NS2

IEEE Projects Titles 2021-2022 | Final Year Projects Titles  2021-2022

 

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IEEE 2021-2022 NS2 Project Titles

Title
year
1. Intrusion detection system using voting-based neural network 2021-2022
2. An evaluation method of internal network security defense ability based on device traffic 2021-2022
3. Communication Security Design of Distribution Automation System with Multiple Protection 2021-2022
4. DeepKeyGen: A Deep Learning-Based Stream Cipher Generator for Medical Image Encryption and Decryption 2021-2022
5. Implementation of Low Delay Distribution Terminal Security Protection 2021-2022
6. Security Issues in Narrowband-IoT: Towards Green Communication 2021-2022
7. Research on the Design of the Implementation Plan of Network Security Level Protection of Information Security 2021-2022
8. Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images 2021-2022
9. Time-Series Snapshot Network for Partner Recommendation: A Case Study on OSS 2021-2022
10. A Content-based Image Retrieval Scheme Using Compressible Encrypted Images 2021-2022
11. A New Image Encryption Algorithm for Grey and Color Medical Images 2021-2022
12. Quantitative Evaluation of an Automated Cone-Based Breast Ultrasound Scanner for MRI–3D US Image Fusion 2021-2022
13. Compressive Spectral Imaging Via Virtual Side Information 2021-2022
14. SAR image de-noising via grouping-based PCA and guided filter 2021-2022
15. Distributed Learning and Inference With Compressed Images 2021-2022
16. An Image Reconstruction Method of Capacitively Coupled Electrical Impedance Tomography (CCEIT) Based on DBSCAN and Image Fusion 2021-2022
17. Coarse-to-Fine Lung Nodule Segmentation in CT Images With Image Enhancement and Dual-Branch Network 2021-2022
18. Rings for Privacy: An Architecture for Large Scale Privacy-Preserving Data Mining 2021-2022
19. Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network 2021-2022
20. Hierarchical Image Segmentation Based on Nonsymmetry and Anti-Packing Pattern Representation Model 2021-2022
21. A Wide Multimodal Dense U-Net for Fast Magnetic Resonance Imaging 2021-2022
22. Hierarchical Image Segmentation Based on Nonsymmetry and Anti-Packing Pattern Representation Model 2021-2022
23. Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior 2021-2022
24. Optimizing an Image Coding Framework with Deep Learning-based Pre- and Post-Processing 2021-2022
25. Distributed Learning and Inference With Compressed Images 2021-2022
26. Determination of Soil Moisture Content using NS2 -A Survey 2021-2022
27. High-Resolution Remote Sensing Image Captioning Based on Structured Attention 2021-2022
28. A Framework for Hexagonal NS2 Using Hexagonal Pixel-Perfect Approximations in Subpixel Resolution 2021-2022
29. Correcting Higher Order Aberrations Using NS2
NS2
2021-2022
30. DenseNet model with RAdam optimization algorithm for cancer image classification 2021-2022
31. Technical analysis of intelligent NS2 of tea 2021-2022
32. Adversarial Attack Against Deep Saliency Models Powered by Non-Redundant Priors 2021-2022
33. Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation 2021-2022
34. Transfer Learning for Automatic Brain Tumor Classification Using MRI Images 2021-2022
35. Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images 2021-2022
NS2
Title
year
1. A Secure and Efficient ID-Based Aggregate Signature Scheme for Wireless Sensor Networks 2021-2022
2. PROVEST: Provenance-based Trust Model for Delay Tolerant Networks 2021-2022
3. GeTrust: A guarantee-based trust model in Chord-based P2P networks 2021-2022
4.Contradiction Based Gray-Hole Attack Minimization for Ad-Hoc Networks 2021-2022
5. Secure and Private Data Aggregation for Energy Consumption Scheduling in Smart Grids 2021-2022
6.Preventing Distributed Denial-of-Service Flooding Attacks With Dynamic Path Identifiers 2021-2022

NS2 TITLES ABSTRACTS

1. A Secure and Efficient ID-Based Aggregate Signature Scheme for Wireless Sensor Networks – IoT

Affording secure and efficient big data aggregation methods is very attractive in the field of wireless sensor networks (WSNs) research. In real settings, the WSNs have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and data tampering, etc. In this paper, we mainly focus on data integrity protection, give an identity-based aggregate signature (IBAS) scheme with a designated verifier for WSNs. According to the advantage of aggregate signatures, our scheme not only can keep data integrity, but also can reduce bandwidth and storage cost for WSNs. Furthermore, the security of our IBAS scheme is rigorously presented based on the computational Diffie-Hellman assumption in random oracle model.

3.GeTrust: A guarantee-based trust model in Chord-based P2P networks Dependable and Secure Computing- Preprint

More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. However, users’ unconstrained behavior makes it necessary to use a trust model when establishing trust relationships between peers. Most existing trust models are based on recommendations, which, however, suffer from the shortcomings of slow convergence and high complexity of trust computations, as well as huge overhead of network traffic. Inspired by the establishment of trust relationships in human society, a guarantee-based trust model, GeTrust, is proposed for Chord-based P2P networks. A service peer needs to choose its guarantee peer(s) for the service it is going to provide, and they are both required to pledge reputation mortgages for the service. The request peer makes evaluations on all the candidates of service peer by referring their service reputations and their guarantee peers’ reputations, and selects the one with highest evaluation to be its service provider. In order to enhance GeTrust’s availability and prevent malicious behavior, we also present incentive mechanism and anonymous reputation management strategy.

6.Preventing Distributed Denial-of-Service Flooding Attacks With Dynamic Path Identifiers Information Forensics and Security

There are increasing interests in using path identifiers ( PIDs ) as inter-domain routing objects. However, the PIDs used in existing approaches are static, which makes it easy for attackers to launch the distributed denial-of-service (DDoS) flooding attacks. To address this issue, in this paper, we present the design, implementation, and evaluation of dynamic PID (D-PID), a framework that uses PIDs negotiated between the neighboring domains as inter-domain routing objects. In D-PID, the PID of an inter-domain path connecting the two domains is kept secret and changes dynamically. We describe in detail how neighboring domains negotiate PIDs and how to maintain ongoing communications when PIDs change. We build a 42-node prototype comprised of six domains to verify D-PID’s feasibility and conduct extensive simulations to evaluate its effectiveness and cost. The results from both simulations and experiments show that D-PID can effectively prevent DDoS attacks.

2.PROVEST: Provenance-based Trust Model for Delay Tolerant Networks – IoT

Delay tolerant networks (DTNs) are often encountered in military network environments where end-to-end connectivity is not guaranteed due to frequent disconnection or delay. This work proposes a provenance-based trust framework, namely PROVEST (PROVEnance-baSed Trust model) that aims to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of a valued object or information. Interdependency leveraged between trustworthiness of information source and information itself in PROVEST. PROVEST takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while estimating a node’s trust dynamically in response to changes in the environmental and node conditions. This work adopts a model-based method to evaluate the performance of PROVEST

4.Contradiction Based Gray-Hole Attack Minimization for Ad-Hoc Networks Mobile Computing

Although quite popular for the protection for ad-hoc networks (MANETs, IoT, VANETs, etc.), detection & mitigation techniques only function after the attack has commenced. Prevention, however, attempts at thwarting an attack before it is executed. Both techniques can be realized either by the collective collaboration of network nodes (i.e., adding security messages to protocols) or by internal deduction of attack state. In this paper, we propose a method for minimizing the gray-hole DoS attack. Our solution assumes no explicit node collaboration, with each node using only internal knowledge gained by routine routing information.

5.Secure and Private Data Aggregation for Energy Consumption Scheduling in Smart Grids Dependable secure computing

The recent proposed solutions for demand side energy management leverage the two-way communication infrastructure provided by modern smart-meters and sharing the usage information with the other users. In this paper, we first highlight the privacy and security issues involved in the distributed demand management protocols. We propose a novel protocol to share required information among users providing privacy, confidentiality, and integrity. We also propose a new clustering-based, distributed multi-party computation (MPC) protocol. Through simulation experiments we demonstrate the efficiency of our proposed solution. The existing solutions typically usually thwart selfish and malicious behavior of consumers by deploying billing mechanisms based on total consumption during a few time slots. However, the billing is typically based on the total usage in each time slot in smart grids. In the second part of this paper, we formally prove that under the per-slot based charging policy, users have incentive to deviate from the proposed protocols. We also propose a protocol to identify untruthful users in these networks. Finally, considering a repeated interaction among honest and dishonest users, we derive the conditions under which the smart grid can enforce cooperation among users and prevents dishonest declaration of consumption.

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