InSight2: An Interactive Web Based Platform for Modeling and Analysis of Large Scale Argus Network Flow Data

Monitoring systems are paramount to the proactive detection and mitigation of problems in computer networks related to performance and security. Degraded performance and compromised end-nodes can cost computer networks downtime, data loss and reputation. InSight2 is a platform that models, analyzes and visualizes large scale Argus network flow data using up-to-date geographical data, organizational information, and emerging threats. It is engineered to meet the needs of network administrators with flexibility and modularity in mind. Scalability is ensured by devising multi-core processing by implementing robust software architecture. Extendibility is achieved by enabling the end user to enrich flow records using additional user provided databases. Deployment is streamlined by providing an automated installation script. State-of-the-art visualizations are devised and presented in a secure, user friendly web interface giving greater insight about the network to the end user

Multipath Routing of Fragmented Data Transfer in a Smart Grid Environment

The purpose of this paper is to do a general survey on the existing communication modes inside a smart grid, the existing security loopholes and their countermeasures. Then we suggest a detailed countermeasure, building upon the Jigsaw based secure data transfer [8] for enhanced security of the data flow inside the communication system of a smart grid. The paper has been written without the consideration of any factor of inoperability between the various security techniques inside a smart gridComment: 5 pages, 2 figure

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis