基于1D CNN与XGBoost的恶意代码纹理检测

恶意代码数量已经呈现爆炸式增长,对于恶意代码的检测防护显得尤为重要.近几年,基于深度学习的恶意代码检测方法开始出现,基于此,提出一种新的检测方法,将恶意代码二进制文件转化为十进制数组,并利用一维卷积神经网络(1 Dimention Convolutional Neural Networks,1D CNN)对数组进行分类和识别.针对代码家族之间数量不平衡的现象,该算法选择在分类预测上表现良好的XGBoost,并对Vision Research Lab中的25个不同恶意软件家族的9458个恶意软件样本进行了实验.实验结果表明,所提的方法分类预测精度达到了97％.     

Pricing and personal data collection strategies of online platforms in the face of privacy concerns

Electronic Commerce Research - We consider a platform providing free content for users and earning profit from the sale of advertising. The platform can collect and analyze personal data to...     

Revealing cracking and breakage behaviours of gibbsite particles

Mitigating gibbsite particle cracking and breakage during industrial alumina production can increase the quality of smelter grade alumina product by reducing the ultrafine particle content. Therefore, it is essential to investigate the particle cracking during static calcination and the breakage of calcined gibbsite particles under external force. In this work, we investigated the impact of the calcination ramping rate and the crystallite size on gibbsite particle cracking during static calcination. A slow ramping rate and a large pristine crystallite size tend to increase particle cracking. Apart from the study of particle cracking behaviour, we also investigated the breakage of calcined gibbsite particle under external force. Cracks on the particle surface can initiate breakage within the crystallite and along the grain boundary under external force. The breakage within crystallite occurs as the cleavage of the crystallite, while the breakage along the grain boundary leads to the shedding of a whole crystallite. We further explored the factors influencing the strength of calcined gibbsite particles. With increasing calcination temperature, the strength of particle increases when gibbsite converts to boehmite, and then decreases when boehmite converts into amorphous alumina. Particles containing smaller crystallites and calcined with fast ramping rates exhibit higher resistance to breakage.     

Polyethylene oxide-polypropylene oxide -polyethylene oxide derived porous carbon materials with different molecular weights as ORR catalyst in alkaline electrolytes

Polyethylene oxide (PEO)-polypropylene oxide (PPO)-polyethylene oxide block copolymer having different molecular weights are used as precursors of carbon materials to prepare Hollow -Derivatives carbon material as an electrocatalyst through block copolymer self-assembly. The composition and microstructure of the prepared catalysts are shown by Raman spectroscopy, X-ray diffraction (XRD), Test of nitrogen adsorption and desorption curves, High resolution transmission electron microscopy (HR-TEM) and scanning electron microscopy (HR-SEM). Oxygen was passed into alkaline electrolyte solution until the solution reached saturation state. With molecular weight increasing, the obtained sample gradually changed from block to hollow and spherical. When the molecular weight was 12600 g mol^?1, the evenly hollow carbon nanocages was acquired (C-12600). In O₂ saturated alkaline electrolyte (0.1 M KOH solution), C-12600's limited current density，half-wave potential and initial potential are 5.23 mA cm^?2@0.4 V, 0.72 V and 0.81 V, respectively. And most important is that half-wave potential and onset potential have barely change after 2000 cycles of cyclic voltammetry. As a result, the porous carbon materials exhibited excellent electrocatalytic activity while maintaining high stability in alkaline KOH solution.     

"砼"趣 ——记清水混凝土建筑荣成青少年活动中心设计施工点滴

从清水混凝土材料特点、空间适用性、肌理、细节和工艺等方面总结了荣成青少年活动中心复杂异形清水混凝土工程从设计到建造的全过程,展现了本土设计理念和地域环境的融合、创新型施工工艺的成功应用及项目最终呈现的高品质完成度.     

Threshold privacy-preserving cloud auditing with multiple uploaders

International Journal of Information Security - Data integrity is a critical security issue in cloud storage. The data integrity checking schemes by a third-party auditor (TPA) have attracted a lot...