Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

Dynamic Service Discovery, Composition and Reconfiguration in a Model Mapping Business Process to Web Services

In any organization where SOA has been implemented, all of the web services are registered in UDDI and users’ needs are served by using appropriate web services. So in this paper, we will try to discover a service from repository first that can provide the required output to the user. The process becomes difficult when a single service is not able to fulfill a user’s need and we need a combination of services to answer complex needs of users. In our paper, we will suggest a simpler approach for dynamic service composition using a graph based methodology. This will be a design time service composition. This approach uses the functional and non-functional parameters of the services to select the most suitable services for composition as per user’s need. This approach involves “service classification” on the basis of functional parameters, “service discovery” on the basis of user’s need and then “service composition” using the selected services on the basis of non-functional parameters like response time, cost, security and availability. Another challenge in SOA implementation is that, once the composition has performed, some services may become faulty at runtime and may stop the entire process of serving a user’s need. So, we will also describe a way of “dynamic service reconfiguration” in our approach that will enable us to identify and replace a faulty service that is violating the SLA or is not accessible anymore. This service reconfiguration is done without redoing or reconfiguring the entire composition. In the end, to simulate the proposed approach, we will represent a prototype application built on php 5.4 using My SQL database at backend.

Machine intelligence,rough sets and rough-fuzzy granular computing:uncertainty handling in bio-informatics and Web intelligence

Machine intelligence,is out of the system by the artificial intelligence shown.It is usually achieved by the average computer intelligence.Rough sets and Information Granules in uncertainty management and soft computing and granular computing is widely used in many fields,such as in protein sequence analysis and biobasis determination,TSM and Web service classification Etc.

SERVICE ATTRIBUTE-PROCESS MATRIX： A TOOL FOR DESIGNING AND MANAGING SERVICES

The growing importance of the service economy during the last 40 years has raised the need for new tools for designing and managing services. As a result, several authors have developed service classification systems, in order to better understand the nature of service operations and provide methods and tools to improve service efficiency and quality. This paper exploits the work resulting from service classification systems and identifies the principal attributes to be considered in service management. The tool introduced for this purpose is the Service Attribute-Process Matrix （SAPM）, which uses selected results from existing service classification schemes to investigate the importance of the significant service attributes to major processes of the service life cycle.

A novel model on dynamic resource allocation in optical networks

A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the transmission qualities of the VWP candidates.