基于Android网络恶意行为检测系统的应用研究

目前Android系统是当今网络用户最对的应用系统之一,而随着科学技术的发展,对于Android系统的恶意行为软件也逐渐增多,给当前的应用用户的财产以及私人信息安全带来了很大的威胁,严重的迟缓了当前移动通信网络技术以及相关于应用客户端的推广。为此本文根据Android系统的特有机构设计出一种基于Binder信息流的自动检测恶意行为系统,以此来解决对于当前网络安全对于Android系统用户带来的负面影响。根据目前网络中的应用通信信息,检测可能存在的泄露用户信息的应用软件为目标,建立信息矢量图以此来分析当前网络中的恶意行为。通过对软件进行检测,研究可实用性和检测效果,结果显示其识别率可以达到100%,并且软件运行只占有内存的7%,结果可以达到当前的Android用户的使用范围。     

基于Android系统的手机恶意软件检测模型

为提高手机应用软件的安全性,提出一种基于Android系统的手机恶意软件检测模型。模型利用数据挖掘的方法对恶意软件中的敏感API调用进行数据挖掘,进而得到恶意软件检测规则;针对检测规则在检测非恶意软件时,产生较高误报率的问题,设计了加权FP-growth关联规则挖掘算法,算法在数据挖掘的两个步骤中,对敏感API调用加权,利用支持度阈值去除一些出现次数频繁而权重小的规则,降低了非恶意软件的误报率。实验结果表明,模型对恶意软件检测率达到81.7%,非恶意软件的检错率降低到11.3%。     

基于Android平台的USB通信技术研究

针对Android平台的USB通信技术的广阔应用前景,对Android 设备与单片机的USB通信技术进行了研究;搭建了Android设备与单片机进行USB通信的硬件平台;以Android USB Host通信技术为核心,开发了Android端与单片机端的USB应用程序;着重阐述了Android端USB应用程序的完整开发过程,给出了其所需API类的功能描述、使用方法以及程序的开发流程图、主要功能实现的关键代码;对Android开发板与单片机之间的数据通信过程进行了测试,结果表明两者通信正常,实现了Android平台下的USB通信功能。     

基于Android系统控制的蓝牙智能车研究

以Android系统为核心,结合蓝牙技术,通过单片机、电机等外围硬件电路实现智能车的控制的研究.     

基于Android的可视化后厨系统设计

为了适应服务型社会的整体趋势,从服务行业现状与面临的挑战出发,结合互联网发展趋势,设计了一种基于Android的可视化后厨系统,实现了餐饮服务的智能化。首先在介绍系统组成及功能的基础上对系统工作原理进行了阐述,然后介绍了服务器端和客户端的设计方案。本系统经测试可以应用于餐饮服务,可以为消费者提供更加透明的用餐体验。     

一种基于边缘能量图的物体检测方法

物体的变形、边缘中断等因素通常对物体检测的结果影响很大。为了减小这些因素的影响,提出一种新的基于边缘能量图的物体检测方法。边缘能量图在一定程度上反映了物体的边缘出现的概率,便于计算并具有较好的鲁棒性。计算出图像的边缘图像,在此基础上分别计算出模型和测试图像的边缘能量图。基于多分辨率的滑动窗口,在图像中找出测试图像边缘能量图与模型边缘能量图最相似的区域。基于边缘能量图快速得到目标物体的轮廓。实验结果显示,该方法使用较快的计算速度得到了相对较高的检测率。     

一种基于边缘能量图的物体检测方法

物体的变形、边缘中断等因素通常对物体检测的结果影响很大。为了减小这些因素的影响,提出一种新的基于边缘能量图的物体检测方法。边缘能量图在一定程度上反映了物体的边缘出现的概率,便于计算并具有较好的鲁棒性。计算出图像的边缘图像,在此基础上分别计算出模型和测试图像的边缘能量图。基于多分辨率的滑动窗口,在图像中找出测试图像边缘能量图与模型边缘能量图最相似的区域。基于边缘能量图快速得到目标物体的轮廓。实验结果显示,该方法使用较快的计算速度得到了相对较高的检测率。     

基于SoC的海天背景下红外斑状目标检测技术

基于红外目标检测的算法在军事和民用领域发挥着重要的作用,然而,在低信噪比条件下,对于飞机、船舶等红外斑状目标的检测存在难度大,误检率、虚警率高等困难。对此,提出了一种基于机器学习的红外斑状目标的算法,检测像素面积在3×3到100×100的红外目标。算法部分采用了形态学方法对目标进行预提取,并使用HOG特征提取与SVM机器学习分类出真实目标。算法分别在大、中、小三个尺度的目标检测上实现了94.01%、92.86%和92.19%的检测精度。此外,在SoC平台上实现了该算法,在低资源使用率的基础上,算法具有很高的实时性。     

基于ZigBee与Android的边境安防系统研究

结合ZigBee技术,设计了一种低成本、易安装的边境安防系统;在以cc2530为核心的ZigBee节点上搭载震动传感器与CMOS传感器提供报警与图像采集功能,通过ZigBee网络、搭载Android系统的ARM控制板将图片信息通过3G网络上传至服务器,由PC端的客户端软件监听、查看图片信息,完成对指定区域的远程监控;介绍了系统各部分的硬件设计与软件实现,最后给出了整套系统的测试效果;实验结果表明,搭载CMOS传感器的ZigBee安防系统能满足边境环境下的安防系统要求。     

基于TD-LTE和Android技术的智能家居系统设计

随着科技水平和生活水平的提高,人们对居住环境提出了更高的要求,智能家居系统应运而生。本文设计了一种基于TD-LTE和Android系统的智能家居系统,该系统采用各种传感器监测家庭环境,并通过TD-LTE网络将信息传送至用户手机,用户通过Android客户端可以随时随地的掌握家居环境的情况。该系统利用了第四代移动通信技术和移动互联网技术,使得其数据通信速率大大提高,适用群体范围增大,可以远程监控家居设备,提供了安全、舒适和便捷的生活环境,极大的提升了家居生活品质。该系统硬件设备简单可靠,成本较低,易于扩展。     

Malicious URL Detection Based on Associative Classification

Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive information, and secretly monitor targeted computer systems. In this paper, a data mining approach known as classification based on association (CBA) to detect malicious URLs using URL and webpage content features is presented. The CBA algorithm uses a training dataset of URLs as historical data to discover association rules to build an accurate classifier. The experimental results show that CBA gives comparable performance against benchmark classification algorithms, achieving 95.8% accuracy with low false positive and negative rates.     

Using Fractal Function Auto-correlative Method to Confirm Fractal Dimension of Function Graph

The effects of light wave propagating in atmospheric turbulence are investigated experimentally, it is essential to measure the physical variable with time. In order to analyze appropriately these data, it is utilized the fractal function auto-correlative method to describe the fractal dimension of function graph of image-space. The fractal dimension Dg=1.47 of function graph is derived from the power spectral analysis of measuring data. It shows that the Kolmogorov' turbulent model is not completely in accord with actual atmospheric turbulence.     

Using Fractal Function Auto correlative Method to Confirm Fractal Dimension of Function Graph

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Overlapping Community Detection Based on Attribute Augmented Graph

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.     

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.     

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

近红外光谱图像处理的霉变稻谷检测方法

稻谷在储藏和运输过程中,在适宜的温湿度环境下极易发生霉变,导致大量的粮食浪费和巨大的经济损失,进而影响粮食安全.为解决传统的稻谷霉变检测存在的繁琐且耗时较长等不足,提出了基于近红外光谱图像处理和神经网络的稻谷霉变程度检测方法.首先,通过农业多光谱相机(Sequoia)和固定光源等设备,构建了霉变稻谷近红外图像数据采集平...     

基于特征投影图的小麦近红外光谱变量选择方法研究

为了简化模型,提高模型预测精度,利用特征投影图(LPG)进行变量选择。对原始光谱进行连续小波变换(CWT),利用主成分分析(PCA)得到LPG,假定LPG中共线性光谱变量对建模作用相同,选出少数特征光谱变量建立预测模型,所得模型预测均方根误差(RMSEP)为0.345 4,优于其他建模方法,研究结果表明,LPG变量选择可有效简化近红外光谱模型,提高模型预测精度。