transactions per second by creating extra channels between the nodes that operate in a different layer not obligated to be on consensus ledger. In this paper, the optimal structure for the second layer network has been presented. In the proposed structure we try to distribute the parameters of the second layer network as symmetrically as possible. To prove the optimality of this structure we first introduce the maximum scalability bound, and then calculate it for the proposed structure. This paper will show how the second layer method can improve the scalability without any information about the rate of transactions between nodes.","PeriodicalId":206256,"journal":{"name":"2020 17th International ISC Conference on Information Security and Cryptology (ISCISC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130267718","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

ISCISC 2020 Index ISCISC 2020索引

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/iscisc51277.2020.9261908

引用次数: 0

A Deep Learning-based Malware Hunting Technique to Handle Imbalanced Data 基于深度学习的恶意软件搜索技术处理不平衡数据

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/ISCISC51277.2020.9261913

Zahra Moti, S. Hashemi, Amir Namavar Jahromi

{"title":"A Deep Learning-based Malware Hunting Technique to Handle Imbalanced Data","authors":"Zahra Moti, S. Hashemi, Amir Namavar Jahromi","doi":"10.1109/ISCISC51277.2020.9261913","DOIUrl":"https://doi.org/10.1109/ISCISC51277.2020.9261913","url":null,"abstract":"Nowadays, with the increasing use of computers and the Internet, more people are exposed to cyber-security dangers. According to antivirus companies, malware is one of the most common threats of using the Internet. Therefore, providing a practical solution is critical. Current methods use machine learning approaches to classify malware samples automatically. Despite the success of these approaches, the accuracy and efficiency of these techniques are still inadequate, especially for multiple class classification problems and imbalanced training data sets. To mitigate this problem, we use deep learning-based algorithms for classification and generation of new malware samples. Our model is based on the opcode sequences, which are given to the model without any pre-processing. Besides, we use a novel generative adversarial network to generate new opcode sequences for oversampling minority classes. Also, we propose the model that is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) to classify malware samples. CNN is used to consider short-term dependency between features; while, LSTM is used to consider longer-term dependence. The experiment results show our method could classify malware to their corresponding family effectively. Our model achieves 98.99% validation accuracy.","PeriodicalId":206256,"journal":{"name":"2020 17th International ISC Conference on Information Security and Cryptology (ISCISC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115252353","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: 5

ISCISC 2020 Contributor Page ISCISC 2020贡献者页面

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/iscisc51277.2020.9261907

引用次数: 0

Advantages and disadvantages of using cryptography in steganography 在隐写术中使用密码术的优点和缺点

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/ISCISC51277.2020.9261921

A. Hadipour, Raheleh Afifi

引用次数: 4

ISCISC 2020 Subject Index Page ISCISC 2020主题索引页

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/iscisc51277.2020.9261915

引用次数: 0

Lurking Eyes: A Method to Detect Side-Channel Attacks on JavaScript and WebAssembly 潜伏的眼睛:一种检测JavaScript和WebAssembly侧通道攻击的方法

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/ISCISC51277.2020.9261920

Mohammad Erfan Mazaheri, Farhad Taheri, Siavash Bayat Sarmadi

{"title":"Lurking Eyes: A Method to Detect Side-Channel Attacks on JavaScript and WebAssembly","authors":"Mohammad Erfan Mazaheri, Farhad Taheri, Siavash Bayat Sarmadi","doi":"10.1109/ISCISC51277.2020.9261920","DOIUrl":"https://doi.org/10.1109/ISCISC51277.2020.9261920","url":null,"abstract":"Side-channel attacks are a group of powerful attacks in hardware security that exploit the deficiencies in the implementation of systems. Timing side-channel attacks are one of the main categories that employ the time difference of running an operation in different states. In recent years, many types of timing side-channel analysis are proposed under the name of cache attacks. The limitation of such attacks is the requirement of running a spy program locally on the targeted device. Various studies have tried to overcome this limitation by implementing timing side-channel attacks, specially cache attacks, remotely on JavaScript and WebAssembly. There are some countermeasures proposed by previous works at three levels of hardware, operating system, and software. The main approach in most of previous works is to prevent timing side-channel attacks by disabling the essential features of JavaScript. In this paper, we weight the pros and cons of the previous countermeasures and propose a novel detection-based approach, namely Lurking Eyes. The proposed approach has the least performance reduction in JavaScript and WebAssembly. The evaluation results show that the Lurking Eyes has an accuracy of 0.998, precision of 0.983, and F-measure of 0.983. Considering the evaluation results and fewer limitations compared to previous works, Lurking Eyes method can be introduced as an effective way to counter timing side-channel attacks on JavaScript and WebAssembly.","PeriodicalId":206256,"journal":{"name":"2020 17th International ISC Conference on Information Security and Cryptology (ISCISC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114676761","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

ISCISC 2020 Cover Page ISCISC 2020封面

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/iscisc51277.2020.9261902

引用次数: 0

A Binary Relevance Adaptive Model-Selection for Ensemble Steganalysis 集成隐写分析的二值关联自适应模型选择

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/ISCISC51277.2020.9261910

Tayebe Abazar, Peyman Masjedi, M. Taheri

{"title":"A Binary Relevance Adaptive Model-Selection for Ensemble Steganalysis","authors":"Tayebe Abazar, Peyman Masjedi, M. Taheri","doi":"10.1109/ISCISC51277.2020.9261910","DOIUrl":"https://doi.org/10.1109/ISCISC51277.2020.9261910","url":null,"abstract":"Steganalysis is an interesting classification problem in order to discriminate the images, including hidden messages from the clean ones. There are many methods, including deep CNN networks to extract fine features for this classification task. Nevertheless, a few researches have been conducted to improve the final classifier. Some state-of-the-art methods try to ensemble the networks by a voting strategy to achieve more stable performance. In this paper, a selection phase is proposed to filter improper networks before any voting. This filtering is done by a binary relevance multi-label classification approach. The Logistic Regression (LR) is chosen here as the last layer of network for classification. The large-margin Fisher’s linear discriminant (FLD) classifier is assigned to each one of the networks. It learns to discriminate the training instances which associated network is suitable for or not. Xu-Net, one of the most famous state-of-the-art Steganalysis models, is chosen as the base networks. The proposed method with different approaches is applied on the BOSSbase dataset and is compared with traditional voting and also some state-of-the-art related ensemble techniques. The results show significant accuracy improvement of the proposed method in comparison with others.","PeriodicalId":206256,"journal":{"name":"2020 17th International ISC Conference on Information Security and Cryptology (ISCISC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122408670","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: 0

A fast non-interactive publicly verifiable secret sharing scheme 一种快速、非交互式、可公开验证的秘密共享方案

2020 17th International ISC Conference on Information Security and Cryptology (ISCISC) Pub Date : 2020-09-09 DOI: 10.1109/ISCISC51277.2020.9261914

Shahrooz Janbaz, R. Asghari, Bagher Bagherpour, A. Zaghian

引用次数: 1