Offline/realtime traffic classification using semi-supervised learning

Identifying and categorizing network traffic by application type is challenging because of the continued evolution of applications, especially of those with a desire to be undetectable. The diminished effectiveness of port-based identification and the overheads of deep packet inspection approaches motivate us to classify traffic by exploiting distinctive flow characteristics of applications when they communicate on a network. In this paper, we explore this latter approach and propose a semi-supervised classification method that can accommodate both known and unknown applications. To the best of our knowledge, this is the first work to use semi-supervised learning techniques for the traffic classification problem. Our approach allows classifiers to be designed from training data that consists of only a few labeled and many unlabeled flows. We consider pragmatic classification issues such as longevity of classifiers and the need for retraining of classifiers. Our performance evaluation using empirical Internet traffic traces that span a 6-month period shows that: (1) high flow and byte classification accuracy (i.e., greater than 90%) can be achieved using training data that consists of a small number of labeled and a large number of unlabeled flows; (2) presence of “mice” and “elephant” flows in the Internet complicates the design of classifiers, especially of those with high byte accuracy, and necessitates the use of weighted sampling techniques to obtain training flows; and (3) retraining of classifiers is necessary only when there are non-transient changes in the network usage characteristics. As a proof of concept, we implement prototype offline and realtime classification systems to demonstrate the feasibility of our approach.     

Dynamic route guidance algorithm based algorithm based on artificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.     

Dynamic route guidance algorithm based on artificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems, this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the metaphor mechanism of vertebrate immune systems. This algorithm, applied to the urban traffic network model established by the node-expanding method, can expediently realize K-shortest paths search in the urban traffic guidance systems. Because of the immune memory and global parallel search ability from artificial immune systems, K-shortest paths can be found without any repeat, which indicates evidently the superiority of the algorithm to the conventional ones. Not only does it perform a better parallelism, the algorithm also prevents premature phenomenon that often occurs in genetic algorithms. Thus, it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications. A case study verifies the efficiency and the practicability of the algorithm aforementioned.     

人工免疫系统的基本理论及其应用

介绍了生物免疫系统的工作机制与特性及人工免疫算法,且将人工免疫系统与其他智能方法进行比较.还归纳了人工免疫系统的工程应用并对人工免疫系统需深入研究的方向进行了展望.     

RFID reader anti-collision algorithm using adaptive hierarchical artificial immune system

In a radio-frequency identification (RFID) system, if a group of readers transmit and/or receive signals at the same time, they will probably interfere with each other, so that the resulting reader collision problems (e.g., reader-to-reader collision, reader-to-tag collision) will happen. Generally, the reader-to-reader collision can be mitigated by maximizing the tag identification capability, which is related to frequencies and time slots, so it can be transferred as a resource scheduling problem by optimizing the tag identification capability. Artificial immune system is an emerging heuristic evolutionary method which is widely applied to scientific researches and engineering problems. This paper formulates a reader-to-reader anti-collision model from the viewpoint of resource scheduling and proposes an adaptive hierarchical artificial immune system (RA-AHAIS) to solve this optimization problem. A series of simulation experiments are arranged to analyzing the effects of time slots and frequency. Further simulation experiments are made to compare such performance indices as number of identified tags between the proposed RA-AHIAS and the other existing algorithms. The numerical simulation results indicate that this proposed RA-AHAIS is an effective reader-to-reader anti-collision method, and performs better in tag identification capability and computational efficiency than the other methods, such as genetic algorithm (RA-GA), particle swarm optimization (RA-PSO) and artificial immune system for resource allocation (RA-AIS).     

Diagnosis of chest diseases using artificial immune system

Chest diseases are one of the greatest health problems for people living in the developing world. Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are most important chest diseases and these are very common illnesses in Turkey. In this paper, a study on chest diseases diagnosis was realized by using artificial immune system. We obtained the classification accuracy with artificial immune system 93.84%. The result of the study was compared with the results of the previous similar studies reported focusing on chest diseases diagnosis. The chest diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports.     

Multi-class pattern classification using neural networks

Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and P-against-Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.     

Symbiotic artificial immune system

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.     

An artificial immune network approach to multi-sensor land use/land cover classification

An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification. Major improvements of OPTINC compared to a typical immune network-based classification model (aiNet) include (1) preservation of the best antibodies of each land cover class from the antibody population suppression, which ensures that each land cover class is represented by at least one antibody; (2) mutation rates being self-adaptive according to the model performance between training generations, which improves the model convergence; and (3) incorporation of both Euclidean distance and spectral angle mapping distance to measure affinity between two feature vectors using a genetic algorithm-based optimization, which helps the model to better discriminate LULC classes with similar characteristics. OPTINC was evaluated using two sites with different remote sensing data: a residential area in Denver, CO with high-spatial resolution QuickBird image and LiDAR data, and a suburban area in Monticello, UT with HyMap hyperspectral imagery. A decision tree, a multilayer feed-forward back-propagation neural network, and aiNet were also tested for comparison. Classification accuracy, local homogeneity of classified images, and model sensitivity to training sample size were examined. OPTINC outperformed the other models with higher accuracy and more spatially cohesive land cover classes with limited salt-and-pepper noise. OPTINC was relatively less sensitive to training sample size than the neural network, followed by the decision tree.     

Automatic knot adjustment using an artificial immune system for B-spline curve approximation

Reverse engineering transforms real parts into engineering concepts or models. First, sampled points are mapped from the object’s surface by using tools such as laser scanners or cameras. Then, the sampled points are fitted to a free-form surface or a standard shape by using one of the geometric modeling techniques. The curves on the surface have to be modeled before surface modeling. In order to obtain a good B-spline curve model from large data, the knots are usually respected as variables. A curve is then modeled as a continuous, nonlinear and multivariate optimization problem with many local optima. For this reason it is very difficult to reach a global optimum. In this paper, we convert the original problem into a discrete combinatorial optimization problem like in Yoshimoto et al. [F. Yoshimoto, M. Moriyama, T. Harada, Automatic knot placement by a genetic algorithm for data fitting with a spline, in: Proceedings of the International Conference on Shape Modeling and Applications, IEEE Computer Society Press, 1999, pp. 162-169] and Sarfraz et al. [M. Sarfraz, S.A. Raza, Capturing outline of fonts using genetic algorithm and splines, in: Fifth International Conference on Information Visualisation (IV’01), 2001, pp. 738-743]. Then, we suggest a new method that solves the converted problem by artificial immune systems. We think the candidates of the locations of knots as antibodies. We define the affinity measure benefit from Akaike’s Information Criterion (AIC). The proposed method determines the appropriate location of knots automatically and simultaneously. Furthermore, we do not need any subjective parameter or good population of initial location of knots for a good iterative search. Some examples are also given to demonstrate the efficiency and effectiveness of our method.     

An artificial immune system approach to CNC tool path generation

Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems (AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper.     

An artificial immune system based algorithm to solve unequal area facility layout problem

This study introduces an artificial immune system (AIS) based algorithm to solve the unequal area facility layout problem (FLP) with flexible bay structure (FBS). The proposed clonal selection algorithm (CSA) has a new encoding and a novel procedure to cope with dummy departments that are introduced to fill the empty space in the facility area. The algorithm showed consistent performance for the 25 test problem cases studied. The problems with 100 and 125 were studied with FBS first time in the literature. CSA provided four new best FBS solutions and reached to sixteen best-so-far FBS solutions. Further, the two very large size test problems were solved first time using FBS representation, and results significantly improved the previous best known solutions. The overall results state that CSA with FBS representation was successful in 95.65% of the test problems when compared with the best-so-far FBS results and 90.90% compared with the best known solutions that have not used FBS representation.     

A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.     

WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.     

Information fusion in the immune system

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.     

Application of an artificial immune algorithm on a statistical model of dam displacement

Statistical analysis is a useful method for setting the whole string of dam displacement measures in mathematical expressions. A statistical model is usually obtained by the method of stepwise regression. The stepwise regressions based on the least square method have limitations such as the lack of stability of the set of selected variables and bias in the parameter estimates. However, the Artificial Immune Algorithm (AIA) provides good performance as an optimization algorithm. This paper proposes an immune statistical model, which merges the statistical model and the immune algorithm together, to resolve the data analysis problems of dam horizontal crest upstream-downstream displacement. The stepwise regression model and immune statistical model have been compared, showing that the immune statistical model provide a higher degree of accuracy in predicting the future behavior of the dam.     

使用机器学习算法分类P2P流量的方法研究

随着时间的推移﹐P2P的应用数量、范围开始快速增长,这在促进社会经济发展和社会大众日常生活水平提升的同时,也带来了较多的问题,而传统的网络流量分类方式存在着较多的缺陷,难以满足时代的发展和社会大众的需求。文章主要提出基于ReliefF-CFS的方法,进而实现流量的特征子集的选择,依据具体的实验研究结果来看,文章所提出的方法取得了较高的分类准确率,比较适合在后续进行推广应用。因此,文章针对使用机器学习算法分类P2P流量的方法进行系统的研究和分析,其主要目的在于高效率处理协议加密的网络流量分类问题,这对于国内的计算机网络技术的发展也具有促进作用。     

基于人工免疫系统的关联规则挖掘算法

给出了一个基于人工免疫系统的关联规则挖掘算法。将训练数据作为抗原,候选模式作为人工识别球(ARB),通过免疫学习生成频繁模式并以免疫记忆的形式加以保存,最终生成关联规则。所给的应用实例说明本算法是可行、有效的。     

Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm

Missing data in large insurance datasets affects the learning and classification accuracies in predictive modelling. Insurance datasets will continue to increase in size as more variables are added to aid in managing client risk and will therefore be even more vulnerable to missing data. This paper proposes a hybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missing data in datasets with numerous variables. The multi-layered artificial immune system creates and stores antibodies that bind to and annihilate an antigen. The genetic algorithm optimises the learning process of a stimulated antibody. The evaluation of the imputation is performed using the RIPPER, k-nearest neighbour, naïve Bayes and logistic discriminant classifiers. The effect of the imputation on the classifiers is compared with that of the mean/mode and hot deck imputation methods. The results demonstrate that when missing data imputation is performed using the proposed hybrid method, the classification improves and the robustness to the amount of missing data is increased relative to the mean/mode method for data missing completely at random (MCAR) missing at random (MAR), and not missing at random (NMAR).The imputation performance is similar to or marginally better than that of the hot deck imputation.     

Network traffic classification based on ensemble learning and co-training

Classification of network traffic is the essential step for many network researches. However,with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model,which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches,most of which only employed single classifier,multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings:limited flow accuracy rate,weak adaptability and huge demand of labeled training set. In this paper,statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set,then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.