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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Naithani, Anukriti Singh, Shailendra Narayan Kant Singh, Krishna Kumar, Sushil Computer Science and Engineering Aset Amity University Noida India Computer Science and Engineering Nit Kurukshetra India

ISBN: (纸本)9798350344837

Distributed denial of service attacks, in the recent times, have become a critical threat to not only private server connections but also meta scale cloud computing environments. This research paper furnishes a detailed analysis of five machine learning algorithms in context with network intrusion detection that are;Isolation Forest, Random Forest, Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The dataset used in this study, to differentiate between attack and normal cases, is the KDD cup 1999, wherein the dataset has been converted into binary classes to differentiate between the attack and normal cases. Following are the part of the research;Preprocessing the data, Scaling the features, Training the model and evaluating the performance using crucial metrics like accuracy, precision and F1 score and recall. Graphical representation and confusion matrices for data visualization have been made use of in addition to the aforementioned. The results show a penetrative information about the relative effectiveness and success of different ML algorithms in identifying network intrusions and hence can be used as insightful references for academics and practitioners in the field of network security. A well- explained algorithm selection, based on performance indicators enables an advanced approach to the study, thus providing a framework for further developments in systems of intrusion detection. © 2024 IEEE.

关键词： Denial-of-service attack

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Recognition of Facial Emotions Using CNN Architecture and FER2013

Recognition of Facial Emotions Using CNN Architecture and FE...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Thomas, Bernard Bhatt, Adityavikram Singh, Shailendra Narayan Amity University ASET Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350378092

Humans are social and emotional beings. With the rapid normalization of work-from-home and hybrid work environments, most people are having a difficult time truly expressing their emotions, which when not addressed inevitably leads to drastic mental problems. In order to address or prevent these mental problems, detection and recognition of facial emotions serve as an initial step. This paper presents a facial emotion recognition model working on the CNN (convolutional neural network) architecture, trained and tested using the FER2013 dataset, achieving an accuracy of 74% of detecting seven emotions - Happy, Neutral, Fear, Sadness, Anger, Disgust, and Surprise. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Recognition and Classification using CNN: A Review

Image Recognition and Classification using CNN: A Review

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Yaseen, Maroofa Singh, Shailendra Narayan Amity University Noida Department of Computer Science and Engineering ASET Uttar Pradesh India

ISBN: (纸本)9798350378092

This review paper has examined the field of foggy image detection and defogging algorithms using convolutional neural networks to handle the challenges and limitations of poor visibility during harsh weather conditions such as fog. A detailed comparison of three of the most successful fog detection algorithms - FogNet, FogCNN, and DeepFog - based on a set of performance indicators including precision, recall, F1 score, false positive rate (FPR), false negative rate (FNR), and the area under the receiver operating characteristic curve (AUC-ROC) has been conducted. From the performance evaluation, it is clear that DeepFog offers the best performance in distinguishing between foggy and non-foggy images with higher levels of accuracy. In addition, we have surveyed various imaging techniques with several methodologies that outperformed previously published defogging algorithms based on metrics such as Structural Similarity Index and Peak Signal-to-Noise Ratio. Moreover, we also consider the future development of such technology through multi-modality fusion strategies, real-time optimization, adversarial training, and human-centric applications to give an impression of the advancement of robust systems for these real-world use scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Generative Adversarial Networks (GANs) in computer-Generated Imagery 2

Generative Adversarial Networks (GANs) in Computer-Generated...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Shah, Sannan Ahmad Drabu, Shahhab Hasin Khan, Murtaza Mahdi Kirmani, Dawar Bin Qayoom Singh, Shailendra Narayan Amity University Computer Science and Engineering Department Aset Noida India

ISBN: (纸本)9798350369960

In order to estimate the model using the method it is necessary to train two different models simultaneously. The first model, called the model (D) is responsible, for determining whether a particular sample is from the training data or generated by another model called G. On the hand G aims to understand the underlying distribution of the data. By training G to maximize the likelihood of D making mistakes a competitive environment is established. There are applications for G and D but one particularly notable solution emerges. D consistently evaluates any input as if it came from training data while G effectively captures the details of that training data distribution. Notably this system can be trained using known techniques like backpropagation when implementing G and D as multilayer perceptrons. This eliminates the need for inference networks or intricate Markov chains, during both training and sample generation processes. Thorough evaluations of the generated samples confirm the potential of this framework as demonstrated through testing. This innovative approach offers an efficient means to capture data distributions, which could potentially revolutionize generative modeling. © 2023 IEEE.

关键词： Deep learning Generative Adversarial Network (GAN) Generator and discriminator Image classification Image synthesis Image generation Machine learning algorithms Synthetic image generation

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A Brief Review Particle Swarm Optimization on Intrusion Detection System 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Nandana, G.M. Yadav, Ashok Kumar Department of Computer Science and Engineering ASET Amity University Noida India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

There has been an improvement in recent years towards increase in volume for remarkable data in the field of Internet and computer networks. This brings up significant security-related challenges. In the past, a number of systems that provide host or network-level security have emerged. Many conventional security measures, such as, firewalls, spyware, authentication methods and antivirus software provide some way towards ensuring security, but they are still vulnerable to cyber-attacks and other system vulnerabilities. Some noteworthy alternatives are discovered, namely Intrusion Detection & Prevention Systems are an intriguing development, but they also have significant drawbacks, such as the inability to identify new assaults and respond to them in real time. Overtime, many researchers have proposed to develop a robust as well as intuitive infrastructure intrusion detection systems (IDS) utilising a variety of neural network models, i.e. support vector machines (SVM), rough sets, Particle Swarm Optimization (PSO) and many other versions. In this paper, we discuss all proposed models have been implemented by researchers in improving IDS performance with PSO model. Also, considering different IDS implemented, we can derive various IDS datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Network security

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IntelliFarmAssist – A Novel Machine Learning integrated Genetic Algorithm Based Optimal Crop Recommendation System 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Mala, D. Jeya Basak, Swetaswa Reynold, A. Pradeep School of Computer Science and Engineering Vellore Institute of Technology Chennai India ASET College of Science and Technology Chennai India

ISBN: (数字)9783031648502

ISBN: (纸本)9783031648496

Crop recommendation is a critical task for agricultural productivity, and weather and soil conditions play a significant role in determining the suitable crop to grow. In this research, an intelligence assisted approach is used to develop a crop recommendation system that takes into account the weather and soil content of a particular region. The system uses weather and soil data to create objects that represent the conditions, and then applies a set of rules used as the fitness criteria in the proposed Genetic Algorithm to determine the optimal crop for the given conditions. The approach allows for flexible and modular design, making it easy to modify and extend the system. The results demonstrate the effectiveness of the proposed system in providing accurate and timely crop recommendations, which can help farmers to make informed decisions and improve their yield. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart city

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Enhanced Security in MANETs Using AODV Protocol

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 327-338页

作者： Gupta, Sunil Singla, Sanjay Sharma, Purushottam Chitkara University Punjab Rajpura India Department of Computer Science and Engineering UIE Chandigarh University Punjab Mohali India ASET Amity University Uttar Pradesh Noida India

This research study introduces an innovative security approach for MANETs to use with the AODV (Adhoc On-Demand Distance Vector) routing protocol, which can protect against attacks like the explicit packet-dropping assault and the implicit packet-dropping attack. The first iteration of the AODV routing protocol had no safeguards in place. As a result, it can’t defend itself against any kind of cyberattack. There are several safeguards available, therefore AODV is completely safe. Attackers in a network can be reduced using the same detection technologies that can find them. The topic was investigated further, and a catastrophic fault in all current safe routing techniques was discovered. Thus, present security measures are labor-intensive and call for an intricate key-management architecture. Using digital signatures and a hash chain, we introduce a new approach to protecting the AODV routing system in this study. This method protects itself from untrusted or malicious nodes with minimum performance hits. © The Institution of Engineers (India) 2024.

关键词： Iterative methods

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A new framework for distributed clustering based data aggregation in WSN

A new framework for distributed clustering based data aggreg...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Singh, Anuj Kumar Bhushan, Shashi Kumar, Ashish School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science ASET Amity University Punjab Mohali India Department of Computer Science and Engineering AKG Engineering College Ghaziabaad India

ISBN: (纸本)9781032669663

Wireless sensor networks (WSNs) are used in a variety of applications, such as industrial control systems, environmental monitoring, military applications, and structural health monitoring. Energy consumption is a key factor in the lifespan of a WSN, as all sensor nodes are energy-constrained devices. In order to improve energy efficiency, various routing protocols have been developed. These protocols aim to establish paths between sensor nodes and a data sink, while also maximizing the lifespan of the network. However, designing such protocols can be challenging due to the resource and power constraints, as well as the harsh conditions in which sensor nodes often operate. In this paper, a novel approach is proposed for improving energy efficiency in WSNs through the use of a distributed clustering-based data aggregation (DCDA) algorithm. In this approach, we use a grid structure as the basis for clustering, with each cluster having a designated cluster head (CH). Rather than transmitting data directly to the base station (BS), sensor nodes (SNs) send their data to their cluster head, which then aggregates and transmits the data to the BS. This approach can extend the network lifetime by reducing the energy required for communication between SNs. Simulations were conducted to evaluate the performance of the DCDA algorithm in a grid-based WSN. The results showed that using the specified parameters for clustering in the protocol significantly reduces the overall energy consumption of the entire network. © 2024 The Author(s).

关键词： Sensor nodes

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Cloud Cryptography: Mechanism of Different Encryption Standards 18

Cloud Cryptography: Mechanism of Different Encryption Standa...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Lanke, Ravikumar Md Zubair Rahman, A.M.J. Bhardwaj, Rajat Somashekhara Reddy, D. Jain, Parth Mahesh, T.R. Dept. of Computer Science and Engineering Bangalore India Al-Ameen Engineering College Karundevanpalayam Erode India Aset Amity University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

The term 'cloud cryptography' describes the application of cryptographic methods and protocols to protect communications and data in cloud computing settings. The provision of computing services, including storage, processing power, and applications, via the internet is known as cloud computing. Although cloud computing has numerous advantages, such as cost-effectiveness and scalability, it also poses security risks because sensitive data is processed and stored outside of the conventional network borders of an organization. Cloud cryptography plays a pivotal role in cloud security by safeguarding against unauthorized access, data disclosure, modification, or destruction. Keeping up with the most recent cryptographic methods and protocols is crucial to addressing new threats and weaknesses in this industry, which is always changing. Organizations can safeguard their data from unauthorized access, modification, and destruction by utilizing encryption techniques. This article explores the cloud cryptography along with its different encryption standards. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Architecture Benefits Cloud Computing Cryptography Encryption

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Unravelling the Potential of Knowledge Graphs and Graph Neural Networks: Techniques and Applications

Unravelling the Potential of Knowledge Graphs and Graph Neur...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Om, Shreyas Dogra, Anika Singh, Ananya Dubey, Ashwani Kumar Sharma, Puneet ASET Amity University Uttar Pradesh Computer Science and Engineering UP Noida India ASET Amity University Uttar Pradesh Electronics and Communication Engineering UP Noida India SR University School of Computer Science & Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350385199

In today's digital economy, organisations must manage an excessive volume of unstructured data. Traditional data warehouses are frequently underutilised due to inconsistencies in how to successfully evaluate such massive amounts of information. Knowledge graphs have developed as an effective solution, giving a structured representation of interrelated facts to improve data organisation, retrieval, and inference across several disciplines. Knowledge graphs, which capture interactions between elements such as people, locations, and concepts, provide a complete framework for interpreting and identifying hidden patterns in data. This study investigates the evolution of knowledge graphs, from simple data integration tools to complex systems that support semantic searches. These searches go beyond standard keyword searches, providing context-aware results that increase the relevance and accuracy of information retrieval. In addition, the study investigates the use of knowledge graphs in recommendation systems, emphasising their capacity to personalise user experiences through personalised suggestions. This research highlights the potential of knowledge graph technology to improve data management and utilisation, promoting innovation and efficiency across a variety of sectors. © 2024 IEEE.

关键词： Knowledge graph

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