Machine learning based deep job exploration and secure transactions in virtual private cloud systems

Wireless Communications and Mobile Computing

Routray

et al. 2022

Self Cite

Securing wireless mesh networks (WMNs) is a crucial issue due to its intrinsic characteristics. Several endangered features might emerge due to the exposure of the networks to a diversity of medium access control (MAC) layers such as distributed denial of service (DDoS) attacks, false reply attacks, and other identity attacks. Against these attacks, the determination of existing techniques is insufficient to ensure the complete security solutions to protect the backbone network at multiple levels. As a result, effective, scalable, and integrated security solutions for WMNs are required. In WMNs, protecting legitimate gateway nodes and internal mesh routers against malicious attacks at the MAC layer remains a difficult problem. Our proposed trust-based security mechanism includes distributed authentication and deauthentication algorithms that validates backbone mesh routers as well as gateway nodes. Particularly, this proposed model targets DDoS attacks in the network. The proposed DDoS attack prevention mechanism (DAPM) uses distributed authentication and deauthentication algorithms to build trusted group heads for managing secure data communication in the network. Our research and practical results show that the proposed mechanism decreases the severity of malicious nodes and strengthens the security compared to existing centralized schemes such as digital signature authentication (DSA-Mesh, MENSA, Mobisec, and AHKM). The experimental solutions show the significance of the proposed work with 10% to 12% of better performance than the existing techniques.

Section: Related Workmentioning

confidence: 99%

Efficient Key Management Mechanism with Trusted Gateways for Wireless Mesh Networks

Karri¹,

Wireless Communications and Mobile Computing

Routray

et al. 2022

Self Cite

“…Due to the gender variability and age particulars, the structures and lengths of bones are dissimilar in x-ray images. [33][34][35][36] Normalization techniques are used to deal with dimensional differences of any image data points. At the same time, the data recreation is not optimal on shrunken images.…”

Section: Human Bone Fracture Detection Using ML Techniquesmentioning

confidence: 99%

“…Region detection techniques are commonly used with Sobel, Laplacian, and Gradient for ensuring an efficient edge detection quality. Due to the gender variability and age particulars, the structures and lengths of bones are dissimilar in x‐ray images 33–36 . Normalization techniques are used to deal with dimensional differences of any image data points.…”

Section: Human Bone Fracture Detection Using ML Techniquesmentioning

confidence: 99%

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

Kumar

Concurrency and Computation

Pandimurugan

et al. 2022

Summary X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.

“…RPL is developed by IETF‐ROLL (internet engineering task force‐routing over low power and lossy networks) with feasible techniques to improve the transport layer quality. It is a proactive distance‐vector protocol 6–8 …”

Section: Introductionmentioning

confidence: 99%

“…It is a proactive distance-vector protocol. [6][7][8] A logical representation of a network is built as destination-oriented directed acyclic graphs. On the other hand, it is vulnerable to rank attacks and blackhole attacks.…”

mentioning

confidence: 99%

Cooperative and feedback based authentic routing protocol for energy efficient IoT systems

Gayathri

Concurrency and Computation

Rajasoundaran

et al. 2022

Self Cite

The open communication medium of the Internet of Things (IoT) is more vulnerable to security attacks. As the IoT environment consists of distributed power limited units, the routing protocol used for distributed routing should be light-weighted compared to other centralized networks. In this situation, complex security algorithms and routing mechanisms affect the generic data communications in IoT platforms. To handle this problem, this proposed system develops a cooperative and feedback-based trustable energy-efficient routing protocol (CFTEERP). This protocol calculates local trust value (LTV) and global trust value (GTV) of each node using node attributes and K-means-based feedback evaluation procedures. The K-means clustering algorithm leaves out the distorted node routing metrics and misbehaving node metrics for all channels. This proposed CFTEERP uses the nearest secure node costs to increase the network lifetime without selecting the nearest nodes for routing the data. In this work, secure routing is initiated using multipath routing strategy that analyses LTV, GTV, next trustable node, average throughput, energy consumption, average packet delivery ratio (PDR) and traffic various metrics of entire IoT communication. The technical aspects of proposed system are implemented to solve different existing techniques' limitations.In the comparative experiment, the proposed method provides 90% of PDR and a minimal energy consumption rate of 25% lesser than the existing systems against different malicious attacks.