Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

Comparison of Cloud Type Classification with Split Window Algorithm Based on Different Infrared Band Combinations of Himawari-8 Satellite

Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus

Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

Scheme for Designing the 1-D Convolution Window of Gabor Filter

A scheme for designing one-dimensional (1-D) convolution window of the circularly symmetric Gabor filter which is directly obtained from frequency domain is proposed. This scheme avoids the problem of choosing the sampling frequency in the spatial domain, or the sampling frequency must be determined when the window data is obtained by means of sampling the Gabor function, the impulse response of the Gabor filter. In this scheme, the discrete Fourier transform of the Gabor function is obtained by discretizing its Fourier transform. The window data can be derived by minimizing the sums of the squares of the complex magnitudes of difference between its discrete Fourier transform and the Gabor function's discrete Fourier transform. Not only the full description of this scheme but also its application to fabric defect detection are given in this paper. Experimental results show that the 1-D convolution windows can be used to significantly reduce computational cost and greatly ensure the quality of the Gabor filters. So this scheme can be used in some real-time processing systems.

A Test Method for the Static/Moving State of Targets Applied to Airport Surface Surveillance MLAT System

Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.

徽州木雕门窗纹饰在现代服饰设计中的应用

为更好地传承和创新中华优秀传统文化,探究徽州木雕门窗纹饰在现代服饰设计中的应用方式。通过采集、整理和归纳徽州木雕门窗的题材内容,分析徽州木雕门窗纹饰特色,提炼其艺术特征与文化价值。对典型徽州木雕门窗纹饰进行深入分析,挖掘其与服饰设计的美学契合点,探讨徽州木雕门窗纹饰在现代服饰中形态设计与色彩转化的方法,并在此基础上完成《徽迹》系列服饰设计的创新实践,为徽州木雕艺术的活态传承与创新发展提供可借鉴的思路和方法。

基于机器视觉的木窗双端铣削加工尺寸测量方法

木窗是一种以木材或木质复合材料为主要构件的门窗产品,具有良好的生态性能和美观效果,适用于多种建筑形式和风格,其中木窗尺寸是衡量木窗加工是否合格的重要指标。对于传统木窗双端铣削加工中人工测量尺寸方式存在的精度低、效率低等问题,提出一种基于机器视觉的木窗双端铣削加工尺寸测量方法,以期提高尺寸测量精度及加工效率。该方法针对木窗厚度引起的透视效应,提出一种物平面提升法,以消除透视投影带来的误差。首先对木窗图像采取灰度化、平滑去噪、图像增强及轮廓分割等举措,完成图像预处理,提取出木窗内外轮廓区域。对轮廓区域应用Canny算子获取木窗像素级轮廓。通过优化的Zernike矩亚像素边缘提取算法对木窗像素级边缘进行更精确的定位,得到亚像素级轮廓坐标。通过最小二乘法联合RANSAC算法对亚像素轮廓坐标进行拟合,得到拟合轮廓及角点坐标,并使用透视矫正模型计算出木窗尺寸。实验利用3种厚度规格相同但尺寸不同的松木材质矩形木窗,分别测量其内框和外框的边框尺寸及对角线尺寸,并与对应的实际物理尺寸对比,验证了所提木窗尺寸测量方法的检测精度。研究结果表明,所提方法与实际物理尺寸值相比,其绝对误差范围在±0.12 mm之内,相对误差在±0.1%之内,且效率及精度高,可以满足对木窗的在线尺寸检测。

YOLOv5-LR:一种遥感影像旋转目标检测模型

真实遥感图像中,目标呈现任意方向分布的特点,原始YOLOv5网络存在难以准确表达目标的位置和范围、以及检测速度一般的问题。针对上述问题,提出一种遥感影像旋转目标检测模型YOLOv5-Left-Rotation,首先利用Transformer自注意力机制,让模型更加注意感兴趣的目标,并且在图像预处理过程中采用Mosaic数据增强,对后处理过程使用改进后的非极大值抑制算法Non-Maximum Suppression。其次,引入角度损失函数,增加网络的输出维度,得到旋转矩形的预测框。最后,在网络模型的浅层阶段,增加滑动窗口分支,来提高大尺寸遥感稀疏目标的检测效率。实验数据集为自制飞机数据集CASIA-plane78和公开的舰船数据集HRSC2016,结果表明,改进旋转目标检测算法相比于原始YOLOv5网络的平均精度提升了3.175%,在吉林一号某星推扫出的大尺寸多光谱影像中推理速度提升了13.6%,能够尽可能地减少冗余背景信息,更加准确检测出光学遥感图像中排列密集、分布无规律的感兴趣目标的区域。

基于Ansys Workbench的双桁架码垛机械手动态性能分析

为了提高定制化门窗材码垛分类的效率,以双桁架码垛机械手为基础对静态、动态性能进行分析以满足定制化门窗材的码垛效率。针对双桁架码垛机械手在分类码垛过程中存在多种运动状态,在双桁架码垛机械手静态性能满足刚度和强度的基础上对其动态特性进行分析,动态特性分析采用模态和谐波响应方法。通过对双桁架码垛机械手的动态分析,得到了其在两个机械手不同频段时X、Y、Z向的位移量,通过响应曲线明确了频段对位移量的影响规律。两个机械手的响应曲线在规避特殊频率段以提高码垛效率、促进分类码垛的智能化发展具有重要意义。

运河文化影响下的临清中洲古城民居木质门窗技艺研究

门窗作为传统民居建筑的重要组成部分,具有实用和审美的双重价值,蕴含着独特的地域民俗文化和乡土审美观念。本文以临清中洲古城民居正房明间的木质门窗为研究对象,对运河文化影响下的临清民居形式进行概述,从装饰艺术角度阐释门窗纹样承载的运河文化内涵,并总结门窗建造过程中所使用的榫卯技艺,以期为我国传统民居建筑门窗的保护与发展提供参考。

基于深度学习的桥梁表观裂缝检测算法研究

针对在复杂背景条件下难以直接对桥梁表观裂缝进行检测的问题,文章提出一种基于深度学习的桥梁表观裂缝检测算法。首先利用滑动窗口算法将采集到的桥梁表观裂缝图像切分为小尺寸的桥梁裂缝面元图像和桥梁背景面元图像,并根据对面元图像的分析,提出一种基于Inception网络和残差网络(ResNet)的桥梁裂缝分类模型,用于桥梁裂缝面元和桥梁背景面元的识别;然后结合桥梁裂缝分类模型与滑动窗口算法对桥梁表观裂缝图像进行检测;最后利用数字图像处理技术测量裂缝宽度。结果表明:该文算法对桥梁表观裂缝有超过99%的分类精度,可满足实际工程需要;实现了裂缝的提取并能准确地定位出裂缝在图像中的位置;根据成像原理能测量出裂缝宽度。与传统的深度学习模型相比,该模型拥有更高的执行效率,可用于大规模检测,更易于应用在桥梁健康检测中。

塑料门窗的特点及在建筑节能中的应用

建筑能耗在总能源能耗中的高占比使得人们看到建筑节能的潜力。门窗材料的选择极大地影响了建筑节能的效果,本文综合分析了塑料门窗在建筑节能中的发展历程以及特点,最后提出优化其未来发展的路径,以期能够在绿色节能发展的背景下充分发挥塑料门窗在建筑领域的优势,推动我国建筑节能向着良性的方向发展。

结合局部纹理特征滤波的海天线检测方法

针对视频监测下海天线检测图像易出现的海天对比度低、海上物体、海雾、海浪及云层等干扰条件引起的图像模糊和海天线遮挡问题,提出一种基于偏心邻域的灰度共生矩阵对比度滤波的梯度域下海天线检测方法。方法设计使用上下两方向偏心盒状滤波器获取图像的对比度梯度值图,利用滑动窗对分块图像搜索梯度极大值候选位置,根据设计策略通过构建海天线参数的概率统计模型提取拟合直线作为检测结果。仿真实验表明,本文方法能提升视觉图像下含多景物等干扰的复杂场景海天线检测的准确率,与同类方法相比提升7.69%。

基于Attention-BiTCN的网络入侵检测方法

为解决网络入侵检测领域多分类准确率不高的问题,文章根据网络流量数据具有时序特征的特点,提出一种基于注意力机制和双向时间卷积神经网络(BiDirectional Temporal Convolutional Network,BiTCN)的网络入侵检测模型。首先,该模型对数据集进行独热编码和归一化处置等预处理,解决网络流量数据离散性强和标度不统一的问题;其次,将预处理好的数据经双向滑窗法生成双向序列,并同步输入Attention-Bi TCN模型中;然后,提取双向时序特征并通过加性方式融合,得到时序信息被增强后的融合特征;最后,使用Softmax函数对融合特征进行多种攻击行为检测识别。文章所提模型在NSL-KDD和UNSW-NB15数据集上进行实验验证,多分类准确率分别达到99.70%和84.07%,优于传统网络入侵检测算法,且比其他深度学习模型在检测性能上有显著提升。

基于滑动窗和多块卷积自编码器的故障检测

为了进一步提升故障检测性能,充分挖掘时序和隐含特征信息,提出一种基于卷积自编码器的故障检测方法。在对原始信息集进行建模的基础上增加了对累计信息与变化率信息的建模,以增强对隐含信息的挖掘;对重构的3个信息集进行滑动窗采样,基于卷积自编码器进行时序特征提取和建模;将卷积自编码器的决策结果进行贝叶斯融合得到统计量,并用核密度估计的方法确定控制限从而进行故障检测。将该方法进行数值仿真并应用于TE过程,仿真结果验证了所提方法的有效性和检测性能。

定制化被动式木门窗材柔性分类智能控制系统设计

针对行业内被动式木门窗扇及窗框等集成材根据订单要求完成定长截断、四面刨铣后,需要对尺寸各异的门窗材进行优化分类堆叠的现状设计柔性分类智能远程控制系统。通过Modbus通信协议及多种传感器协同完成现场数据的实时采集,运用消息队列遥测传输(Message Queuing Telemetry Transport,MQTT)协议和通信模块将采集的数据上传至云平台,终端用户通过手机App或Web端实时查看系统运行数据及进行远程操控,实现基于物联网的远程监控与报警。结果显示,设计的控制系统尺寸信息采集系统的鲁棒性较高,系统故障率低,同时提高分类堆叠效率。控制系统的设计实现了门窗材定制化分类堆叠的自动化、数据实时采集与传输以及远程监控,为实木加工的智能化与信息化应用提供了技术支撑。