Sumit Miglani

Mining Professional's Data from LinkedIn

Social media has become very popular communication tool among internet users in the recent years. A large unstructured data is available for analysis on the social web. The data available on these sites have redundancies as users are free to enter the data according to their knowledge and interest. This data needs to be normalized before doing any analysis due to the presence of various redundancies in it. In this paper, LinkedIn data is extracted by using LinkedIn API and normalized by removing redundancies. Further, data is also normalized according to locations of LinkedIn connections using geo coordinates provided by Microsoft Bing. Then, clustering of this normalized data set is done according to job title, company names and geographic locations using Greedy, Hierarchical and K-Means clustering algorithms and clusters are visualized to have a better insight into them.

ADS: Protecting NTFS from hacking

Alternate Data Streams is one of the possible ways to hide data in NTFS file system in Windows. It was introduced to make Windows NTFS compatible with HFS file system of Macintosh. This paper explains what exactly alternate data streams are, their requirement and their functionality. It also explains whether alternate data streams is a feature or a vulnerability of NTFS file system. It explains how hacker can utilize this functionality of NTFS to hide malicious codes in victims machine so as to compromise it. All possible ways of hiding data and techniques for detecting and removing ADS are also explained. It mainly focuses on criminals who use various data hiding techniques in order to hide their data from the forensic analysts. Finally its main focus is on explaining an ADS Tool that is a graphical tool which enables user to create, start, detect and delete ADS.

Efficient and secure message transfer in VANET

Wireless medium shows a key role from past few years in the world of communication. Mobile Adhoc Network (MANET) is an important field of wireless communication. Vehicular Adhoc Network (VANET) is a new area of MANET in which a car acts as node communicator with rest of the cars and road side infrastructure in the network. Security is a major issue in VANET as it provide safety as well as non safety application to the users. Varied works have already been done by researchers for the security in VANET but securing the message communication between cars still poses an issue. From this paper, we introduce a model which provides a secure communication between cars. In our model, RSUs acts as a Certificate Authority(CA) which will generate the key using Elliptic Curve Cryptography(ECC) for the cars and after that communication between cars takes place using Elliptic Curve Diffie Hellman (ECDH).

Analysis and Visualization of Professional’s LinkedIn Data

Social media has become very popular communication tool among internet users in the recent years. A large unstructured data is available for analysis on the social web. The data available on these sites have redundancies as users are free to enter the data according to their knowledge and interest. This data needs to be normalized before doing any analysis due to the presence of various redundancies in it. In this paper, LinkedIn data is extracted by using LinkedIn API and normalized by removing redundancies. Further, data is also normalized according to locations of LinkedIn connections using geo coordinates provided by Microsoft Bing. Then, clustering of this normalized data set is done according to job title, company names and geographic locations using Greedy, Hierarchical and K-Means clustering algorithms and clusters are visualized to have a better insight into them.