Biblio

Filters: Author is Singh, Ashutosh Kumar  [Clear All Filters]
2023-06-30
Gupta, Rishabh, Singh, Ashutosh Kumar.  2022.  Privacy-Preserving Cloud Data Model based on Differential Approach. 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T). :1–6.
With the variety of cloud services, the cloud service provider delivers the machine learning service, which is used in many applications, including risk assessment, product recommen-dation, and image recognition. The cloud service provider initiates a protocol for the classification service to enable the data owners to request an evaluation of their data. The owners may not entirely rely on the cloud environment as the third parties manage it. However, protecting data privacy while sharing it is a significant challenge. A novel privacy-preserving model is proposed, which is based on differential privacy and machine learning approaches. The proposed model allows the various data owners for storage, sharing, and utilization in the cloud environment. The experiments are conducted on Blood transfusion service center, Phoneme, and Wilt datasets to lay down the proposed model's efficiency in accuracy, precision, recall, and Fl-score terms. The results exhibit that the proposed model specifies high accuracy, precision, recall, and Fl-score up to 97.72%, 98.04%, 97.72%, and 98.80%, respectively.
2018-06-11
Kumar, Naveen, Singh, Ashutosh Kumar, Srivastava, Shashank.  2017.  Evaluating Machine Learning Algorithms for Detection of Interest Flooding Attack in Named Data Networking. Proceedings of the 10th International Conference on Security of Information and Networks. :299–302.

Named Data Networking (NDN) is one of the most promising data-centric networks. NDN is resilient to most of the attacks that are possible in TCP/IP stack. Since NDN has different network architecture than TCP/IP, so it is prone to new types of attack. These attacks are Interest Flooding Attack (IFA), Cache Privacy Attack, Cache Pollution Attack, Content Poisoning Attack, etc. In this paper, we discussed the detection of IFA. First, we model the IFA on linear topology using the ndnSIM and CCNx code base. We have selected most promising feature among all considered features then we applied diïňĂerent machine learning techniques to detect the attack. We have shown that result of attack detection in case of simulation and implementation is almost same. We modeled IFA on DFN topology and compared the results of different machine learning approaches.