A Hierarchical Trust Model for Peer-to-Peer Networks

Trust has become an increasingly important issue given society’s growing reliance on electronic transactions. Peer-to-peer (P2P) networks are among the main electronic transaction environments affected by trust issues due to the freedom and anonymity of peers (users) and the inherent openness of these networks. A malicious peer can easily join a P2P network and abuse its peers and resources, resulting in a large-scale failure that might shut down the entire network. Therefore, a plethora of researchers have proposed trust management systems to mitigate the impact of the problem. However, due to the problem’s scale and complexity, more research is necessary. The algorithm proposed here, HierarchTrust, attempts to create a more reliable environment in which the selection of a peer provider of a file or other resource is based on several trust values represented in hierarchical form. The values at the top of the hierarchical form are more trusted than those at the lower end of the hierarchy. Trust, in HierarchTrust, is generally calculated based on the standard deviation. Evaluation via simulation showed that HierarchTrust produced a better success rate than the well-established EigenTrust algorithm.     

Toward Robust Classifiers for PDF Malware Detection

Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM) based ensemble model with three different kernels. The two models were trained and tested on two different datasets. The experimental results show that the accuracy of both models is approximately 100%, and the robustness against evasive samples is excellent. Further, the robustness of the models was evaluated with malicious PDF documents generated using Mimicus. Both models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability, accuracy, and robustness.     

基于极限学习机的计量测量技术设计

极限学习机是一种针对单隐含层前馈神经网络的新算法,具有训练速度快,泛化性能高等优点。将其应用于软测量技术,避免了传统神经网络高计算复杂度的缺点,可以实现难以直接测量参数的快速获取,在计量测量技术领域有着广阔的应用前景。     

A novel intelligent strategy for improving measurement precision ofFOG

This paper discusses a neural network-based strategy for reducing the existing errors of fiber-optic gyroscope (FOG). A series-single-layer neural network, which is composed of two single-layer networks in series, is presented for eliminating random noises. This network has simpler architecture, faster learning speed, and better performance compared to conventional backpropagation (BP) networks. Accordingly after considering the characteristics of the power law noise in FOG, an advanced learning algorithm is proposed by using the increments of errors in energy function. Furthermore, a radial basis function (RBF) neural network-based method is also posed to evaluate and compensate the temperature drift of FOG. The orthogonal least squares (OLS) algorithm is applied due to its simplicity, high accuracy, and fast learning speed. The simulation results show that the series-single-layer network (SSLN) with the advanced learning algorithm provides a fast and effective way for eliminating different random noises including stable and unstable noises existing in FOG, and the RBF network-based method offers a powerful and successful procedure for evaluating and compensating the temperature drift     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-of-the-art techniques.     

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection.     

A Recommendation System Based on Fusing Boosting Model and DNN Model

In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can construct new features related before and after tree. Convolutional neural networks have a better perception of local features. In this paper, we take advantage of convolutional networks to capture the local features. The features are constructed by the node leaf of gradient boosting decision tree. This paper employs the tree leaf node to mine the user behavior path features, and uses the deep model to extract the user abstract features. Based on a Kaggle competition, our model performs better in the test data than any other model.     

Use of neural networks and expert systems to control a gas/solid sorption chilling machine: Utilisation des réseaux neuronaux et des systèmes experts pour réguler une machine frigorifique à sorption gaz/solide

This works focuses on using neural networks and expert systems to control a gas/solid sorption chilling machine. In such systems, the cold production changes cyclically with time due to the batchwise operation of the gas/solid reactors. The accurate simulation of the dynamic performance of the chilling machine has proven to be difficult for standard computers when using deterministic models. Additionally, some model parameters dynamically change with the reaction advancement. A new modelling approach is presented here to simulate the performance of such systems using neural networks. The backpropagation learning rule and the sigmoid transfer function have been applied in feedforward, full connected, single hidden layer neural networks. Overall control of this system is divided in three blocks: control of the machine stages, prediction of the machine performance and fault diagnosis.     

Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets

In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect the malicious payload and identify their categories with high accuracy, we proposed a packet-based malicious payload detection and identification algorithm based on object detection deep learning network. A dataset of malicious payload on code execution vulnerability has been constructed under the Metasploit framework and used to evaluate the performance of the proposed malware detection and identification algorithm. The experimental results demonstrated that the proposed object detection network can efficiently find and identify malicious payloads with high accuracy.     

基于椭球单元网络的旋转机械多故障同时性诊断

阐述了椭球单元（ＥｌｉｐｓｏｉｄａｌＵｎｉｔ）网络的原理及其结构，研究了网络权重初始化方法和网络的训练算法，借助这种高阶网络泛化的有界性，针对大型旋转机械多故障同时性诊断问题，构造了一种由多个子网络组成的分级诊断网络（ＨＤＡＮＮ）。测试结果表明：用基于椭球单元网络的ＨＤＡＮＮ网络分级诊断策略解决大规模故障诊断问题是合理有效的，且具有较高的诊断精度，可用于旋转机械工况实时监测和诊断场合。     

优化遗传神经网络及其在机械故障诊断中的应用

提出了一种改进的遗传神经网络算法,该算法综合了遗传算法的全局性和神经网络的并行快速性等特点,有利于克服神经网络存在易陷入局部极小和收敛速度慢的问题,达到了优化网络的目的.此算法应用于磨机故障诊断,通过试验得出对故障模式的识别精度较高,具有较好的应用前景.     

基于机器学习的电喷印精度预测方法研究

目的 节省电流体喷射打印精度预测的时间和解决电流体工艺参数的选择问题,达到提高电流体打印的质量和效率的目的。方法 为了对电流体喷射打印精度进行预测,提出有限元模型与机器学习相结合的方法。基于线性回归、支持向量回归和神经网络等机器学习算法建立4种参数与射流直径的关系模型。结果 算法结果表明:支持向量回归和神经网络预测模型的决定系数R²能达到0.9以上,表示模型可信度高;支持向量回归和神经网络预测模型指标都比线性回归预测模型的小。结论 机器学习算法可对电喷印打印精度进行有效预测,预测效率提高了十几倍,节省了精度预测的时间。     

Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods, in this paper a robot detection scheme based on weighted network topology is proposed, which introduces an improved network representation learning algorithm to extract the local structure features of the network, and combined with the graph convolution network (GCN) algorithm based on the graph filter, to obtain the global structure features of the network. An end-to-end semi-supervised combination model (Semi-GSGCN) is established to detect malicious social robots. Experiments on a social network dataset (cresci-rtbust-2019) show that the proposed method has high versatility and effectiveness in detecting social robots. In addition, this method has a stronger insight into robots in social networks than other methods.     

Application of core vector machines for on-line voltage security assessment using a decisiontree-based feature selection algorithm

This study presents a core vector machine (CVM)-based algorithm for on-line voltage security assessment of power systems. To classify the system security status, a CVM has been trained for each contingency. The proposed CVM-based security assessment algorithm has a very small training time and space in comparison with support vector machines (SVMs) and artificial neural networks (ANNs)-based algorithms. The proposed algorithm produces less support vectors (SVs). Therefore is faster than existing algorithms. One of the main points to apply a machine learning method is feature selection. In this study, a new decision tree (DT)-based feature selection algorithm has been presented. The proposed CVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line voltage security assessment. The effectiveness of the proposed feature selection algorithm has also been investigated. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.     

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.     

Using ensemble and metaheuristics learning principles with artificial neural networks to improve due date prediction performance

One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes.     

前馈神经网络混合训练算法及其应用

介绍了单隐层前馈神经网络的混合训练算法(HFM)和正则化混合训练算法(RHFM),然后将该算法应用于UCI数据库上的实际回归例子中,并将其与BP、NNRW以及FM算法进行了比较.仿真实验表明,HFM算法的收敛速度优于其它几种算法,RHFM算法有较好的泛化性能,而NNRW算法在训练时间上占优,尽管如此,HFM算法在时间上还是大大优于BP算法.说明,混合训练算法是一种有效的算法.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Incorporation of liquid-crystal light valve nonlinearities in optical multilayer neural networks

Sigmoidlike activation functions, as available in analog hardware, differ in various ways from the standard sigmoidal function because they are usually asymmetric, truncated, and have a nonstandard gain. We present an adaptation of the backpropagation learning rule to compensate for these nonstandard sigmoids. This method is applied to multilayer neural networks with all-optical forward propagation and liquid-crystal light valves (LCLV) as optical thresholding devices. The results of simulations of a backpropagation neural network with five different LCLV response curves as activation functions are presented. Although LCLV's perform poorly with the standard backpropagation algorithm, it is shown that our adapted learning rule performs well with these LCLV curves.