Aditya Naidu’s scientific contributions

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Publications (2)


Method, apparatus and program for detecting spoofed network traffic
  • Patent
  • Full-text available

December 2014

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9 Reads

Ravichander Vaidyanathan

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Abhrajit Ghosh

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Aditya Naidu

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[...]

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A method, an apparatus and a program for detecting spoofed Internet Protocol (IP) traffic directed to a network having a plurality of autonomous systems (AS) is provided. The method comprises receiving an incoming packet through an AS, the incoming packet containing a source IP address and a destination IP address, acquiring a corresponding source and destination IP address prefixes, converting the corresponding source and destination IP address prefixes into a source AS number and a destination AS number, determining if the incoming packet arrived from an unexpected source based upon the corresponding destination IP address prefix and the converted source and destination AS number using an unexpected pair tuple table generated from network routing information and generating an alert indicating that the incoming packet is not allowed to enter the network.

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Fig.2 RTFF architecture
Fig.3 RTFF user interface
Fig.4 RTFF web front end
Fig.5 RTFF alert correlation
Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering

March 2013

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112 Reads

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5 Citations

China Communications

In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.

Citations (1)


... Fan et al. adopted a multilevel hierarchical ID method based on GA (genetic algorithm) to solve the problems of a single-level ID system [9]. Ghosh et al. proposed a method of constructing an ID system with a decision tree, which can identify unknown attacks in the network [10]. Alotaibi and Alotaibi proposed an abnormal traffic detection method based on a deep neural network, which can identify the normal or abnormal connections in the network, and the detection effect is good [11]. ...

Reference:

Simulation Training Auxiliary Model Based on Neural Network and Virtual Reality Technology
Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering

China Communications