A Virtual Network PaaS for 3GPP 4G and Beyond Core Network Services

Cloud computing and Network Function Virtualization (NFV) are emerging as key technologies to overcome the challenges facing 4G and beyond mobile systems. Over the last few years, Platform-as-a-Service (PaaS) has gained momentum and has become more widely adopted throughout IT enterprises. It simplifies the applications provisioning and accelerates time-to-market while lowering costs. Telco can leverage the same model to provision the 4G and beyond core network services using NFV technology. However, many challenges have to be addressed, mainly due to the specificities of network services. This paper proposes an architecture for a Virtual Network Platform-as-a-Service (VNPaaS) to provision 3GPP 4G and beyond core network services in a distributed environment. As an illustrative use case, the proposed architecture is employed to provision the 3GPP Home Subscriber Server (HSS) as-a-Service (HSSaaS). The HSSaaS is built from Virtualized Network Functions (VNFs) resulting from a novel decomposition of HSS. A prototype is implemented and early measurements are made.Comment: 7 pages, 6 figures, 2 tables, 5th IEEE International Conference on Cloud Networking (IEEE CloudNet 2016

Alert-BDI: BDI Model with Adaptive Alertness through Situational Awareness

In this paper, we address the problems faced by a group of agents that possess situational awareness, but lack a security mechanism, by the introduction of a adaptive risk management system. The Belief-Desire-Intention (BDI) architecture lacks a framework that would facilitate an adaptive risk management system that uses the situational awareness of the agents. We extend the BDI architecture with the concept of adaptive alertness. Agents can modify their level of alertness by monitoring the risks faced by them and by their peers. Alert-BDI enables the agents to detect and assess the risks faced by them in an efficient manner, thereby increasing operational efficiency and resistance against attacks.Comment: 14 pages, 3 figures. Submitted to ICACCI 2013, Mysore, Indi

Hierarchical Design Based Intrusion Detection System For Wireless Ad hoc Network

In recent years, wireless ad hoc sensor network becomes popular both in civil and military jobs. However, security is one of the significant challenges for sensor network because of their deployment in open and unprotected environment. As cryptographic mechanism is not enough to protect sensor network from external attacks, intrusion detection system needs to be introduced. Though intrusion prevention mechanism is one of the major and efficient methods against attacks, but there might be some attacks for which prevention method is not known. Besides preventing the system from some known attacks, intrusion detection system gather necessary information related to attack technique and help in the development of intrusion prevention system. In addition to reviewing the present attacks available in wireless sensor network this paper examines the current efforts to intrusion detection system against wireless sensor network. In this paper we propose a hierarchical architectural design based intrusion detection system that fits the current demands and restrictions of wireless ad hoc sensor network. In this proposed intrusion detection system architecture we followed clustering mechanism to build a four level hierarchical network which enhances network scalability to large geographical area and use both anomaly and misuse detection techniques for intrusion detection. We introduce policy based detection mechanism as well as intrusion response together with GSM cell concept for intrusion detection architecture.Comment: 16 pages, International Journal of Network Security & Its Applications (IJNSA), Vol.2, No.3, July 2010. arXiv admin note: text overlap with arXiv:1111.1933 by other author

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

On web user tracking of browsing patterns for personalised advertising

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Parallel, Emergent and Distributed Systems on 19/02/2017, available online: http://www.tandfonline.com/doi/abs/10.1080/17445760.2017.1282480On today’s Web, users trade access to their private data for content and services. App and service providers want to know everything they can about their users, in order to improve their product experience. Also, advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users’ actions and tastes to suggest a range of products to buy. Both service providers and advertisers try to track users’ behaviour across their product network. For application providers this means tracking users’ actions within their platform. For third-party services following users, means being able to track them across different websites and applications. It is well known how, while surfing the Web, users leave traces regarding their identity in the form of activity patterns and unstructured data. These data constitute what is called the user’s online footprint. We analyse how advertising networks build and collect users footprints and how the suggested advertising reacts to changes in the user behaviour.Peer ReviewedPostprint (author's final draft