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Swathi, K.
- A Study on Sharing Encrypted Information in P2P Network
Abstract Views :183 |
PDF Views:3
Authors
K. Swathi
1,
G. Satyavathy
1
Affiliations
1 Sri Ramakrishna College of Arts & Science for Women, Coimbatore-641044, IN
1 Sri Ramakrishna College of Arts & Science for Women, Coimbatore-641044, IN
Source
Fuzzy Systems, Vol 5, No 7 (2013), Pagination: 217-219Abstract
In this paper, an encryption technique is proposed based on the general idea that offers efficiency in uploading and downloading a file or data in an encrypted form between peers. The proposed method, introduces a theoretical come practical model that forecasts the act of the system and computes the values of the practical parameters that achieve a desired performance. The proposed method provides very strong security for cooperation and also improves the presentation of P2P networks considerably. In particular, theoretical and realistic models show that it reduces the query response time and file upload and download delays by organizing the magnitude, and doubles the system’s throughput.Keywords
Encryption, P2P Networks, Throughput, Response Time.
- Fast kNN Classifiers for Network Intrusion Detection System
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 Computer Center, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, IN
1 Computer Center, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 10, No 14 (2017), Pagination:Abstract
Objective and Background: To adapt two fast kNN classification algorithms i.e., Indexed Partial Distance Search kNearest Neighbor (IKPDS), Partial Distance Search kNearest Neighbor (KPDS) and comparing with traditional kNN classification for Network Intrusion Detection. Methods/Statistical Analysis: NSL-KDD data set is used to evaluate the kNN classification, KPDS and IKPDS with 10 fold cross validation test. This experiment results shows that the IKPDS reduces the classification completion time compare with kNN and KPDS by preserving the same classification accuracy as well as the same error rate for different types of attacks. A novelistic method proposed for classifying the unknown patterns whether it is a malicious or legitimate using IKPDS algorithm. Findings: These algorithms efficiency were tested with the sample of 12597 instances and verified with actual class label. The resultsshow that 99.6% accuracy of the proposed method. Applications/Improvements: A deep analysis can be performed on DoS and Probe attacks as they are exhibiting similar characters andfeature selection techniques may also be implemented inorder to improve the accuracy and reduce the computational time.Keywords
IKPDS, Intrusion Detection, kNN Classification, NSL-KDD, Partial Distance SearchFull Text
- Batch Biosorption Studies for the Removal of Chromium
Abstract Views :143 |
PDF Views:0
Authors
Affiliations
1 VMKV Engineering College, Salem, Tamilnadu, IN
2 Vikas College of Pharmacy, Suryapet, Andhra Pradesh, IN
3 Nalanda College of Pharmacy, Nalgonda, Andhra Pradesh, IN
4 SASTRA University, Thanjavur, Tamilnadu, IN
1 VMKV Engineering College, Salem, Tamilnadu, IN
2 Vikas College of Pharmacy, Suryapet, Andhra Pradesh, IN
3 Nalanda College of Pharmacy, Nalgonda, Andhra Pradesh, IN
4 SASTRA University, Thanjavur, Tamilnadu, IN
Source
Asian Journal of Research in Chemistry, Vol 3, No 2 (2010), Pagination: 346-350Abstract
Batch sorption experiments were carried out using a novel adsorbent, Acid treated Pongamia Leaf Powder (APLP) and Acid treated Neem Leaf Powder (ANLP), for the removal of Chromium(VI) from aqueous solutions. Potential of APLP and ANLP for adsorption of chromium from aqueous solution was found to be excellent. Effects of process parameters pH, contact time and adsorbent capacity were studied. Langmuir model represent the experimental data well. Maximum dye uptake was found indicating that APLP and ANLP can be used as an excellent low-cost adsorbent. Comparison of adsorption capacity of APLP and ANLP for chromium clearly indicates that the capacity of APLP for adsorption of chromium is quite high than ANLP. It can be expected that APLP and ANLP would have similar capacities for dyes with similar molecular weight, structure and/or ionic load. Thus, the naturally defoliated the Pongamia and Neem leaf powders a low-cost natural resource, can be effectively used to remove pollutants from effluents.- Hybrid Intrusion Detection Method Based on Constraints Optimized SAE and Grid Search Based SVM-RBF on Cloud
Abstract Views :248 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, K L Deemed to be University, Vaddeswaram, Guntur, Andhra Pradesh, IN
1 Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, K L Deemed to be University, Vaddeswaram, Guntur, Andhra Pradesh, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 6 (2021), Pagination: 776-787Abstract
The present era is facing lot of Security, Privacy, and Integrity issues because of tremendous development in communication technology, data storage devices, and computing advancements leading to unavoidable losses. As a result of the aforementioned technological revolutions day by day, many of the organizations or institutions started migrating to cloud environment. Because of this, security issues have increased coupled with the advent of new ways of penetration into networks. Unauthorized users and many professionals with malicious intent started exploiting the legitimate users through cyber-crimes. So, there is a need to implement a proper Intrusion Detection System with optimization procedures. This paper proposes a hybrid Intrusion Detection approach with a combination of Constraints Optimized Stacked Autoencoder (COSAE) for dimension reduction and grid search based SVM-RBF classifier (GSVM-RBF). The COSAE+GSVM-RBF model enhanced the performance using a two-fold. i) The SAE is optimized through regularization techniques with the adoption of weight and dropout constraints, ii) To enhance the performance of the SVM classifier with RBF for tuning the hyperparameters using grid search. Various experiments are conducted to validate this model with four activation functions Scaled Exponential Linear Unit (SELU), Rectified Linear Unit, softplus, and Exponential Linear Unit (ELU) for dimension reduction using COSAE. The improvements carried out in this paper result in exploding gradients and vanishing gradients avoids overfitting in large datasets, intrusion detection rate, gain in computational time, and 100% F-Measure in classifying minor class labels. The proposed approach is validated on the CICIDS2017 dataset. Further, a comparative analysis of the proposed approach with state-of-the-art approaches has been conducted. Based on the experimental results it is observed that the proposed approach outperforms the prevailing approaches.Keywords
Cloud Computing, Intrusion Detection, Stacked Autoencoder, Support Vector Machine, Regularization Constraints.Full Text
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