Towards applying FCM with DBSCAN for Detecting DDoS Attack in Cloud Infrastructure to Improve Data Transmission Rate

Cloud is a pay-to-use technology which can be used to offer IT resources instead of buying computer hardware. It is time saving and cheaper technology. This paper analyzes the DDoS attack on cloud infrastructure and can be detected by using FCM with DBSCAN hybrid algorithm that classifies the clusters of data packets and detects the outlier in that particular data packet. The experimental outcome shows that the enhanced hybrid approach has better results in detecting the DDoS attack. The DDoS attack targets the main host of the cloud infrastructure by sending unwanted packets. This attack is a major threat to the network security. The FCM with DBSCAN hybrid approach detects outliers and also assigns one specific data point in clusters to detect DDoS attack in cloud infrastructure. By using this hybrid approach the data can be grouped as clusters and the data beyond the noise level can also be detected. This algorithm helps in identifying the data that are vulnerable to DDoS attack. This detection helps in improving the data transmission rate

A flap graft technique for the reconstruction of extensor mechanism of the knee in a case of peri-articular synovial sarcoma managed by limb salvage

Synovial sarcoma is a malignant soft tissue tumour arising from the primitive mesenchymal cells which has a delayed progression has a slow progression and often mis-diagnosed. A fifty-two years old female presented with complaint of swelling, pain and inability to move her left knee for three years duration. Presented with recurrence for which Wide excision of the tumour, with reconstruction of knee using custom mega-prosthesis and extensor mechanism reconstruction was done. Patient had a good functional outcome and no recurrence

Intelligent Vehicle Black Box System Using IoT

Humans are currently involved in numerous transportation-related accidents on the roads. Numerous accidents and fatalities caused by human error are the result of the rising number of vehicles on Indian roads and a lack of enforcement of traffic laws. They also suffer loss of life and valuable property as a result of those accidents. As a result, accidents involving driver inattention to traffic laws and driver fatigue follow. The field of computer vision is actively researching real-time eye detection and tracking. Face alignment can benefit from eye localization and tracking. The duration of eye closure and percentage of eye closure (PEC) can be used to measure driver fatigue. It is based on a hardware system that uses a camera to capture images of the driver in real time and software to monitor the driver's eye in order to prevent accidents. In the event of an accident, GSM and GPS are utilised to track the location of the automobile, and the local hospital and police are alerted. Thanks to IoT technology, this position may always be found in the cloud platform service. The 24/7 Governance is notified to call for emergency assistance by using the push and panic button

MXene-Embedded Porous Carbon-Based Cu₂O Nanocomposites for Non-Enzymatic Glucose Sensors

This work explores the use of MXene-embedded porous carbon-based Cu2O nanocomposite (Cu2O/M/AC) as a sensing material for the electrochemical sensing of glucose. The composite was prepared using the coprecipitation method and further analyzed for its morphological and structural characteristics. The highly porous scaffold of activated (porous) carbon facilitated the incorporation of MXene and copper oxide inside the pores and also acted as a medium for charge transfer. In the Cu2O/ M/AC composite, MXene and Cu2O influence the sensingparameters, which were confirmed using electrochemical techniques such as cyclic voltammetry, electrochemical impedance spectroscopy, and amperometric analysis. The prepared composite shows two sets of linear ranges for glucose with a limit of detection (LOD) of 1.96 μM. The linear range was found to be 0.004 to 13.3 mM and 15.3 to 28.4 mM, with sensitivity values of 430.3 and 240.5 μA mM−1 cm−2, respectively. These materials suggest that the prepared Cu2O/M/AC nanocomposite can be utilized as a sensing material for non-enzymatic glucose sensors

An Efficient and Secure Shortest-Path Discovery in Location-Based Services

Cloud computing is a rapidly growing technology that offers a cost-efficient infrastructure with on-demand scalability and increased flexibility. Security and Privacy has been the biggest hurdle for the widespread cloud migration. Specifically, the data stored in cloud carries a lot of user’s sensitive information and is vulnerable to attacks because of reduced visibility and control. A commonly used data structure in computing is Graph, which is flexible and dynamic. Graphs are ubiquitous and are becoming more pervasive across various domains. One such domain is military, where the graph theory is employed to determine the organization structure and network flow which aids in transporting the supplies for optimal usage of resources while attacking the target. In our project, we focus on Location-Based Services, where users want to retrieve the services like map directions without disclosing their locations. In particular, our research aims to address the following problem: Given a user’s location S and a preferred destination T over a graph G, can we retrieve the shortest path route from S to T in a privacy-preserving manner? Although, there exist some solutions for this problem, they are either inefficient or insecure. To address this gap, we proposed an efficient and secure solution based on homomorphic encryption with novel data aggregation technique. Additionally, we investigate the security and complexity of our solution and present a comparative performance analysis to demonstrate the superiority of the proposed solution compared to the existing work

ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. Specifically, our paper aims to address the following problem: Given a user’s location s, a target destination t, and a graph G stored in a cloud, can users retrieve the shortest path route from s to t in a privacy-preserving manner? Although there exist a few solutions to this problem, they are either inefficient or insecure. For example, existing solutions either leak intermediate results to untrusted cloud providers or incur significant costs on the end-user. To address this gap, we propose an efficient and secure solution based on homomorphic encryption properties combined with a novel data aggregation technique. We formally show that our solution achieves semantic security guarantees under the semi-honest model. Additionally, we provide complexity analysis and experimental results to demonstrate that the proposed protocol is significantly more efficient than the current state-of-the-art techniques

Intelligent Vehicle Black Box System Using IoT

Humans are currently involved in numerous transportation-related accidents on the roads. Numerous accidents and fatalities caused by human error are the result of the rising number of vehicles on Indian roads and a lack of enforcement of traffic laws. They also suffer loss of life and valuable property as a result of those accidents. As a result, accidents involving driver inattention to traffic laws and driver fatigue follow. The field of computer vision is actively researching real-time eye detection and tracking. Face alignment can benefit from eye localization and tracking. The duration of eye closure and percentage of eye closure (PEC) can be used to measure driver fatigue. It is based on a hardware system that uses a camera to capture images of the driver in real time and software to monitor the driver's eye in order to prevent accidents. In the event of an accident, GSM and GPS are utilised to track the location of the automobile, and the local hospital and police are alerted. Thanks to IoT technology, this position may always be found in the cloud platform service. The 24/7 Governance is notified to call for emergency assistance by using the push and panic button