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A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection

Cybersecurity 2018年第1期1卷 847-859页

作者： Serafeim Moustakidis patrik karlsson AIDEAS OU Narva mnt 5TallinnHarju maakondEstonia

Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network *** deal with the dynamic and complex nature of cyber-attacks,advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning(ML)and deep learning(DL).Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection ***,just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security *** papers have explored various dimensionality reduction algorithms,however a large number of selected features is still required to detect the attacks successfully,which humans cannot intuitively or immediately *** enhance user’s trust and understanding on data without sacrificing on accuracy,this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk *** achieve this,a novel feature extraction pipeline was implemented consisting of the following components:(i)a fuzzy allocation scheme that transforms raw data to fuzzy class memberships,(ii)a novel modality transformation mechanism for converting feature vectors to images(Vec2im)and(iii)a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature *** performance of the proposed methodology was validated with respect to detection accuracy,dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness(86.64%testing accuracy using only one feature)over a number of well-known feature selection(FS)and extracti

关键词： Feature extraction Siamese convolutional neural networks Machine learning Intrusion detection

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An APXPS endstation for gas–solid and liquid–solid interface studies at SSRF

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Nuclear Science and Techniques 2019年第5期30卷 103-112页

作者： Jun Cai Qiao Dong Yong Han Bao-Hua Mao Hui Zhang patrik G.karlsson John ?hlund Ren-Zhong Tai Yi Yu Zhi Liu State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences School of Physical Science and Technology ShanghaiTech University University of Chinese Academy of Sciences Scienta Omicron Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai Synchrotron Radiation Facility

In the past few decades, various surface analysis techniques find wide applications in studies of interfacial phenomena ranging from fundamental surface science,catalysis, environmental science and energy *** the help of bright synchrotron sources, many of these techniques have been further advanced into novel in-situ/operando tools at synchrotron user facilities, providing molecular level understanding of chemical/electrochemical processes in-situ at gas–solid and liquid–solid *** a proper endstation for a dedicated beamline is one of the challenges in utilizing these techniques efficiently for a variety of user's requests. Many factors,including pressure differential, geometry and energy of the photon source, sample and analyzer, need to be optimized for the system of interest. In this paper, we discuss the design and performance of a new endstation at beamline02 B at the Shanghai Synchrotron Radiation Facility for ambient pressure X-ray photoelectron spectroscopy *** system, equipped with the newly developed hightransmission HiPP-3 analyzer, is demonstrated to be capable of efficiently collecting photoelectrons up to 1500 eV from ultrahigh vacuum to ambient pressure of 20 *** spectromicroscopy mode of HiPP-3 analyzer also enables detection of photoelectron spatial distribution with resolution of 2.8 ± 0.3 lm in one dimension. In addition,the designing strategies of systems that allow investigations in phenomena at gas–solid interface and liquid–solid interface will be highlighted through our discussion.

关键词： Ambient pressure XPS Synchrotron Liquid- solid interface Spectromicroscopy

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