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.

/pdf/a-deep-learning-approach-to-network-intrusion-detection-n63kpq7big.pdf

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

Hidden Indicator Based PIN-Entry Method Using Audio Signals

Power analysis (PA) is known as a powerful physical attack method in the field of information security. This method uses the statistical characteristics of leaked power consumption signals measured from security devices to reveal the secret keys. However, when measuring a leakage power signal, it may be easily distorted by the noise due to its low magnitude values, and thus the PA attack shows different performances depending on the noise level of the measured signal. To overcome this vulnerability of the PA attack, we propose a noise-reduction method based on wavelet de-noising. Experimental results show that the proposed de-noising method improves the attack efficiency in terms of the number of signals required for the successful attack as well as the reliability on the guessing key.

Performance Improvement of Power Analysis Attacks based on Wavelet De-noising

A PUF is a technology for distinguishing a device from other de vices like biological information such as humans’ iris or fingerprints Over the past decade, many researchers studied va rious methods for implementing PUFs and utilizing them in identification, random number generation, key distribution and authentication However, various attacks on the PUFs are the major reason to inhibiting the proliferation of PUF For the re asons, various technologies are being studied to enhance safety of PUFs In this paper, we will see several PUF implementations and various attacks on PUFs, and suggest guidelines for securely implementing PUFs We expect our guidelines would be t he foundation for implementing the secure and reliable PUFsKeywords: Physically Unclonable Function; Implementation Guideline;접수일(2014년 1월 2일), 수정일(2014년 2월 5일), 게재확정일(2014년 2월 8일)* 본 논문은 한국전자통신연구원의 기초연구과제로 수행한 연구결과입니다†주저자, guneez@pusanackr‡교신저자, howonkim@pusanackr (Corresponding author) I서 론 최근 IT 기술의 급격한 발전으로 인해 빠른 속도로 새로운 최첨단 디바이스들이 등장하고 있지만, 불법 복제에 대한 피해와 위조로 인한 경제적, 산업적 손실이 날이 갈수록 커져가고 있는 실정이다 이러한 문제를 해결하고자 Physical Unclonable Function (PUF)라는 새로운 기술이 등장하였다 PUF는 마치 인간의 지문이나 홍채와 같은 생체 정보처럼 각각의

Guidelines for Safe and Reliable PUF Implementation

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

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RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network

Bandwidth trading procedures can be made to incentivize users to sell their needless traffic and indirectly reduce the probability of traffic congestion. However, implementation of bandwidth trading is opex-heavy from Internet Service Provider (ISP) perspective, while on the other hand, users also do not trust network executions from the ISP due to its heavily centralized control. These issues hinder the applicability of bandwidth trading and become our motivation to propose this paper. Our bandwidth-trading framework utilize software-defined networking (SDN) and blockchain. SDN automates the bandwidth trading executions from the ISP side and reduces the opex. Meanwhile, the smart contract is a trusted platform for building a trading marketplace where buyers, sellers, and SDN controllers can negotiate the trading terms. Once the trading is executed, SDN controllers generate proof of trading that must be submitted to the smart contract as proof of provisioning. We implement our works using Ethereum and POX SDN controllers, and the results prove that it can provide a seamless bandwidth trading experience with reasonable overhead. Furthermore, by committing to our framework, bandwidth trading can be executed fairly and securely because all previous provisioning can be cross-checked through the provided proof-of-trading.

Howon Kim

Papers

Hidden Indicator Based PIN-Entry Method Using Audio Signals

Performance Improvement of Power Analysis Attacks based on Wavelet De-noising

Guidelines for Safe and Reliable PUF Implementation

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network

Blockchain-Powered Bandwidth Trading on SDN-Enabled Edge Network