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Xiong, Leilei, Grijalva, Santiago.  2019.  N-1 RTU Cyber-Physical Security Assessment Using State Estimation. 2019 IEEE Power Energy Society General Meeting (PESGM). :1–5.
Real-time supervisory control and data acquisition (SCADA) systems use remote terminal units (RTUs) to monitor and manage the flow of power at electrical substations. As their connectivity to different utility and private networks increases, RTUs are becoming more vulnerable to cyber-attacks. Some attacks seek to access RTUs to directly control power system devices with the intent to shed load or cause equipment damage. Other attacks (such as denial-of-service) target network availability and seek to block, delay, or corrupt communications between the RTU and the control center. In the most severe case, when communications are entirely blocked, the loss of an RTU can cause the power system to become unobservable. It is important to understand how losing an RTU impacts the system state (bus voltage magnitudes and angles). The system state is determined by the state estimator and serves as the input to other critical EMS applications. There is currently no systematic approach for assessing the cyber-physical impact of losing RTUs. This paper proposes a methodology for N-1 RTU cyber-physical security assessment that could benefit power system control and operation. We demonstrate our approach on the IEEE 14-bus system as well as on a synthetic 200-bus system.
Zhang, Zhiyi, Yu, Yingdi, Afanasyev, Alexander, Burke, Jeff, Zhang, Lixia.  2017.  NAC: Name-based Access Control in Named Data Networking. Proceedings of the 4th ACM Conference on Information-Centric Networking. :186–187.

As a proposed Internet architecture, Named Data Networking must provide effective security support: data authenticity, confidentiality, and availability. This poster focuses on supporting data confidentiality via encryption. The main challenge is to provide an easy-to-use key management mechanism that ensures only authorized parties are given the access to protected data. We describe the design of name-based access control (NAC) which provides automated key management by developing systematic naming conventions for both data and cryptographic keys. We also discuss an enhanced version of NAC that leverages attribute-based encryption mechanisms (NAC-ABE) to improve the flexibility of data access control and reduce communication, storage, and processing overheads.

Aguinaldo, Roberto Daniel, Solano, Geoffrey, Pontiveros, Marc Jermaine, Balolong, Marilen Parungao.  2021.  NAMData: A Web-application for the Network Analysis of Microbiome Data. TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON). :341–346.
Recent projects regarding the exploration of the functions of microbiomes within communities brought about a plethora of new data. That specific field of study is called Metagenomics and one of its more advancing approach is the application of network analysis. The paper introduces NAMData which is a web-application tool for the network analysis of microbiome data. The system handles the compositionality and sparsity nature of microbiome data by applying taxa filtration, normalization, and zero treatment. Furthermore, compositionally aware correlation estimators were used to compute for the correlation between taxa and the system divides the network into the positive and negative correlation network. NAMData aims to capitalize on the unique network features namely network visualization, centrality scores, and community detection. The system enables researchers to include network analysis in their analysis pipelines even without any knowledge of programming. Biological concepts can be integrated with the network findings gathered from the system to either support existing facts or form new insights.
Lim, H., Ni, A., Kim, D., Ko, Y. B..  2017.  Named data networking testbed for scientific data. 2017 2nd International Conference on Computer and Communication Systems (ICCCS). :65–69.

Named Data Networking (NDN) is one of the future internet architectures, which is a clean-slate approach. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/Data packets and innetwork caching. The continually increasing demand for rapid dissemination of large-scale scientific data is driving the use of NDN in data-intensive science experiments. In this paper, we establish an intercontinental NDN testbed. In the testbed, an NDN-based application that targets climate science as an example data intensive science application is designed and implemented, which has differentiated features compared to those of previous studies. We verify experimental justification of using NDN for climate science in the intercontinental network, through performance comparisons between classical delivery techniques and NDN-based climate data delivery.

Bouk, Safdar Hussain, Ahmed, Syed Hassan, Hussain, Rasheed, Eun, Yongsoon.  2018.  Named Data Networking's Intrinsic Cyber-Resilience for Vehicular CPS. IEEE Access. 6:60570–60585.
Modern vehicles equipped with a large number of electronic components, sensors, actuators, and extensive connectivity, are the classical example of cyber-physical systems (CPS). Communication as an integral part of the CPS has enabled and offered many value-added services for vehicular networks. The communication mechanism helps to share contents with all vehicular network nodes and the surrounding environment, e.g., vehicles, traffic lights, and smart road signs, to efficiently take informed and smart decisions. Thus, it opens the doors to many security threats and vulnerabilities. Traditional TCP/IP-based communication paradigm focuses on securing the communication channel instead of the contents that travel through the network. Nevertheless, for content-centered application, content security is more important than communication channel security. To this end, named data networking (NDN) is one of the future Internet architectures that puts the contents at the center of communication and offers embedded content security. In this paper, we first identify the cyberattacks and security challenges faced by the vehicular CPS (VCPS). Next, we propose the NDN-based cyber-resilient, the layered and modular architecture for VCPS. The architecture includes the NDN's forwarding daemon, threat aversion, detection, and resilience components. A detailed discussion about the functionality of each component is also presented. Furthermore, we discuss the future challenges faced by the integration of NDN with VCPS to realize NDN-based VCPS.
Conference Name: IEEE Access
Herwanto, Guntur Budi, Quirchmayr, Gerald, Tjoa, A Min.  2021.  A Named Entity Recognition Based Approach for Privacy Requirements Engineering. 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW). :406—411.
The presence of experts, such as a data protection officer (DPO) and a privacy engineer is essential in Privacy Requirements Engineering. This task is carried out in various forms including threat modeling and privacy impact assessment. The knowledge required for performing privacy threat modeling can be a serious challenge for a novice privacy engineer. We aim to bridge this gap by developing an automated approach via machine learning that is able to detect privacy-related entities in the user stories. The relevant entities include (1) the Data Subject, (2) the Processing, and (3) the Personal Data entities. We use a state-of-the-art Named Entity Recognition (NER) model along with contextual embedding techniques. We argue that an automated approach can assist agile teams in performing privacy requirements engineering techniques such as threat modeling, which requires a holistic understanding of how personally identifiable information is used in a system. In comparison to other domain-specific NER models, our approach achieves a reasonably good performance in terms of precision and recall.
Evangelatos, Pavlos, Iliou, Christos, Mavropoulos, Thanassis, Apostolou, Konstantinos, Tsikrika, Theodora, Vrochidis, Stefanos, Kompatsiaris, Ioannis.  2021.  Named Entity Recognition in Cyber Threat Intelligence Using Transformer-based Models. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :348—353.
The continuous increase in sophistication of threat actors over the years has made the use of actionable threat intelligence a critical part of the defence against them. Such Cyber Threat Intelligence is published daily on several online sources, including vulnerability databases, CERT feeds, and social media, as well as on forums and web pages from the Surface and the Dark Web. Named Entity Recognition (NER) techniques can be used to extract the aforementioned information in an actionable form from such sources. In this paper we investigate how the latest advances in the NER domain, and in particular transformer-based models, can facilitate this process. To this end, the dataset for NER in Threat Intelligence (DNRTI) containing more than 300 pieces of threat intelligence reports from open source threat intelligence websites is used. Our experimental results demonstrate that transformer-based techniques are very effective in extracting cybersecurity-related named entities, by considerably outperforming the previous state- of-the-art approaches tested with DNRTI.
He, Bingjun, Chen, Jianfeng.  2021.  Named Entity Recognition Method in Network Security Domain Based on BERT-BiLSTM-CRF. 2021 IEEE 21st International Conference on Communication Technology (ICCT). :508–512.
With the increase of the number of network threats, the knowledge graph is an effective method to quickly analyze the network threats from the mass of network security texts. Named entity recognition in network security domain is an important task to construct knowledge graph. Aiming at the problem that key Chinese entity information in network security related text is difficult to identify, a named entity recognition model in network security domain based on BERT-BiLSTM-CRF is proposed to identify key named entities in network security related text. This model adopts the BERT pre-training model to obtain the word vectors of the preceding and subsequent text information, and the obtained word vectors will be input to the subsequent BiLSTM module and CRF module for encoding and sorting. The test results show that this model has a good effect on the data set of network security domain. The recognition effect of this model is better than that of LSTM-CRF, BERT-LSTM-CRF, BERT-CRF and other models, and the F1=93.81%.
Song, Z., Kar, P..  2020.  Name-Signature Lookup System: A Security Enhancement to Named Data Networking. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1444–1448.
Named Data Networking (NDN) is a content-centric networking, where the publisher of the packet signs and encapsulates the data packet with a name-content-signature encryption to verify the authenticity and integrity of itself. This scheme can solve many of the security issues inherently compared to IP networking. NDN also support mobility since it hides the point-to-point connection details. However, an extreme attack takes place when an NDN consumer newly connects to a network. A Man-in-the-middle (MITM) malicious node can block the consumer and keep intercepting the interest packets sent out so as to fake the corresponding data packets signed with its own private key. Without knowledge and trust to the network, the NDN consumer can by no means perceive the attack and thus exposed to severe security and privacy hazard. In this paper, the Name-Signature Lookup System (NSLS) and corresponding Name-Signature Lookup Protocol (NSLP) is introduced to verify packets with their registered genuine publisher even in an untrusted network with the help of embedded keys inside Network Interface Controller (NIC), by which attacks like MITM is eliminated. A theoretical analysis of comparing NSLS with existing security model is provided. Digest algorithm SHA-256 and signature algorithm RSA are used in the NSLP model without specific preference.
Karatas, Nihan, Yoshikawa, Soshi, Okada, Michio.  2016.  NAMIDA: Sociable Driving Agents with Multiparty Conversation. Proceedings of the Fourth International Conference on Human Agent Interaction. :35–42.

We propose a multi party conversational social interface NAMIDA through a pilot study. The system consists of three robots that can converse with each other about environment throughout the road. Through this model, the directed utterances towards the driver diminishes by utilizing turn-taking process between the agents, and the mental workload of the driver can be reduced compared to the conventional one-to-one communication based approach that directly addresses the driver. We set up an experiment to compare the both approaches to explore their effects on the workload and attention behaviors of drivers. The results indicated that the multi-party conversational approach has a better effect on reducing certain workload factors. Also, the analysis of attention behaviors of drivers revealed that our method can better promote the drivers to focus on the road.

Masago, Hitoshi, Nodaka, Hiro, Kishimoto, Kazuma, Kawai, Alaric Yohei, Shoji, Shuichi, Mizuno, Jun.  2022.  Nano-Artifact Metrics Chip Mounting Technology for Edge AI Device Security. 2022 17th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT). :1—4.
In this study, the effect of surface treatment on the boding strength between Quad flat package (QFP) and quartz was investigated for establishing a QFP/quartz glass bonding technique. This bonding technique is necessary to prevent bond failure at the nano-artifact metrics (NAM) chip and adhesive interface against physical attacks such as counterfeiting and tampering of edge AI devices that use NAM chips. Therefore, we investigated the relationship between surface roughness and tensile strength by applying surface treatments such as vacuum ultraviolet (VUV) and Ar/O2 plasma. All QFP/quartz glass with surface treatments such as VUV and Ar/O2 plasma showed increased bond strength. Surface treatment and bonding technology for QFP and quartz glass were established to realize NAM chip mounting.
Wang, Xingbin, Zhao, Boyan, HOU, RUI, Awad, Amro, Tian, Zhihong, Meng, Dan.  2021.  NASGuard: A Novel Accelerator Architecture for Robust Neural Architecture Search (NAS) Networks. 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA). :776–789.
Due to the wide deployment of deep learning applications in safety-critical systems, robust and secure execution of deep learning workloads is imperative. Adversarial examples, where the inputs are carefully designed to mislead the machine learning model is among the most challenging attacks to detect and defeat. The most dominant approach for defending against adversarial examples is to systematically create a network architecture that is sufficiently robust. Neural Architecture Search (NAS) has been heavily used as the de facto approach to design robust neural network models, by using the accuracy of detecting adversarial examples as a key metric of the neural network's robustness. While NAS has been proven effective in improving the robustness (and accuracy in general), the NAS-generated network models run noticeably slower on typical DNN accelerators than the hand-crafted networks, mainly because DNN accelerators are not optimized for robust NAS-generated models. In particular, the inherent multi-branch nature of NAS-generated networks causes unacceptable performance and energy overheads.To bridge the gap between the robustness and performance efficiency of deep learning applications, we need to rethink the design of AI accelerators to enable efficient execution of robust (auto-generated) neural networks. In this paper, we propose a novel hardware architecture, NASGuard, which enables efficient inference of robust NAS networks. NASGuard leverages a heuristic multi-branch mapping model to improve the efficiency of the underlying computing resources. Moreover, NASGuard addresses the load imbalance problem between the computation and memory-access tasks from multi-branch parallel computing. Finally, we propose a topology-aware performance prediction model for data prefetching, to fully exploit the temporal and spatial localities of robust NAS-generated architectures. We have implemented NASGuard with Verilog RTL. The evaluation results show that NASGuard achieves an average speedup of 1.74× over the baseline DNN accelerator.
Elliott, Sean.  2019.  Nash Equilibrium of Multiple, Non-Uniform Bitcoin Block Withholding Attackers. 2019 2nd International Conference on Data Intelligence and Security (ICDIS). :144—151.
This research analyzes a seemingly malicious behavior known as a block withholding (BWH) attack between pools of cryptocurrency miners in Bitcoin-like systems featuring blockchain distributed databases. This work updates and builds on a seminal paper, The Miner's Dilemma, which studied a simplified scenario and showed that a BWH attack can be rational behavior that is profitable for the attacker. The new research presented here provides an in-depth profit analysis of a more complex and realistic BWH attack scenario, which includes mutual attacks between multiple, non-uniform Bitcoin mining pools. As a result of mathematical analysis and MATLAB modeling, this paper illustrates the Nash equilibrium conditions of a system of independent mining pools with varied mining rates and computes the equilibrium rates of mutual BWH attack. The analysis method quantifies the additional profit the largest pools extract from the system at the expense of the smaller pools. The results indicate that while the presence of BWH is a net negative for smaller pools, they must participate in BWH to maximize their remaining profits, and the results quantify the attack rates the smaller pools must maintain. Also, the smallest pools maximize profit by not attacking at all-that is, retaliation is not a rational move for them.
Ferguson, B., Tall, A., Olsen, D..  2014.  National Cyber Range Overview. Military Communications Conference (MILCOM), 2014 IEEE. :123-128.

The National Cyber Range (NCR) is an innovative Department of Defense (DoD) resource originally established by the Defense Advanced Research Projects Agency (DARPA) and now under the purview of the Test Resource Management Center (TRMC). It provides a unique environment for cyber security testing throughout the program development life cycle using unique methods to assess resiliency to advanced cyberspace security threats. This paper describes what a cyber security range is, how it might be employed, and the advantages a program manager (PM) can gain in applying the results of range events. Creating realism in a test environment isolated from the operational environment is a special challenge in cyberspace. Representing the scale and diversity of the complex DoD communications networks at a fidelity detailed enough to realistically portray current and anticipated attack strategies (e.g., Malware, distributed denial of service attacks, cross-site scripting) is complex. The NCR addresses this challenge by representing an Internet-like environment by employing a multitude of virtual machines and physical hardware augmented with traffic emulation, port/protocol/service vulnerability scanning, and data capture tools. Coupled with a structured test methodology, the PM can efficiently and effectively engage with the Range to gain cyberspace resiliency insights. The NCR capability, when applied, allows the DoD to incorporate cyber security early to avoid high cost integration at the end of the development life cycle. This paper provides an overview of the resources of the NCR which may be especially helpful for DoD PMs to find the best approach for testing the cyberspace resiliency of their systems under development.

Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. July/August

Bot detection - identifying a software program that's using a computer system -- is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Robert St. Amant, David Roberts.  2016.  Natural interaction for bot detection. IEEE Internet Computing. 20:69–73.
Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. 20(4):69–73.

Bot detection - identifying a software program that's using a computer system – is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Buck, Joshua W., Perugini, Saverio, Nguyen, Tam V..  2018.  Natural Language, Mixed-initiative Personal Assistant Agents. Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication. :82:1–82:8.
The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an activity, and is often exhibited in human-human conversations. In this paper, we study user support for mixed-initiative interaction with dialog-based systems through natural language using a bag-of-words model and k-nearest-neighbor classifier. We study this problem in the context of a toolkit we developed for automated, mixed-initiative dialog system construction, involving a dialog authoring notation and management engine based on lambda calculus, for specifying and implementing task-based, mixed-initiative dialogs. We use ordering at Subway through natural language, human-computer dialogs as a case study. Our results demonstrate that the dialogs authored with our toolkit support the end user's completion of a natural language, human-computer dialog in a mixed-initiative fashion. The use of natural language in the resulting mixed-initiative dialogs afford the user the ability to experience multiple self-directed paths through the dialog and makes the flexibility in communicating user utterances commensurate with that in dialog completion paths—an aspect missing from commercial assistants like Siri.
Kadebu, Prudence, Thada, Vikas, Chiurunge, Panashe.  2018.  Natural Language Processing and Deep Learning Towards Security Requirements Classification. 2018 3rd International Conference on Contemporary Computing and Informatics (IC3I). :135–140.
Security Requirements classification is an important area to the Software Engineering community in order to build software that is secure, robust and able to withstand attacks. This classification facilitates proper analysis of security requirements so that adequate security mechanisms are incorporated in the development process. Machine Learning techniques have been used in Security Requirements classification to aid in the process that lead to ensuring that correct security mechanisms are designed corresponding to the Security Requirements classifications made to eliminate the risk of security being incorporated in the late stages of development. However, these Machine Learning techniques have been found to have problems including, handcrafting of features, overfitting and failure to perform well with high dimensional data. In this paper we explore Natural Language Processing and Deep Learning to determine if this can be applied to Security Requirements classification.
Christopherjames, Jim Elliot, Saravanan, Mahima, Thiyam, Deepa Beeta, S, Prasath Alias Surendhar, Sahib, Mohammed Yashik Basheer, Ganapathi, Manju Varrshaa, Milton, Anisha.  2021.  Natural Language Processing based Human Assistive Health Conversational Agent for Multi-Users. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). :1414–1420.
Background: Most of the people are not medically qualified for studying or understanding the extremity of their diseases or symptoms. This is the place where natural language processing plays a vital role in healthcare. These chatbots collect patients' health data and depending on the data, these chatbot give more relevant data to patients regarding their body conditions and recommending further steps also. Purposes: In the medical field, AI powered healthcare chatbots are beneficial for assisting patients and guiding them in getting the most relevant assistance. Chatbots are more useful for online search that users or patients go through when patients want to know for their health symptoms. Methods: In this study, the health assistant system was developed using Dialogflow application programming interface (API) which is a Google's Natural language processing powered algorithm and the same is deployed on google assistant, telegram, slack, Facebook messenger, and website and mobile app. With this web application, a user can make health requests/queries via text message and might also get relevant health suggestions/recommendations through it. Results: This chatbot acts like an informative and conversational chatbot. This chatbot provides medical knowledge such as disease symptoms and treatments. Storing patients personal and medical information in a database for further analysis of the patients and patients get real time suggestions from doctors. Conclusion: In the healthcare sector AI-powered applications have seen a remarkable spike in recent days. This covid crisis changed the whole healthcare system upside down. So this NLP powered chatbot system reduced office waiting, saving money, time and energy. Patients might be getting medical knowledge and assisting ourselves within their own time and place.
Hirlekar, V. V., Kumar, A..  2020.  Natural Language Processing based Online Fake News Detection Challenges – A Detailed Review. 2020 5th International Conference on Communication and Electronics Systems (ICCES). :748–754.
Online social media plays an important role during real world events such as natural calamities, elections, social movements etc. Since the social media usage has increased, fake news has grown. The social media is often used by modifying true news or creating fake news to spread misinformation. The creation and distribution of fake news poses major threats in several respects from a national security point of view. Hence Fake news identification becomes an essential goal for enhancing the trustworthiness of the information shared on online social network. Over the period of time many researcher has used different methods, algorithms, tools and techniques to identify fake news content from online social networks. The aim of this paper is to review and examine these methodologies, different tools, browser extensions and analyze the degree of output in question. In addition, this paper discuss the general approach of fake news detection as well as taxonomy of feature extraction which plays an important role to achieve maximum accuracy with the help of different Machine Learning and Natural Language Processing algorithms.
Nambiar, Sindhya K, Leons, Antony, Jose, Soniya, Arunsree.  2019.  Natural Language Processing Based Part of Speech Tagger using Hidden Markov Model. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :782–785.
In various natural language processing applications, PART-OF-SPEECH (POS) tagging is performed as a preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes the methods to build a Part of speech tagger by using hidden markov model. Supervised learning approach is implemented in which, already tagged sentences in malayalam is used to build hidden markov model.
[Anonymous].  Submitted.  Natural Language Processing Characterization of Recurring Calls in Public Security Services.
Extracting knowledge from unstructured data silos, a legacy of old applications, is mandatory for improving the governance of today's cities and fostering the creation of smart cities. Texts in natural language often compose such data. Nevertheless, the inference of useful information from a linguistic-computational analysis of natural language data is an open challenge. In this paper, we propose a clustering method to analyze textual data employing the unsupervised machine learning algorithms k-means and hierarchical clustering. We assess different vector representation methods for text, similarity metrics, and the number of clusters that best matches the data. We evaluate the methods using a real database of a public record service of security occurrences. The results show that the k-means algorithm using Euclidean distance extracts non-trivial knowledge, reaching up to 93% accuracy in a set of test samples while identifying the 12 most prevalent occurrence patterns.
Bhagat, V., J, B. R..  2020.  Natural Language Processing on Diverse Data Layers Through Microservice Architecture. 2020 IEEE International Conference for Innovation in Technology (INOCON). :1–6.
With the rapid growth in Natural Language Processing (NLP), all types of industries find a need for analyzing a massive amount of data. Sentiment analysis is becoming a more exciting area for the businessmen and researchers in Text mining & NLP. This process includes the calculation of various sentiments with the help of text mining. Supplementary to this, the world is connected through Information Technology and, businesses are moving toward the next step of the development to make their system more intelligent. Microservices have fulfilled the need for development platforms which help the developers to use various development tools (Languages and applications) efficiently. With the consideration of data analysis for business growth, data security becomes a major concern in front of developers. This paper gives a solution to keep the data secured by providing required access to data scientists without disturbing the base system software. This paper has discussed data storage and exchange policies of microservices through common JavaScript Object Notation (JSON) response which performs the sentiment analysis of customer's data fetched from various microservices through secured APIs.
Sanyal, Hrithik, Shukla, Sagar, Agrawal, Rajneesh.  2021.  Natural Language Processing Technique for Generation of SQL Queries Dynamically. 2021 6th International Conference for Convergence in Technology (I2CT). :1—6.
Natural Language Processing is being used in every field of human to machine interaction. Database queries although have a confined set of instructions, but still found to be complex and dedicated human resources are required to write, test, optimize and execute structured query language statements. This makes it difficult, time-consuming and many a time inaccurate too. Such difficulties can be overcome if the queries are formed dynamically with standard procedures. In this work, parsing, lexical analysis, synonym detection and formation processes of the natural language processing are being proposed to be used for dynamically generating SQL queries and optimization of them for fast processing with high accuracy. NLP parsing of the user inputted text for retrieving, creation and insertion of data are being proposed to be created dynamically from English text inputs. This will help users of the system to generate reports from the data as per the requirement without the complexities of SQL. The proposed system will not only generate queries dynamically but will also provide high accuracy and performance.