Research

For the information technology sector, cybersecurity is essential. One of the main issues in the modern world is sending information from one system to another without letting the information out. Online crimes, which are on the rise daily, are the first thing that comes to mind when we think about cyber security. Various governments and businesses are adopting a number of actions to stop these cybercrimes. A lot of individuals are still quite worried about cyber security after taking many safeguards. This study’s primary goal is to examine the difficulties that modern technology-based cyber security faces, especially in light of the rising acceptance of cutting-edge innovations like server less computing, blockchain, and artificial intelligence (AI). The aim of this paper is to give readers a good overview of the most recent cyber security trends, ethics, and strategies. This study focuses on the present state of cyber security and the steps that may be taken to address the rising dangers posed by modern technology through a thorough investigation of the existing literature and actual case studies.

Past few years have been transformed greatly due to the rapid progress in technologies Machine Learning, Natural Language Processing, Metaverse, Generative AI, and Artificial Intelligence. These changes mark a new era with large language models like GPT-3, GPT-4, and Llama2 gaining importance. These models, trained on vast text data, generate human-like text and find use in content creation, Q&A systems, and chatbots. Machine learning advances enable models like ChatGPT to respond to real-time text prompts. However, they are vulnerable to attacks, leading to inaccurate or harmful content. Adversarial prompting addresses these issues, including input manipulation, creating inducive prompts which causes LLMs to produce illegal content, simulating violence and harm, privacy breaches and other illegitimate actions. Large language models are especially prone to text attacks, with minor changes resulting in different outputs. Researchers are improving model security through adversarial training. To enhance the security of large language models and prevent them from responding to adversarial prompts, we aim to utilize machine learning, deep learning and transformer models as external prompt classifiers for LLM-based applications like ChatGPT, to detect and counter malicious prompts that may lead to inappropriate responses from LLMs. We have conducted analyses on multiple machine learning, deep learning, and Transformer-based models, incorporating techniques such as ensembling and attention mechanisms. These models are trained on word-level text classification to categorize prompts into the adversarial category, preventing them from being answered by LLMs. We compared our results with those of other ML and DL models, and our proposed ensemble of the BERT-DistilBERT model achieved the best outcome with 97.56% accuracy in identifying malicious/adversarial prompts.

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Spear-phishing emails are an effective cyber-attack method due to the fact that the emails sent are highly personalized to look like a regular legitimate email. Recently, it was discovered that personality traits of the victim have an impact on a person’s susceptibility to spear-phishing. This study aims to identify which personality traits affect spear-phishing susceptibility besides other traits such as Information Technology background, gender, and age. In addition, measure of the effectiveness of embedded training systems and see whether message framing can further help increase its effectiveness. A personality trait survey was sent to 100 participants, followed by a real-life spear-phishing simulation to measure a certain personality trait’s influence on phishing susceptibility. After a two-week period, the second round of spear-phishing emails was sent again to measure message framing effectiveness. The personality traits analysis results show that users with higher levels of Internet anxiety are less susceptible to spear-phishing emails. While the message framing did not show any significant results, the embedded training program reduced the click rate. Findings revealed that certain people are more susceptible to spear-phishing emails than others. Thus, this work can guide an institution or organizations to identify which group of people are more vulnerable to spearphishing.

The evolution of industry has been significantly influenced by blockchain technology. Regarding security, data access, auditing, and transaction management inside digital platforms, blockchain decentralized technology and privacy protocols offer potential benefits to many businesses. Blockchain is based on distributed and secure decentralized protocols; there is no central authority or point of control, and the network’s nodes generate, add, and validate the data blocks. Blockchains enable transparency by allowing each participant to observe transactions at any moment and smart contracts provide secure transactions, reducing the risk of third-party disruption. Ethereum is a decentralized platform that facilitates the execution of smart contracts. This allows developers to design markets that move funds in accordance with instructions issued years ago. Decentralization and immutability are the principal characteristics of blockchain and promote faster transactions and validation. The security and applications of blockchain technology can be reviewed and improved further. These security concerns ought to have an impact on transactions and offer some protection against certain attacks and provide some solutions to these problems.

Recently, machine learning models have become a key methodology in detection of cardiovascular diseases (CVD). This gives medical practitioners diagnostic support and indicators. In this work, we compare various machine learning (ML) classification algorithms, apply them to disease dataset and examine how these algorithms perform when subjected to either of the classes to aid in the study and investigation of CVD through computer-aided diagnosis (CAD). Our two main goals in this work are to first offer an automated machine learning ensemble model for categorizing cardiovascular malignancies and second to compare the performance of several classification algorithms to find the best classifier for the task. The proposed technique is specifically developed as a potential support for clinical care based on patient diagnostic data. The proposed approach exhibits an accuracy of 94.28% in the detection of cardiac illnesses when a thorough examination of binary classification is performed and averaged over numerous model training iterations. We believe that incorporating the suggested ensemble methods would produce stable and dependable CAD systems.

Cobots, or collaborative robots, have significantly increased productivity and efficiency across a number of industries. Cobots exist alongside humans and interact with crucial systems, but this growing reliance on them also poses security issues. By recognizing and thwarting potential security threats and attacks, adversarial learning, a branch of machine learning, has emerged as a promising method to improve the security of Cobots. This chapter explores the use of adversarial learning in Cobot-driven sectors with a focus on identifying and reducing security vulnerabilities. We investigate different adversarial learning methods, including Generative Adversarial Networks (GANs) and adversarial training, and their potential uses in Cobot security. This chapter also explores how adversarial learning is used in Cobot-driven sectors, with a focus on identifying and reducing security vulnerabilities. The chapter also examines computational cost and scalability as well as other drawbacks of adversarial learning in this setting. To solve these issues and establish a secure environment for Cobots in industrial settings, we also propose prospective future research trends and advancements. This chapter seeks to contribute to the safe and reliable integration of Cobots, ensuring human safety and encouraging the continuous expansion of Cobot-driven enterprises by understanding and utilizing adversarial learning to minimize security threats.

This study introduces an original comprehensive system centered on identifying specific terms that indicate a user’s position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.

The global impact of the COVID-19 pandemic has extended across various facets of human existence, leading to a significant loss of life and overwhelming medical resources. Furthermore, the global economy has suffered considerably due to widespread job losses, resulting in severe financial disruptions. Diverse segments of society have made varied contributions to curb the virus’s propagation and safeguard public health. Notably, commendation has been directed towards medical researchers for their dedicated endeavors in crafting COVID-19 vaccines. Rigorous clinical testing has affirmed the remarkable efficacy of these vaccines in mitigating symptomatic COVID-19 occurrences. However, a notable portion of the global population has exhibited reluctance towards receiving COVID-19 vaccinations. This reluctance stems from misconceptions surrounding the vaccines, a trend that has been exacerbated through a confluence of factors, encompassing the unrestricted access to internet-based information and the influential effect wielded by prominent personalities and authoritative figures. In this particular context, we have conducted an evaluation of responses furnished by the ChatGPT system concerning inquiries pertinent to vaccine fallacies. The affirmative and supportive perspectives articulated by the AI chatbot could potentially assume a pivotal role in molding individuals’ outlooks on vaccinations. Additionally, these responses have the capacity to effectively motivate users to embrace vaccination, thereby effectively diminishing prevalent misconceptions.

Breast Cancer is the leading form of cancer found in women, and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset, with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques, as well as the comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset. The findings of this study are in line with those of previous studies, which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several models, including SVM, Random Forest Classifier, Decision Tree, Logistic Regression, Naive Bayes, KNN, Decision Tree, Adaboost, Gradient Boost, XGBoost, LGBM Classifier, and a Custom Classifier, achieved high accuracy, precision, and F1 scores for benign and malignant tumours, respectively, according to the findings from the study. We also found that models with hyperparameter adjustment performed better than those without, and that boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumors. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the data’s complexity and nonlinearity. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.

Significant concentration of air pollutants can have a number of negative health effects. It increases the risk of respiratory infections, lung cancer, and heart problems. Air pollution exposure, both short-term and long-term, has been linked to negative health effects. People who are already ill are more susceptible to negative consequences. Modern human culture has had constant advancements that have had a negative impact on the quality of the air. Daily transportation, industrial, and domestic operations churn up dangerous contaminants in our surroundings. In the modern day, air quality monitoring and forecasting have become crucial tasks, particularly in developing nations like India. Contrary to conventional ways, machine learning-based prediction technologies have emerged as the most effective instruments in recent years for researching such contemporary threats. This study analyses and forecasts air quality using data on air pollution from Victoria and Rabindra, two distinct areas in Kolkata. The dataset has undergone thorough pre-processing, and the correlation analysis has been used to identify essential features. To get insights into numerous hidden patterns in the dataset, an exploratory data analysis is carried out, and each pollutant is transformed to its sub-indexes based on certain ranges for each pollutant. In the pandemic year of 2020, practically all pollutants show a significant decrease. To predict air quality, five machine learning models with hyperparameter adjustment are used. These models’ outputs are compared with the accepted metrics. The Support Vector Machine model has the lowest accuracy, while the Random Forest Classifier model obtains the maximum accuracy. Through the use of established performance parameters, the performances of these models are assessed and contrasted. Among the other models, the Naive Bayes model fared the best and achieved the highest linearity between the predicted and actual data.

The book "Building Blockchain-powered Decentralized Applications with Solidity: A Project-Oriented Guide" is a comprehensive guide for anyone looking to learn and build decentralized applications using blockchain technology. The book is divided into three sections, each covering different aspects of the topic. The first section, "Introduction, Installation and Solidity Programming Language," covers the fundamentals of blockchain technology and decentralized applications (DApp), including a brief overview of their evolution, requirements for development and deployment, and a kick-start to the Solidity programming language. The second section, "Practical Projects on Blockchain," focuses on providing hands-on experience by working on real-life projects, such as a decentralized voting application, among others. This section provides practical and project-based learning, allowing readers to understand the concepts in a more interactive and engaging way. The third section, "Blockchain with Other Advance Technologies," covers the integration of blockchain with other advanced technologies such as InterPlanetary File System (IPFS), Internet of Things (IoT), Decentralized Autonomous Organizations (DAOs), Decentralized Finance (DeFi), and Non-Fungible Tokens (NFTs). This book takes a practical and project-based approach to learning and building decentralized applications, making it a valuable resource for anyone looking to start a career in blockchain development or those looking to expand their knowledge of the technology. The book is also ideal for professionals and practitioners looking for a comprehensive guide on the subject.