Informatics Publishing Limited

S. Madhavi ¹, I. Ramesh Babu ²

Affiliations
1 Department of Computer Science and Engineering, P. V. P. Siddhartha Institute of Technology, affiliated to Jawaharlal Nehru Technological University, IN
2 Department of Computer Science, Nagarjuna University, IN

Abstract

Maximizing the channel throughput capacity and providing security for nodes in the mobile adhoc networks is becoming an attractive challenge for many researchers today. The nodes in the mobile adhoc networks move from one region to another. But still they demand for the security for their transactions. Today’s firewalls and encryption software’s are not sufficient and effective to protect the networks. An intrusion detection system (IDS) is required to monitor the network, detect the misbehaviors or anomalies and notify other nodes in the network to avoid or punish the misbehaving nodes. Because of these misbehaving nodes the network performance degrades. Hence a method is proposed to identify and control the misbehaving nodes so as identify the misbehaving nodes, and control their behavior. This ultimately improves the channel throughput. We proposed a new mIDS (Mobile Intrusion Detection System) suitable for multi-hop ad-hoc wireless networks in [2][4][5][6][7] which detects nodes misbehavior, anomalies in packet forwarding, such as intermediate nodes dropping or delaying packets. mIDS does relay on overhearing packet transmissions of neighboring nodes. In [8][9][10] we proposed a new scheduling method which improves the channel efficiency and network capacity.

In this paper we evaluated the metrics like

1. Packet dropping
2. Minimize the Control packet overhead on the nodes
3. Maximize the Channel throughput
in mobile adhoc networks using the mIDS protocol.

Keywords

CDMA, Dos, IDS, mIDS, QoS, TDMA.

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N. Nagamalleswara Rao ¹, O. Naga Raju ², I. Ramesh Babu ²

Affiliations
1 Department of C S & E, R.V.R & J. C. College of Engineering, Guntur, A. P., IN
2 Department of Computer Science and Engineering, Nagarjuna University, Guntur, Andhra Pradesh, IN

Abstract

Job sequencing is the arrangement of the task that is to be performed or processed in a machine in that particular order. Job sequencing problem has become the major problem in the computer field. A finite set of n jobs where each job consists of a chain of operations and a finite set of m machines where each machine can handle at most one operation at a time. Each operation needs to be processed during an uninterrupted period of a given length on a given machine and our Purpose is to find a schedule, that is, an allocation of the operations to time intervals to machines that has minimal length.

Keywords

Job Sequencing, Processing Time, Schedule, Non-Preemptive, Preemptive, Time Deviation, Time Duration, Total Elapsed Time, Heuristic Technique, Sequencing Rules, Earliest Due Date, Shortest Processing Time Rule.

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P. Srinivasulu ¹, J. Ranga Rao ¹, I. Ramesh Babu ²

Affiliations
1 Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada, IN
2 Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, IN

Abstract

In the faceless world of the Internet, online fraud is one of the greatest reasons of loss for web merchants. Advanced solutions are needed to protect e-businesses from the constant problems of fraud. Many popular fraud detection algorithms require supervised training, which needs human intervention to prepare training cases. Since it is quite often for an online transaction database to have Terabyte-level storage, human investigation to identify fraudulent transactions is very costly. This paper describes the automatic design of user profiling method for the purpose of fraud detection. We use a FP (Frequent Pattern) Tree rule-learning algorithm to adaptively profile legitimate customer behavior in a transaction database. Then the incoming transactions are compared against the user profile to uncover the anomalies. The anomaly outputs are used as input to an accumulation system for combining evidence to generate high-confidence fraud alert value. Favorable experimental results are presented.

Keywords

Fraud Detection, FP Tree, Anomalies, Adaptive Mining, Association Mining.

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