IET Information Security最新文献_第10页

On the performance of non-profiled side channel attacks based on deep learning techniques 基于深度学习技术的非轮廓侧信道攻击性能研究

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-12-20 DOI: 10.1049/ise2.12102

Ngoc-Tuan Do, Van-Phuc Hoang, Van Sang Doan, Cong-Kha Pham

{"title":"On the performance of non-profiled side channel attacks based on deep learning techniques","authors":"Ngoc-Tuan Do, Van-Phuc Hoang, Van Sang Doan, Cong-Kha Pham","doi":"10.1049/ise2.12102","DOIUrl":"https://doi.org/10.1049/ise2.12102","url":null,"abstract":"In modern embedded systems, security issues including side-channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profiled and non-profiled SCAs. This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non-profiled attacks on the AES-128 encryption implementation. Especially, the proposed network is fine-tuned with different number of hidden layers, labelling techniques and activation functions. Along with the designed models, a dataset reconstruction and labelling technique for the proposed model has also been performed for solving the high dimension data and imbalanced dataset problem. As a result, the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC-V microcontroller, and ChipWhisperer boards has achieved a higher performance of non-profiled attacks. Specifically, necessary investigations to evaluate the efficiency of the proposed techniques against different SCA countermeasures, such as masking and hiding, have been performed. In addition, the effect of the activation function on the proposed DL models was investigated. The experimental results have clarified that the exponential linear unit function is better than the rectified linear unit in fighting against noise generation-based hiding countermeasure.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"17 3","pages":"377-393"},"PeriodicalIF":1.4,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ise2.12102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50138614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-12-19 DOI: 10.1049/ise2.12104

Jiamei Liu, Lin Tan, Hong Xu

{"title":"Improved related-tweakey rectangle attacks on round-reduced Deoxys-BC","authors":"Jiamei Liu, Lin Tan, Hong Xu","doi":"10.1049/ise2.12104","DOIUrl":"https://doi.org/10.1049/ise2.12104","url":null,"abstract":"Deoxys-BC is the internal tweakable block cipher of the authenticated encryption (AE) Deoxys family, in which Deoxys-II is the primary choice for the use case of ‘Defence in depth’ among the portfolio of CAESAR competition. Improvements of the related-tweakey rectangle attacks on round-reduced Deoxys-BC using the known distinguishers is focussed in this study. Under the new related-key rectangle attack framework proposed by Dong et al. in EUROCRYPT 2022, we present three kinds of precomputed tables to further reduce the time complexity in the key-recovery phase. In the related-tweakey rectangle attack, the invalid quartets are filtered or the subtweakey candidates are obtained by lookup the precomputed tables without more computation. Based on the precomputed table technique, we improved the related-tweakey rectangle attacks on 11-round Deoxys-BC-256, 13-round and 14-round Deoxys-BC-384. Furthermore, we reduce the time complexity of the 13-round related-tweakey rectangle attack on Deoxys AE scheme Deoxys-I-256-128 by a factor of 224 compared with the best previous attack.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"17 3","pages":"407-422"},"PeriodicalIF":1.4,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ise2.12104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50137697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preimage attacks on reduced-round Keccak hash functions by solving algebraic systems 用代数系统求解降维圆Keccak散列函数的预图像攻击

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-12-13 DOI: 10.1049/ise2.12103

Junling Pei, Lusheng Chen

引用次数: 1

Lattice-based cryptosystems in standardisation processes: A survey 标准化过程中的基于格的密码系统：综述

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-12-10 DOI: 10.1049/ise2.12101

Anyu Wang, Dianyan Xiao, Yang Yu

引用次数: 0

MF-RF: A detection approach based on multi-features and random forest algorithm for improved collusive interest flooding attack MF-RF：一种基于多特征和随机森林算法的改进共谋利益泛滥攻击检测方法

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-11-28 DOI: 10.1049/ise2.12100

Meng Yue, Silin Peng, Wenzhi Feng

引用次数: 0

Conditional differential analysis on the KATAN ciphers based on deep learning 基于深度学习的KATAN密码的条件差分分析

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-11-19 DOI: 10.1049/ise2.12099

Dongdong Lin, Manman Li, Zezhou Hou, Shaozhen Chen

引用次数: 0

Design and cryptanalysis of a new iterative structure 一种新的迭代结构的设计与密码分析

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-11-14 DOI: 10.1049/ise2.12098

Jiajie Liu, Bing Sun, Chao Li

引用次数: 1

An embedded intrusion detection and prevention system for home area networks in advanced metering infrastructure 一种先进计量基础设施中用于家庭局域网的嵌入式入侵检测和预防系统

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-11-07 DOI: 10.1049/ise2.12097

Sahar Lazim Qaddoori, Qutaiba Ibrahim Ali

{"title":"An embedded intrusion detection and prevention system for home area networks in advanced metering infrastructure","authors":"Sahar Lazim Qaddoori, Qutaiba Ibrahim Ali","doi":"10.1049/ise2.12097","DOIUrl":"https://doi.org/10.1049/ise2.12097","url":null,"abstract":"With the widespread adoption of smart metres in the power sector, anomaly detection has become a critical tool for analysing customers' unusual consumption patterns and network traffic. Detecting anomalies in power consumption and communication is primarily a real-time big data analytics issue regarding data mining along with a vast number of parallel streaming data from smart metres. In this study, an embedded Intrusion Detection and Prevention System (IDPS) is proposed as a Wifi-based smart metre for Home Area Networks (HANs) in the Advanced Metering Infrastructure (AMI) network. So, the proposed system employs one machine learning model based on IDPS to guard the HAN network from various attacks that utilise the Message Queueing Telemetry Transport protocol between the smart metre and IoT sensors. Also, it uses two machine learning models to detect the abnormality in periodic and daily data metering respectively. So, multiple algorithms have been used to find the suitable algorithm for each of the three anomaly detection models. These models have been evaluated and tested using real data sets regarding resources usage and detection performance to demonstrate the efficiency and effectiveness of using machine learning algorithms in the built anomaly detection models. The experiments show that the anomaly detection models performed well for various abnormalities.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"17 3","pages":"315-334"},"PeriodicalIF":1.4,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ise2.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50124342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning-based framework to identify and characterise heterogeneous secure network traffic 一种基于深度学习的框架，用于识别和表征异构安全网络流量

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-10-03 DOI: 10.1049/ise2.12095

Faiz Ul Islam, Guangjie Liu, Weiwei Liu, Qazi Mazhar ul Haq

{"title":"A deep learning-based framework to identify and characterise heterogeneous secure network traffic","authors":"Faiz Ul Islam, Guangjie Liu, Weiwei Liu, Qazi Mazhar ul Haq","doi":"10.1049/ise2.12095","DOIUrl":"https://doi.org/10.1049/ise2.12095","url":null,"abstract":"The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non-standard ports and encryption of data payloads, the classical port-based and payload-based classification techniques fail to classify the secured network traffic. To solve the above-mentioned problems, this paper proposed an effective deep learning-based framework employed with flow-time-based features to predict heterogeneous secure network traffic best. The state-of-the-art machine learning strategies (C4.5, random forest, and K-nearest neighbour) are investigated for comparison. The proposed 1D-CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state-of-the-art machine learning techniques employed for secure network traffic analysis.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"17 2","pages":"294-308"},"PeriodicalIF":1.4,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ise2.12095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50120020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Efficient module learning with errors-based post-quantum password-authenticated key exchange 基于错误的后量子密码认证密钥交换的高效模块学习

IF 1.4 4区计算机科学

IET Information Security Pub Date : 2022-09-20 DOI: 10.1049/ise2.12094

Peixin Ren, Xiaozhuo Gu, Ziliang Wang

{"title":"Efficient module learning with errors-based post-quantum password-authenticated key exchange","authors":"Peixin Ren, Xiaozhuo Gu, Ziliang Wang","doi":"10.1049/ise2.12094","DOIUrl":"10.1049/ise2.12094","url":null,"abstract":"Password-authenticated key exchange (PAKE) is a cryptographic primitive that can establish secure remote communications between the client and the server, especially with the advantage of amplifying memorable passwords into strong session keys. However, the arrival of the quantum computing era has brought new challenges to traditional PAKE protocols. Thus, designing an efficient post-quantum PAKE scheme becomes an open research question. In this paper, the authors construct a quantum-safe PAKE protocol, which is a horizontal extension of the password-authenticated key (PAK) protocol in the field of module lattices. Subsequently, the authors accompany the proposed protocol with a rigorous security proof in the random oracle model with two adaptions: applying the CDF-Zipf model to characterise the ability of the adversary and using the pairing with errors assumption to simplify the proof. Taking the flexibility of the module learning with errors (MLWE) problem, the authors elaborately select three parameter sets to meet different application scenarios. Specifically, the authors’ Recommended-PAKE implementation achieves 177-bit post-quantum security with a generous margin to cope with later improvement in cryptanalysis. The performance results indicate that the authors’ MLWE-PAKE is quite practical: compared with the latest Yang-PAK, the authors’ Recommended-PAK reduces the communication cost and the running time by 36.8% and 13.8%, respectively.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"17 1","pages":"3-17"},"PeriodicalIF":1.4,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ise2.12094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41530747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2