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计算摄像学专题概述
戴琼海
Frontiers of Information Technology & Electronic Engineering. 2017, 18 (9): 1205-1206.
https://doi.org/10.1631/FITEE.1730000
计算摄像学是为了突破传统成像技术局限而诞生的新兴领域,在研制新型民用相机和科学观测设备方面均有巨大潜力。过去十余年,计算摄像学在计算机视觉、图形学、光学以及信号处理等多学科交叉领域开启了新的前沿,学术界和工业界共同见证了此领域的一系列创新和重大进展。可以说,该领域充满机遇和挑战。为此,我们出版此计算摄像学专题,以推动此领域的研究。 依据视觉信号的维度,我们将计算摄像学研究分为空间结构成像、多光谱采集、相位成像以及瞬态信息记录等。计算摄像学研究也受益于光电科技的发展。为促进读者对此领域最新进展的全面了解,此专题包括8篇邀请文章,其中7篇综述——包括1篇该领域总体概述和6篇关于视觉信号不同维度计算成像方法进展的调查——以及1篇关于片上光互连近期进展的研究论文。
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Automatic malware classification and new malware detection using machine learning
Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG
Frontiers of Information Technology & Electronic Engineering. 2017, 18 (9): 1336-1347.
https://doi.org/10.1631/FITEE.1601325
The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.
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20篇文章
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