Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods.

Privacy-preserving and Efficient Aggregation based on Blockchain for Power Grid Communications in Smart Communities

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-net.2019.0156

RBFNN: a radial basis function neural network model for detecting and mitigating the cache pollution attacks in named data networking

Named Data Networking (NDN) is a developing Internet design that utilizes a new network communication model dependent on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves an important role of recording Interest packet information. In managing PIT, the issue of flow PIT measuring has been very challenging because of the huge use of long Interest lifetime especially when there is no adaptable replacement strategy, subsequently affecting PIT performance. Named Data Networking (NDN) might experience some emerging threats such as Interest Flooding Attacks (IFA). In this paper, we focus on the IFA that can seriously devour the memory resource for the Pending Interest Table (PIT) of each included NDN router by flooding a huge amount of malicious Interests with spoofed names. To extricate the pressure of PIT attacked by IFA, we propose a methodology of efficient Secured PIT management and attack detection strategy by using a cuckoo search optimization algorithmDeep convolutional neural network (CSOA-DCNN) algorithm in Named Data Network. The CSO algorithm initially utilizes a learning technique and afterward considers improved search operators and deep convolutional neural network architecture (DCNN) for classification. The network simulation tool is utilized to design and calculate PIT management. The results of the study on a 20 Gbps gateway trace shows that the corresponding PIT contains 1.5 M entries, and the lookup, insert and delete frequencies are 1.4 M/s, 0.9 M/s and 0.9 M/s. The contribution of this study is significant for Interest packet management in NDN routing and forwarding systems.

/pdf/an-efficient-secured-pit-management-and-attack-detection-4f5a1lasu1.pdf

Ramachandira Moorthy Buvanesvari

Papers

RBFNN: a radial basis function neural network model for detecting and mitigating the cache pollution attacks in named data networking

An Efficient Secured PIT Management and Attack Detection Strategy Enhanced by CSOA-DCNN Algorithm in a Named Data Networking (NDN)