Prakash Kalavadekar - Academia.edu (original) (raw)
IJREAM VOLUME 03 ISSUE 08 by Prakash Kalavadekar
For most of the people, web interaction is a very common phase to acquire information. It is poss... more For most of the people, web interaction is a very common phase to acquire information. It is possible that in a combined environment, more than one person may try to obtain similar information in one domain. One person may like to solve a problem using an unfamiliar Apache Tomcat which he had studied by another person before. Connecting and then sharing with that persons will be more beneficial to get there learned knowledge. Fine grained knowledge sharing is proposed for this combined environment. The system is proposed to classify the surfed data into clusters and summarize the details in fine grained details. For any system the efficiency depends upon the surfing. The framework includes: Data which is surfed, clustered into tasks. Then task is mined in fine grained output. To get proper result, the search method is applied to the output (mined results).The concept of Data Mining in fine grained knowledge is combined with the information gathering and classification to produce efficient data searching technique in agriculture system.
Papers by Prakash Kalavadekar
Ijca Proceedings on International Conference on Recent Trends in Engineering and Technology 2013, Feb 5, 2013
Intrusion detection is the process of monitoring and analyzing the events occurring in a computer... more Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. Today most of the intrusion detection approaches focused on the issues of feature selection or reduction, since some of the features are irrelevant and redundant which results lengthy detection process and degrades the performance of an intrusion detection system (IDS). The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. The design of IDS is investigated from these three perspectives. The goals are to remove redundant instances that causes the learning algorithm to be unbiased, identify suitable subset of features by employing a wrapper based feature selection algorithm and realizing proposed IDS with neurotree to achieve better detection accuracy.
International Journal of Engineering & Technology
Conventional methods of intrusion prevention like firewalls, cryptography techniques or access ma... more Conventional methods of intrusion prevention like firewalls, cryptography techniques or access management schemes, have not provided complete protection to computer systems and networks from refined malwares and attacks. Intrusion Detection Systems (IDS) are giving the right solution to the current issues and became an important part of any security management system to detect these threats and will not generate widespread harm. The basic goal of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. The signature based concept using genetic algorithm as features selection and, J48 as classifier to detect attack is proposed in this paper. The system was evaluated on KDD Cup 99, NSL-KDD and Kyoto 2006+ datasets.
Conventional intrusion prevention methods such as firewalls, access management schemes or cryptog... more Conventional intrusion prevention methods such as firewalls, access management schemes or cryptography techniques, have not proved themselves to completely defend networks and systems from refined malwares and attacks. The Intrusion Detection Systems (IDS) proved to be the right salvage to the current issues and became an important element of any security infrastructure to sight these threats before they induce widespread harm. The basic aim of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. These approaches are broadly categorized into three approaches: i) Signature-based approach ii) anomaly based approach and iii) hybrid approach that combines signature and anomaly detection approaches. Hybrid approach has been found to be superior that either signature based or anomaly based approaches. However, despite superior performance, the hybrid approach has till date failed to provide desired detection rate and time needed for detection.
For most of the people, web interaction is a very common phase to acquire information. It is poss... more For most of the people, web interaction is a very common phase to acquire information. It is possible that in a combined environment, more than one person may try to obtain similar information in one domain. One person may like to solve a problem using an unfamiliar Apache Tomcat which he had studied by another person before. Connecting and then sharing with that persons will be more beneficial to get there learned knowledge. Fine grained knowledge sharing is proposed for this combined environment. The system is proposed to classify the surfed data into clusters and summarize the details in fine grained details. For any system the efficiency depends upon the surfing. The framework includes: Data which is surfed, clustered into tasks. Then task is mined in fine grained output. To get proper result, the search method is applied to the output (mined results).The concept of Data Mining in fine grained knowledge is combined with the information gathering and classification to produce efficient data searching technique in agriculture system.
Ijca Proceedings on International Conference on Recent Trends in Engineering and Technology 2013, Feb 5, 2013
Intrusion detection is the process of monitoring and analyzing the events occurring in a computer... more Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. Today most of the intrusion detection approaches focused on the issues of feature selection or reduction, since some of the features are irrelevant and redundant which results lengthy detection process and degrades the performance of an intrusion detection system (IDS). The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. The design of IDS is investigated from these three perspectives. The goals are to remove redundant instances that causes the learning algorithm to be unbiased, identify suitable subset of features by employing a wrapper based feature selection algorithm and realizing proposed IDS with neurotree to achieve better detection accuracy.
International Journal of Engineering & Technology
Conventional methods of intrusion prevention like firewalls, cryptography techniques or access ma... more Conventional methods of intrusion prevention like firewalls, cryptography techniques or access management schemes, have not provided complete protection to computer systems and networks from refined malwares and attacks. Intrusion Detection Systems (IDS) are giving the right solution to the current issues and became an important part of any security management system to detect these threats and will not generate widespread harm. The basic goal of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. The signature based concept using genetic algorithm as features selection and, J48 as classifier to detect attack is proposed in this paper. The system was evaluated on KDD Cup 99, NSL-KDD and Kyoto 2006+ datasets.
Conventional intrusion prevention methods such as firewalls, access management schemes or cryptog... more Conventional intrusion prevention methods such as firewalls, access management schemes or cryptography techniques, have not proved themselves to completely defend networks and systems from refined malwares and attacks. The Intrusion Detection Systems (IDS) proved to be the right salvage to the current issues and became an important element of any security infrastructure to sight these threats before they induce widespread harm. The basic aim of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. These approaches are broadly categorized into three approaches: i) Signature-based approach ii) anomaly based approach and iii) hybrid approach that combines signature and anomaly detection approaches. Hybrid approach has been found to be superior that either signature based or anomaly based approaches. However, despite superior performance, the hybrid approach has till date failed to provide desired detection rate and time needed for detection.