ARPNetSteg: Network Steganography using Address Resolution Protocol

Steganography is a technique that allows hidden transfer of data using some media such as Image, Audio, Video, Network Protocol or a Document, without its existence getting noticed. Over the past few years, a lot of research has been done in the field of Image, Video and Audio Steganography but very little work has been done in Network Steganography. A Network Steganography technique hides data in a Network Data Unit, i.e., a Network Protocol Packet. In this paper we present an algorithm ARPNetSteg that implements Network Steganography using the Address resolution protocol. Our technique is a robust technique that can transfer 44 bits of covert data per ARP reply packet

ARPNetSteg: Network Steganography using Address Resolution Protocol

Steganography is a technique that allows hidden transfer of data using some media such as Image, Audio, Video, Network Protocol or a Document, without its existence getting noticed. Over the past few years, a lot of research has been done in the field of Image, Video and Audio Steganography but very little work has been done in Network Steganography. A Network Steganography technique hides data in a Network Data Unit, i.e., a Network Protocol Packet. In this paper we present an algorithm ARPNetSteg that implements Network Steganography using the Address resolution protocol. Our technique is a robust technique that can transfer 44 bits of covert data per ARP reply packet

Social Collaborative Retrieval

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval---a combination of these two traditional problems---has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset.Comment: 10 page

APSO based automated planning in Constructive Simulation

Constructive simulations are the applications used by the military for the training of their commanders in planning and analysis of various threats and Courses of Action. In the ‘analysis wargames’, there are need to automate many of the tasks of the commander which are carried out by subunit commanders on the ground. Deployment of defence units is one of such important decision making by commander. Deployments of units (and sub units) is dependent on multiple factors which needs to be satisfied/optimised for meeting the given objective of the unit. In this paper we have attempted to solve the multi criterion decision problem of optimal deployment of defence units in mountainous terrain using Particle Swarm Optimization(PSO) and Adaptive Particle Swarm Optimization(APSO). The algorithm has been tested with varied number of decision parameters and their weights using digital elevation and vector data of the terrain features. The auto deployment outcomes are found satisfactory. Our solution approach has potential in automated planning in constructive simulations.   &nbsp