Packet Dropping and Intrusion Detection using Forensic and Flow Based Classification Techniques
Shyju S.1, Prathibha S Nair2

1Shyju S, M.Tech, Department of Computer Science and Engineering, Mohandas College of Engineering and Technology Trivandrum (Kerala), India.
2Prathibha S. Nair, Department of Computer Science and Engineering, Mohandas College of Engineering and Technology, Trivandrum (Kerala), India.  

Manuscript received on 13 June 2017 | Revised Manuscript received on 20 June 2017 | Manuscript Published on 30 June 2017 | PP: 66-72 | Volume-6 Issue-5, June 2017 | Retrieval Number: E5004066517/17©BEIESP
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Abstract: Internal Intrusion detection is one of the serious problems in the computer network areas. Most of the computer system uses username and password as login pattern to enter in to the system. This is one of the weakest points of computer security. Some studies claimed that analyzing system calls (SCs) generated by commands can identify these commands and obtains the features of an attack. This paper propose a security system, named the Internal Intrusion Detection and Protection System(IIDPS) to detect insider attacks at SC level by using data mining and forensic techniques in networked data. The IIDPS creates users’ personal profiles to keep track of users’ usage habits as their forensic features and determines whether a valid login user is the account holder or not by comparing users current computer usage behaviors with the patterns collected in the account holder’s personal profile. The idea behind the inside attacker detection in wireless sensor network by exploiting the spatial correlation between the packet ratio, which help to detecting dynamic attacking behaviors The routing is performed to identify the shortest path between each source node and their destination address and residual energy is calculated for each node in the network.
Keywords: Insider Attacks, Intrusion Detection, Flow Based Classification And System Calls.

Scope of the Article: Classification