Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model—A Case Study of Chennai Metropolitan Area, Tamil Nadu, India

Urban Growth Models (UGMs) are very essential for a sustainable development of a city as they predict the future urbanization based on the present scenario. Neural Network based Cellular Automata models have proved to predict the urban growth more close to reality. Recently, deep learning based techniques are being used for the prediction of urban growth. In this current study, urban growth of Chennai Metropolitan Area (CMA) of 2017 was predicted using Neural Network based Cellular Automata (NN-CA) model and Deep belief based Cellular Automata (DB-CA) model using 2010 and 2013 urban maps. Since the study area experienced congested type of urban growth, “Existing Built-Up” of 2013 alone was used as the agent of urbanization to predict urban growth in 2017. Upon validating, DB-CA model proved to be the better model, as it predicted 524.14 km2 of the study area as urban with higher accuracy (kappa co-efficient: 0.73) when compared to NN-CA model which predicted only 502.42 km2 as urban (kappa co-efficient: 0.71), while the observed urban cover of CMA in 2017 was 572.11 km2. This study also aimed at analyzing the effects of different types of neighbourhood configurations (Rectangular: 3 × 3, 5 × 5, 7 × 7 and Circular: 3 × 3) on the prediction output based on DB-CA model. To understand the direction and type of the urban growth, the study area was divided into five distance based zones with the State Secretariat as the center and entropy values were calculated for the zones. Results reveal that Chennai Corporation and its periphery experience congested urbanization whereas areas away from the Corporation boundary follow dispersed type of urban growth in 2017.

Prediction Model of Aircraft Icing Based on Deep Neural Network

Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.

基于Deep Belief Nets的中文名实体关系抽取

关系抽取是信息抽取的一项子任务,用以识别文本中实体之间的语义关系.提出一种利用DBN(deepbelief nets)模型进行基于特征的实体关系抽取方法,该模型是由多层无监督的RBM(restricted Boltzmann machine)网络和一层有监督的BP(back-propagation)网络组成的神经网络分类器.RBM网络以确保特征向量映射达到最优,最后一层BP网络分类RBM网络的输出特征向量,从而训练实体关系分类器.在ACE04语料上进行的相关测试,一方面证明了字特征比词特征更适用于中文关系抽取任务;另一方面设计了3组不同的实验,分别使用正确的实体类别信息、通过实体类型分类器得到实体类型信息和不使用实体类型信息,用以比较实体类型信息对关系抽取效果的影响.实验结果表明,DBN非常适用于基于高维空间特征的信息抽取任务,获得的效果比SVM和反向传播网络更好.

面向入侵检测系统的Deep Belief Nets模型

连续的网络流量会导致海量数据问题,这为入侵检测提出了新的挑战。为此,提出一种面向入侵检测系统的深度信念网络(deep belief nets oriented to the intrusion detection system,DBN-IDS)模型。首先,通过无监督的、贪婪的算法自底向上逐层训练每一个受限玻尔兹曼机(restricted Boltzmann machine,RBM)网络,使得大量高维、非线性的无标签数据映射为最优的低维表示;然后利用带标签数据被附加到顶层,通过反向传播(back propagation,BP)算法自顶向下有监督地对RBM网络输出的低维表示进行分类,并同时对RBM网络进行微调;最后,利用NSLKDD数据集对模型参数和性能进行了深入的分析。实验结果表明,DBN-IDS分类效果优于支持向量机(support vector machine,SVM)和神经网络(neural network,NN),适用于高维、非线性的海量入侵数据的分类处理。

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.

Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performance of incorporating optimization techniques for medical image classification is better than conventional techniques.

Adaptive Deep Learning Model for Software Bug Detection and Classification

Software is unavoidable in software development and maintenance.In literature,many methods are discussed which fails to achieve efficient software bug detection and classification.In this paper,efficient Adaptive Deep Learning Model(ADLM)is developed for automatic duplicate bug report detection and classification process.The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory(CRF-LSTM)and Dingo Optimizer(DO).In the CRF,the DO can be consumed to choose the efficient weight value in network.The proposed automatic bug report detection is proceeding with three stages like pre-processing,feature extraction in addition bug detection with classification.Initially,the bug report input dataset is gathered from the online source system.In the pre-processing phase,the unwanted information from the input data are removed by using cleaning text,convert data types and null value replacement.The pre-processed data is sent into the feature extraction phase.In the feature extraction phase,the four types of feature extraction method are utilized such as contextual,categorical,temporal and textual.Finally,the features are sent to the proposed ADLM for automatic duplication bug report detection and classification.The proposed methodology is proceeding with two phases such as training and testing phases.Based on the working process,the bugs are detected and classified from the input data.The projected technique is assessed by analyzing performance metrics such as accuracy,precision,Recall,F_Measure and kappa.

Hierarchical Representations Feature Deep Learning for Face Recognition

Most modern face recognition and classification systems mainly rely on hand-crafted image feature descriptors. In this paper, we propose a novel deep learning algorithm combining unsupervised and supervised learning named deep belief network embedded with Softmax regress (DBNESR) as a natural source for obtaining additional, complementary hierarchical representations, which helps to relieve us from the complicated hand-crafted feature-design step. DBNESR first learns hierarchical representations of feature by greedy layer-wise unsupervised learning in a feed-forward (bottom-up) and back-forward (top-down) manner and then makes more efficient recognition with Softmax regress by supervised learning. As a comparison with the algorithms only based on supervised learning, we again propose and design many kinds of classifiers: BP, HBPNNs, RBF, HRBFNNs, SVM and multiple classification decision fusion classifier (MCDFC)—hybrid HBPNNs-HRBFNNs-SVM classifier. The conducted experiments validate: Firstly, the proposed DBNESR is optimal for face recognition with the highest and most stable recognition rates;second, the algorithm combining unsupervised and supervised learning has better effect than all supervised learning algorithms;third, hybrid neural networks have better effect than single model neural network;fourth, the average recognition rate and variance of these algorithms in order of the largest to the smallest are respectively shown as DBNESR, MCDFC, SVM, HRBFNNs, RBF, HBPNNs, BP and BP, RBF, HBPNNs, HRBFNNs, SVM, MCDFC, DBNESR;at last, it reflects hierarchical representations of feature by DBNESR in terms of its capability of modeling hard artificial intelligent tasks.

基于粒子群优化深度置信网络的气体绝缘金属封闭开关设备局部放电模式识别

气体绝缘金属封闭开关设备(gas insulated metal-enclosed switchgear,GIS)局部放电模式识别是其绝缘缺陷诊断和状态评估的重要部分,为实现放电类型的准确识别,提出了一种基于粒子群优化(particle swarm optimization,PSO)深度置信网络(deep belief network,DBN)的局部放电模式识别方法。该方法通过PSO算法对DBN网络的权值参数进行优化,提高网络对局部放电特征的学习能力。首先,选取现场多平台的4种GIS局部放电类型监测数据组成样本集,用于对所提方法进行分析;其次,用改进的PSO算法结合样本数据确定DBN网络的初始最优权值参数,建立初始DBN网络;然后,利用训练样本对初始DBN网络进行训练,得到局部放电识别模型。最后,基于渤海油田岸电海上动力平台GIS的局部放电数据,采用多种不同局部放电识别模型对数据样本进行算例分析,结果表明:所提的PSO-DBN模型可有效识别GIS设备局部放电类型,相较于传统的DBN网络、多层前馈神经网络(back propagation,BP)、支持向量机(support vector machine,SVM)和卷积神经网络(convolutional neural networks,CNN)具有更高的准确识别率。

基于结构动力特性的结构损伤深度置信网络分层识别研究

为了高效准确地识别结构损伤,将机器学习和智能算法相结合,提出一种基于结构动力特性的结构损伤深度置信网络分层识别方法,分层依次识别损伤位置与损伤程度。为识别损伤位置,利用结构前3阶竖向振动频率和单节点3阶模态位移建立六元向量,以此六元向量作为输入参数,通过深度置信网络识别损伤位置;为识别损伤程度,分别采用前3阶竖向振动固有频率和模态位移或6节点模态曲率差为参数输入深度置信网络识别损伤程度,并以简支梁为模型进行验证。结果表明:识别损伤位置时,即使噪声程度达到10%,仍可准确识别损伤位置;识别损伤程度时,基于6节点模态曲率差的深度置信网络抗噪性强,在15%噪声水平下对损伤程度预测最大相对误差不超过5.08%,均方差为0.4878。与BP神经网络相比,无噪声时,BP神经网络的预测能力优于深度置信网络;相同噪声水平下,深度置信网络的预测能力明显优于BP神经网络,体现了基于结构动力特性的结构损伤深度置信网络分层识别方法鲁棒性强,识别结果精度高。

Prediction of Primary Frequency Regulation Capability of Power System Based on Deep Belief Network

The primary frequency response ability plays a crucial role in the rapid recovery and stability of the power grid when the grid is disturbed to generate a power imbalance.In order to predict the primary frequency control ability of power system,a new model is proposed based on deep belief networks.The key feature of the proposed model lies in the fact that it considers three key factors,i.e.,disturbance information,system state feature,and unit operation mode.Through this way,it predicts the primary frequency control ability of the power system accurately.The simulation results on real power system data verify the feasibility and accuracy of the proposed model.

深度学习研究综述

深度学习是一类新兴的多层神经网络学习算法,因其缓解了传统训练算法的局部最小性,引起机器学习领域的广泛关注。首先论述了深度学习兴起渊源,分析了算法的优越性,并介绍了主流学习算法及应用现状,最后总结了当前存在的问题及发展方向。

深度学习理论及其在电机故障诊断中的研究现状与展望

电机已经被广泛应用到人们生产生活的各个领域中,电机的故障不但会对电机本身会造成损害,甚至会引发经济损失、人员伤亡等各种问题。因此,将及时且高效的故障诊断技术应用于电机有着重要意义。相比较传统故障诊断技术而言,深度学习因其更强大更复杂的数据表达能力,已被应用于电机故障诊断领域,并取得了一定的研究成果。因此,介绍了深度置信网络(DBN)、自编码网络(AE)、卷积神经网络(CNN)和循环神经网络(RNN)这四类经典的深度学习模型,并总结了这四类模型在电机故障诊断中的应用。最后对深度学习在电机故障诊断领域中所面临的问题和挑战进行了总结和展望。

深度学习方法研究新进展

本文依据模型结构对深度学习进行了归纳和总结,描述了不同模型的结构和特点。首先介绍了深度学习的概念及意义,然后介绍了4种典型模型:卷积神经网络、深度信念网络、深度玻尔兹曼机和堆叠自动编码器,并对近3年深度学习在语音处理、计算机视觉、自然语言处理以及医疗应用等方面的应用现状进行介绍,最后对现有深度学习模型进行了总结,并且讨论了未来所面临的挑战。

基于微调优化的深度学习在语音识别中的应用

针对深度学习模型在对小样本进行训练时会出现过拟合现象,提出随机退出优化方法和随机下降连接优化方法.这两种方法针对深度学习模型的微调阶段进行改进,最大限度减少由于训练数据量较少使得深层网络模型训练出现过拟合现象,并且使权值的更新过程更具有独立性,而不是依赖于有固定关系的隐层节点间的作用,同时可以降低识别错误率.对自建孤立语音词汇库进行了训练和识别,结果表明,在深度信念网络的基础上引入随机退出优化方法和随机下降连接优化方法可以提升识别率,缓解过拟合现象.

基于极限学习的深度学习算法

在使用传统的全局优化方法对整个深度信念网(DBN)进行优化的过程中需要大量的时间,且基于梯度的优化方法易陷入局部最优。为加快DBN的训练速度,将极限学习机(ELM)运用到DBN模型的训练中。分别将传统DBN与改进后的IDBN算法应用在手写体数据集MNIST、Binary Alphadigits数据集和USPS数据集上,实验结果表明,改进后的IDBN算法能够保证已有的学习准确性,提高学习的速度。

深度学习:开启大数据时代的钥匙

随着大数据时代的到来,基于深度学习技术的机器学习方法被用于有效地分析和处理这些数据。本文首先概述了深度学习技术的由来,对比了浅层结构与深度结构模型的差异,分析了深度结构模型在大数据应用中的优势;认为深度学习取得成功的条件是,大规模训练数据集的支撑、先进的硬件平台支持、新的优化技术的提出;基于计算机视觉应用,从有监督特征学习和无监督特征学习两个方面分别介绍了当前深度学习研究的现状和典型的深度结构模型的基本原理和主要应用;针对当前深度学习的发展现状,总结了深度学习研究存在的挑战和未来的研究方向。

多模深度卷积神经网络应用于视频表情识别

由于视频中的手工特征和主观情感之间的直接相关性很小,识别视频序列中的面部表情是一项很有挑战性的任务,为了克服这个缺陷,有效提高视频中的人脸表情识别性能。本方法采用两个深度卷积神经网络,即空间卷积神经网络和时间卷积神经网络,用于视频中的时空表情特征学习。其中,空间卷积神经网络用于提取视频中每一帧静态的表情图像的空间信息特征,而时间卷积神经网络用于从视频中多帧表情图像的光流信息中提取动态信息特征。然后,将这两个深度卷积神经网络学习到的时空特征进行基于深度信念网络(DBN)的特征层融合,输入到支持向量机实现视频中的人脸表情分类任务。在公共的RML和BAUM-1s视频情感数据集的测试结果表明,该方法分别取得了71.06%和52.18%的正确识别率,明显优于现有文献报导的结果。多模深度卷积神经网络的人脸表情识别方法能提高视频中人脸表情的识别性能。

随机子空间深度回归方法在紫外光谱水质分析中的应用

紫外光谱法进行TOC浓度分析时存在数量多、维数高等问题。针对此问题,提出了一种基于随机子空间深度回归的分析方法。该算法首先采集TOC标准溶液的紫外光谱数据进行预处理,得到吸光度数据;然后在高维数据空间随机选取低维子空间来构造不同的特征子集,并采用深度信念网络对各子集进行特征提取;最后将得到的低维特征进行组合后送入BP神经网络中进行训练,建立TOC浓度反演模型。在构建的水质分析平台上的实验结果表明,提出的基于随机子空间深度回归的水质分析方法对每种TOC浓度反演结果的相对误差均在1%以内,且反演结果的稳定性和准确性也要优于常规的水质分析方法。