iosr-JCE   Volume-25 ~ Issue-3 ~ May - June 2023

Abstract: For years' data has been a critical part of the technology and data has been perceived with the growth in technology and the population. This data is often referred to as NoSQL and unstructured data. Over the period of time, it is growing in complexity for traditional database management systems to manage some enormous databases in a virtual cloud environment. Nowadays different cloud services are offered like NoSQL databases to manage such Non-relational data which is dressing different requirements like availability, reliability, performance, safety as well as security. Hence there is a need to evaluate processes and find results in the performance and behavior of different NoSQL databases HBase and Cassandra using YCSB. The scale of big data will come from different sources it will be.....

Key Word: Big Data, Hadoop, NoSQL Databases, Cassandra, HBase, YCSB Application.

[1]. Ahmed, M. Razu, M. Arifa Khatun, A. Ali, and Kenneth Sundaraj. "A literature review on NoSQL database for big data processing." Int. J. Eng. Technol 7, no. 2 (2018): 902-906. "Apache HBase Architecture Overview." https://hbase.apache.org/book.html Accessed on: 04-08-2021.
[2]. Jakkula, Prashanth. "HBase or Cassandra? A Comparative Study of NoSQL Database Performance." International Journal of Scientific and Research Publications 10, no. 3 (2020): 808-820. "Apache Tephra." https://tephra.apache.org/ Accessed on: 04-08-2023.
[3]. Jakkula, Prashanth. "HBase or Cassandra? A Comparative Study of NoSQL Database Performance." International Journal of Scientific and Research Publications 10, no. 3 (2020): 808-820.
[4]. Khattab, AbdAlhamid, Alsayed Algergawy, and Amany Sarhan. "MAG: A performance evaluation framework for database systems." Knowledge-Based Systems 85 (2015): 245-255.
[5]. Kabakus, Abdullah Talha, and Resul Kara. "A performance evaluation of in-memory databases." Journal of King Saud University-Computer and Information Sciences 29, no. 4 (2017): 520-525..

Abstract: V-slot loaded Triangular patch antenna analysis using Machine learning ANN technique. Initially fractal antenna is designed and analyzed using HFSS tool. The training data is generated from the HFSS tool. Later, training data is applied to ML Random Forest. A triangular patch with a relative permittivity of 4.4 and a thickness of 0.6 mm is developed on a FR4 substrate and mounted 6 mm above the ground plane. With consistent pattern properties, like gain and cross polarisation, within its bandwidth, bandwidth as high as 9.2% are attained. This design technique involves inserting a single feed and two small slots in the shape of a V on the microstrip antenna......

Key Word: HFSS Tool, Python, ML-Algorithms, Linear Regression, Decision Tree Regressor, Random Forest Regressor, Elastic net, Lasso..

[1]. C. Aissaoui, A. Benghalia, "Comparison of resonant frequencies of rectangular, isosceles and equilateral micro strip antennas having the same patches surface",2012 IEEE conference, Algeria.
[2]. A.A. Deshmukh, "Broadband gap -coupled isosceles triangular microstrip antennas",2017 international conference on emerging trend & innovation in ICT, pune, India.
[3]. Amit A. Deshmuk," Broadband 90o isosceles triangular microstrip antenna",2015 fifth international conference on advances in computing and communications, Mumbai, India.
[4]. James, J.R. and Hall, P.S.: "Handbook of Microstrip Antennas" (Peter Peregrinus).
[5]. Constantine A. Balanis : "Antenna Theory, Analysis and Design" (John Wiley & Sons)..

Abstract: The scope of the establishment of internetworking security is large enough that requires special attention to details to realize its ultimate wholesome state. As soon as the Data/Information is placed in a medium to transmit over to the next hop, it is at risk of being fabricated by the Man-in-the-middle. Over the years many encryption/decryption algorithms were invented to ensure the integrity and authenticity of the content co compiled at the sender end. Cryptography is a subset of cryptography that only deals with encrypting data that can be passed through secure channels along with keys to ensure security.......

Key Word: Cryptology, Cryptography, Crptanalysis, Symmetric Key Encryption, Assymetric Key Encryption, Man-in-the-middle.

[1]. https://techblogs.42gears.com/encrypt-and-decrypt-a-message-using-des-algorithm-in-python/
[2]. Maqsood, F., Ahmed, M., Ali, M. M., & Shah, M. A. (2017). Cryptography: a comparative analysis for modern techniques. International Journal of Advanced Computer Science and Applications, 8(6).
[3]. Hercigonja, Z. (2016). Comparative analysis of cryptographic algorithms. International Journal of Digital Technology & Economy, 1(2), 127-134.
[4]. Jeeva, A. L., Palanisamy, D. V., & Kanagaram, K. (2012). Comparative analysis of performance efficiency and security measures of some encryption algorithms. International Journal of Engineering Research and Applications (IJERA), 2(3), 3033-3037.
[5]. Thambiraja, E., Ramesh, G., & Umarani, D. R. (2012). A survey on various most common encryption techniques. International journal of advanced research in computer science and software engineering, 2(7).

Abstract: Systems that can interpret text descriptions to build an image allows humans to visually express their thoughts without words. The goal is to develop a system that will generate images based on the text descriptions provided by the user. With a manifold of increasing applications in real time from interior designing, fashion designing to art generation and photo editing, the most taxing part is to produce a model trained on a massive dataset, that can develop photorealistic images and generate the image as envisioned by humans. This project primarily focuses on using a vast dataset and generating the human visualized image

Key Word: Generative Adversarial Networks (GAN), Deep Learning, Text Descriptions, Image Generation, Generator, Discriminator.

[1]. Wang Z, Liu W, He Q, Wu X, Yi Z. CLIP-GEN: "Language-Free Training of a Text-to-Image Generator with CLIP." arXiv preprint arXiv:2203.00386. 2022 Mar 1, https://doi.org/10.48550/arXiv.2203.00386
[2]. Ramzan S, Iqbal MM, Kalsum T. Text-to-Image Generation Using Deep Learning. Engineering Proceedings. 2022 Jul 29;20(1):16,
[3]. https://doi.org/10.3390/engproc2022020016
[4]. Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. "High-resolution image synthesis with latent diffusion models." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684-10695. 2022.,arXiv:2112.10752
[5]. Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G. Learning transferable visual models from natural language supervision. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 8748-8763). PMLR, http://proceedings.mlr.press/v139/radford21a/radford21a.pdf

Abstract: Mobile messaging service is increasingly used to disseminate necessary information in the event of an emergency. As a result, a wide range of institutions, including colleges and universities are currently partnering with third-party vendors promises to improve physical security by rapidly providing such post. Unfortunately, these products do not work as announced due to mobile infrastructure limitations and thereby giving their users a false sense of security. In this article, we do our first in-depth investigation and characteristics of the boundaries of an emergency alert System (EAS) uses SMS as a security incident feedback mechanism. Do we display text message-based emergency alert systems ? can only respond to delivery requests in 10 minutes due to......

Key Word: Emergency Alerting System, IoT Devices, Sensor Data Collection, Cloud Computing, Mobile Application Development.

[1] In 2011 L Chun-Yuan Has Designed an Intelligent Fire Alarm System Based on GSM: - the purpose of the project is to solve the problem of complex cabling, missed declaration, and missing alarm of traditional fire alarm system. By adapting smoke detectors and using a variable threshold alert algorithm with temperature compensation the accuracy of the fire alarm is improved.
[2] In 2015 K Sen, J Sarkar, S Saha, A Roy, D Dey, And S Baitalik, has developed an automated fire detection and controlling system based on smoke and heat detection: - it is comprised of a combination of electrical/electronic devices/equipment working together to detect the presence of fire and alert people through an audio or visual medium after detection. the system will send short message service (sms) to the registered mobile numbers and switch on a water sprayer or a solenoid pump to spray water or fire ceasing foam.
[3] In 2013 P Y Mulge, Has Designed Remote Temperature Minoring Using a Wireless Sensor Network Prototype for Remote Room Temperature Monitoring: - this network will be used for the management of fire rescue operations. lm35 sensor senses the remote room temperature and temperature status is transmitted to the smartphone via gprs. this work aims at the monitoring of remote room temperature. this provides an opportunity to quickly respond to fire emergencies.
[4] Srinivasan, Leela, Jeya Bharathi, Kirthik, Rajasree: - in this research paper they talked about gas leakage detection and control. in this paper, the gas leakage resulting into fatal inferno has become a serious problem in household gas is handled and used. It alerts the subscribe through the alarm and the status displays beside turning off the gas supply valve as a primary safety measure

Abstract:Now a days information is generating enormously using smartphones, laptops ,sensors, surveillance cameras, etc in various places such as Universities, Digital libraries, Data centres, Emails, Legal documents, social media, chatbots, Surveys, Medical fields, Sensors, Shopping malls, public and private sectors, etc .In digital world, It is estimated that around 80% of all information is unstructured. Unstructured means no format for stored data or information is in sentence format. Hence classification of unstructured text is becoming very important task of all type of organizations as it allows to easily do data analysis from unstructured data and automate real time on going processes. Text classification is the process of categorizing.....

Key Word: Unstructured data, Keywords, Key phrases, concept, unigram, Context Relevance score, Central metrics..

[1]. Mohammad Badrul Alam Miah, Suryanti Awang, Md Mustafizur Rahman, A. S. M. Sanwar Hosen and In-Ho Ra, "A New Unsupervised Technique to Analyze the Centroid and Frequency of Key phrases from Academic Articles", Electronics 2022, 11, 2773, doi:10.3390/electronics11172773
[2]. Abdullah Alqahtani, Habib Ullah Khan2, Shtwai Alsubai, Mohemmed Sha, Ahmad Almadhor, Tayyab Iqbal4 and Sidra Abbas, "An efficient approach for textual data classification using deep learning", Frontiers in Computational Science, 2022, DOI 10.3389/fncom.2022.992296
[3]. Dmitriy Dligach, Timothy Miller, Steven Bethard and Guergana Savova, "Exploring Text Representations for Generative Temporal Relation Extraction", Proceedings of the 4th Clinical Natural Language Processing Workshop, pages 109 - 113 July 14, 2022, 2022 Association for Computational Linguistics
[4]. Anna Glazkova and Dmitry Morozov, "Applying Transformer-based Text Summarization for Keyphrase Generation", 2022, DOI:10.48550/arXiv.2209.03791
[5]. Miah, M.B.A.; Awang, S.; Azad, M.S.; Rahman, M.M, "Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach", Int. J. Adv. Comput. Sci. Appl. 2022, 13, pp. 788-796

Abstract: This paper describes how penetration testing works in the technical field with automated tools use for testing for securing our web applications. We will also look at how to prevent ourselves from such attacks and how to implement a keylogger inside the system to act as a monitoring tool for an organization. In global cyber threat landscape has seen increasing in recent years. Cybercriminal has taken advantage of remote work because of the pandemic businesses has moved to a remote environment. These breaches cost businesses an an-average loss of 4.35 million dollars in the year 2022 and around 236.1 million ransomware-attacks reportedly globally in the year 2022. An attack on a web resource is an action that can exploit a vulnerability, or it can enact a new threat. Network......

Key Word: Penetration Testing, Cyber security, data protection, confidentiality, vulnerabilities, cyber awareness..

[1]. Kaur, S., & Singh, H. (2016). A DESCRIPTIVE REVIEW OF DIFFERENT PENETRATION TESTING TOOLS AND METHODS. IJESRT, (March 2016). https://doi.org/10.5281/zenodo.47030
[2]. Sindhu, P., Naveen, K., Akash, P., Bhaskar, K. U., & Kanuri, B. (2021). PENTEST BASED NETWORK SECURITY ASSESSMENT. JETIR, Volume 8, Issue 6(June 2021). https://doi.org/(ISSN-2349-5162)
[3]. Bacudio, A. G., Yuan, X., Chu, B. T. B., & Jones, M. (2011). An overview of penetration testing. International Journal of Network Security & Its Applications, 3(6), 19.
[4]. Weidman, G. (2014). Penetration Testing A Hands-On Introduction to hacking. William Pollock.
[5]. Pandey, P., & Mathur, D. P. (2019). SAFEGUARD YOURSELF FROM BEING HACKED THROUGH VAPT. JETIR, Volume 6(Issue 5). https://doi.org/(ISSN-2349-5162)

Abstract: The rapid proliferation of Edge Artificial Intelligence (Edge AI) has introduced significant cybersecurity challenges due to decentralized computation, limited resource constraints, and increased attack surfaces. Traditional security mechanisms often fall short in addressing dynamic and adversarial environments where intelligent attackers continuously adapt their strategies. This paper explores the application of game-theoretic algorithms to enhance cybersecurity in Edge AI ecosystems by modeling interactions between defenders (e.g., edge nodes, gateways) and attackers as strategic games......

Key Word: Edge Computing, Cybersecurity, Edge A, Threat Detection, Threat Mitigation, Intrusion Detection, Adversarial Modeling, Security Framework, Autonomous Systems Security, AI-driven Security.

[1]. Bu, K., Yu, F. R., Liang, X., & Liu, Q. (2019). A game-theoretic approach to secure and resilient resource allocation in fog computing. IEEE Transactions on Network and Service Management, 16(4), 1520–1533.
[2]. Cheng, R., Liu, F., Zhang, Z., & Xu, M. (2020). Game-theoretic approaches for securing edge computing: A survey. IEEE Internet of Things Journal, 7(7), 5828–5842.
[3]. Chen, M., Zhang, Y., & Li, Y. (2019). Intelligent offloading for mobile edge computing in 5G heterogeneous networks: A deep reinforcement learning approach. IEEE Transactions on Wireless Communications, 18(11), 5304–5317.
[4]. Elazab, A., Abd El-Latif, A. A., & El-Samie, F. E. A. (2019). A game-theoretic model for securing data transmission in IoT networks. Journal of Network and Computer Applications, 126, 68–75.
[5]. Fung, C., Zhang, Z., & Boutaba, R. (2021). A comprehensive survey on network virtualization attacks and defenses. IEEE Communications Surveys & Tutorials, 23(2), 961–996.