Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning
B. Sudhakar1, V. B. Narsimha2, G. Narsimaha3
1B. Sudhakar *, Reserach Scholar-JNTUH & Associate Professor, Department of CSE-GNIT.
2V. B. Narsimha, Associate Professor, Department of CSE- University College of Engineering, OU.
3Dr. G. Narsimaha, Professor, Department of CSE- JNTUH College of Engineering, JNTUH.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2852-2861 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8783088619/2019©BEIESP | DOI: 10.35940/ijeat.F8783.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the rapid growth in the data processing and data sharing, the application owners and the consumers of the applications are more influenced to use the remote storage on cloud-based data centre and the application generated data is also growing ups and bounds. Nevertheless, the adaptation of the data sharing, and data processing applications were not easy for the consumers. The application owners and the service providers have struggled with the sensitive data of the consumers and the consumers were also faced trust issues with the complete framework. The standard legacy applications were designed for the traditional centralized scenarios, where the intrusion detection can be performed only using the network status analysis and the application characteristics analysis. Moreover, most of the parallel calculations initially enhance the hybrid likelihood and change likelihood of GA as indicated by the populace advancement variable-based math and wellness esteem. Nevertheless, the population of data and the attacks on the data is high and the correct population size is highly difficult to determine. Regardless to mention, that the use of fitness functions will restrict the attack detection to certain types and these algorithms are bound to fail in case of a newer attack. However, with the migration of application to the data processing framework, the consumers have started demanding more security against the intrusions. A good number of research attempts were made to map the traditional security algorithms into the data processing space, nonetheless, the attempts were highly criticized due to the lack of proper analysis of security attacks on data processing applications. Hence, this work proposes a novel framework to detect the intrusions on data processing framework with justifying attack characteristics. This work proposes a novel algorithm to reduce the features of attack characteristics to justify the gaps on data processing frameworks with significant reduction in time for processing and further, proposes an algorithm to derive a strong rule engine to analyse the attack characteristics for detecting newer attacks. The complete proposed framework demonstrates nearly 93% and higher accuracy, which is much higher than the existing parallel research outcomes with least time complexity.
Keywords: Attack Characteristics, Dynamic Rule engine, Hybrid Feature Selection, IDS, Knowledge-based feature.