Natural Disaster Resilience Approach (NDRA) to Online Social Networks
- PDF / 3,632,013 Bytes
- 28 Pages / 595.276 x 790.866 pts Page_size
- 5 Downloads / 228 Views
ORIGINAL RESEARCH
Natural Disaster Resilience Approach (NDRA) to Online Social Networks N. Poornima1 · M. Murugan2 Received: 24 December 2019 / Accepted: 25 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Numerous disasters impact people around the world, including floods, cyclones, tsunamis and so on. Disaster resilience (recovery) is a realtime challenge. The research goal of the proposed Natural Disaster Resilience Approach (NDRA) is to fasten and automate disaster resilience within the affected region. Prioritization for saving lives must be provided to the region most affected. Currently almost all people use the Online Social Networks (OSNs) to share their daily activities. Actually only their friends in OSN can note the photos posted by OSN users in the disaster-affected region and in turn can support the friends in need. Our research goal is to conduct speedier and prioritized disaster resilience processes by directly addressing honest disaster help requests from images posted by OSN users. To achieve the above goal, we suggest an approach known as the Natural Disaster Resilience Approach (NDRA). The NDRA is a three-tiered framework: Tier-1: sybil(malicious) user avoidance engine: prevents fake OSN user profile creation; Tier-2: ‘D’- attributed image classifier: determines the originality of the images posted and Tier-3: sybil user prediction: uses an Advanced Sybil Node Prediction Algorithm (ASYNPA) to check the authenticity of the user and priorities for faster resilience requests. In NDRA we use Advogato dataset with 6541 users and 51,127 edges. Ultimately, the comparison is only made between the proposed ASYNPA tier-3 and the existing VoteTrust algorithm, and the graph is plotted against the False Positive (FP) rate, taking into account the precision and recall metrics. Around 99.84% of the expected sybils were confirmed in ASYNPA, which is 3.49% higher than VoteTrust. Keywords Natural Disaster Resilience Approach (NDRA) · Advanced Sybil Node Prediction Algorithm (ASYNPA) · Sybil · Classifier · ‘D’-attributed image · Online Social Networks (OSNs)
1 Introduction Human induced and natural disasters cause adverse effects of greater risks on human livelihood. Even the occurrences of the aforementioned disasters are still rare, causing arduous damage to the public and monetary loss. The Government of India (GoI) has developed a Natural Disaster Management Plan (NDMP) in tandem with the United Nations (UN) to strengthen disaster resilience. NDMP was designed to
* N. Poornima [email protected] M. Murugan [email protected] 1
Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
2
reduce risk and improvise the capacity to deal with disasters with less or no loss of life and assets. NDMP is implemented by the Nation
Data Loading...
