National Institute of Standards and Technology における超伝導研究及び生活

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Cognitive engineering

Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup ’99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

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A Deep Learning Approach to Network Intrusion Detection

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

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Deep Learning Approach for Intelligent Intrusion Detection System

Recently, because of irregular power demand, we have suffered from an electric power shortage. The necessity of the adoption of smart grid which makes effective supply of power by using the two-way communication across the grid between the customers and electric energy providers is growing more and more. If smart grid set up in our country, the third-parties which provide services to customer using the information acquired from smart grid, might be revved up. In this paper, we suggest a methodology how classify the types of family by analysing an power consumption pattern using data mining technique. To make a classifier for categorizing the household types, we need power consumption data and their family type. However, it is hard to get both of them. Therefore we develop the simulator that generates power consumption patterns of the household and classify the types of family. Also, we present a potential for application services such as customized services for a specific family or goods marketing. 논문 14-39C-01-08 한국통신학회논문지(J-KICS) '14-01 Vol.39C No.01 http://dx.doi.org/10.7840/kics.2014.39C.1.72 72 ※ 본 연구는 지식경제부 산업융합원천기술개발사업으로 지원된 연구결과입니다(No.10043907) First Author : 부산대학교 전자전기컴퓨터공학과 정보보호 및 IoT 연구실, jihyunkim@pusan.ac.kr, 학생회원 ° Corresponding Author : 부산대학교 정보컴퓨터공학부 정보보호 및 IoT 연구실, hownkim@pusan.ac.kr, 종신회원 * 부산대학교 전자전기컴퓨터공학과 정보보호 및 IoT 연구실 논문번호:KICS2013-11-496, 접수일자:2013년 11월 15일, 심사일자 : 2013년 12월 23일, 최종논문접수일자 : 2014년 1월 8일 논문 / 스마트 그리드 환경에서 가정의 소비전력 생성 시뮬레이터 개발 및 데이터 마이닝 기법을 이용한 가족 유형 분류

Development of the Power Consumption Simulator and Classification of the Types of Household by Using Data Mining Over Smart Grid

 Abstract—In case we use a quantum computer, we have to construct the countermeasure. Due to the high efficiency and fast computation time, the multivariate quadratic public key systems are considered as an alternative to RSA or ECC based systems. However, the large key size is a fatal disadvantage of the multivariate quadratic public key systems. For this reason, the multivariate quadratic public key systems are not widely used. In this paper, we measure how much the private key memory size can be reduced by using a secure pseudo-random number generator in the UOV scheme. Almost 93% of the private key memory size is reduced.

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Analysis for Memory Reduction of the UOV Scheme with the Application of PRNG

This paper presents an efficient filtering system for a metastable state-based true random number generator. To output a result with high randomness, we use loop-storage for storing the value of metastability. During the metastable state, the output value is accumulated to the storage. When the non-metastable state arises, the stored metastable value will be used for output instead of the result of the non-metastable state. As a result, we can maintain high entropy together with the original throughput.

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Always Metastable State True Random Number Generator

Radio Frequency Identification (RFID) systems are increasingly becoming accepted for many EPC Network applications However, RFID systems have some privacy problems In this paper, a system for missing child location tracking in the EPC Network applications, is proposed The proposed system improves security and privacy compared to existing applications, while also keeping in line with traditional procedures, commonly accepted by most industrial applications The proposed MCLT (Missing Child Location Tracking) system can protect users’ privacy while providing location tracking of the RFID tag

/pdf/an-rfid-system-based-mclt-system-with-improved-privacy-olw1sq6pcj.pdf

An RFID system based MCLT system with improved privacy

Traditional surveillance system is enabled by closed-circuit television(CCTV) monitoring each district in real time. However, this approach should install expensive CCTV to every destination and collected images or videos should go through complex post-processing to get useful and meaningful information. Furthermore nowadays CCTV violates people's private life, which is crucial problem in modern society. If our goal is secure and robust street, more simple and cheap approaches could be favorable. In this paper, we present a novel surveillance system using light sensor which is commonly available in embedded processors or modern smart-phones. On the contrast to the traditional method, light sensor is cheap module and easy to install and process the information. After processing, we can determine the secure or insecure places with derived information. For practical evaluation, we made micro testbed in our campus. First we collected light information from several locations in different time domains. And then secure or insecure places are determined in each time domain. We defined bright and dark places as secure and insecure places, respectively. The evaluation shows that our approach is unprecedented ultra light-weight approach and cost effective method to improve security in our society.

Howon Kim

Papers

Development of the Power Consumption Simulator and Classification of the Types of Household by Using Data Mining Over Smart Grid

Analysis for Memory Reduction of the UOV Scheme with the Application of PRNG

Always Metastable State True Random Number Generator

An RFID system based MCLT system with improved privacy

Short paper: Surveillance system with light sensor