WEB APPLICATION RECONNAISANCE TOOL

Devika Passi

Abstract


In general, system security has turned into a significant piece of the computerized viewpoint. In fact, by directing a security evaluation a system can be assessed by checking for the presence of weakness. The data security evaluation is required these days with the increasing number of cyber-attacks. Pen tester in leading assessments need to learn or get whatever amount of information as could be anticipated with the objective which for this situation is as ports, directory and subdomain. In this way, we want a device that supports viable and proficient Information Gathering to aid analysis and reporting. There are as yet numerous Information Gathering tools that a pen tester can utilize yet utilizing that large number of devices independently can be a tedious cycle. In this study, we have depicted the tool which makes it simpler for Cyber Security scientists/examiners by performing 3 tasks in a single tool.

Keywords


Directory, Ports, Subdomain, Vulnerability

References


P. Whig, R. R. Nadikattu, and A. Velu, “COVID-19 pandemic analysis using application of AI,” Healthcare Monitoring and Data Analysis Using IoT: Technologies and Applications, p. 1, 2022.

M. Anand, A. Velu, and P. Whig, “Prediction of Loan Behaviour with Machine Learning Models for Secure Banking,” Journal of Computer Science and Engineering (JCSE), vol. 3, no. 1, pp. 1–13, 2022.

Y. Alkali, I. Routray, and P. Whig, “Study of various methods for reliable, efficient and Secured IoT using Artificial Intelligence,” Available at SSRN 4020364, 2022.

G. Chopra and P. WHIG, “A clustering approach based on support vectors,” International Journal of Machine Learning for Sustainable Development, vol. 4, no. 1, pp. 21–30, 2022.

G. Chopra and P. Whig, “Smart Agriculture System Using AI,” International Journal of Sustainable Development in Computing Science, vol. 1, no. 1, 2022.

M. Madhu and P. WHIG, “A survey of machine learning and its applications,” International Journal of Machine Learning for Sustainable Development, vol. 4, no. 1, pp. 11–20, 2022.

G. Chopra and P. Whig, “Energy Efficient Scheduling for Internet of Vehicles,” International Journal of Sustainable Development in Computing Science, vol. 4, no. 1, 2022.

G. Chopra and P. WHIG, “Using machine learning algorithms classified depressed patients and normal people,” International Journal of Machine Learning for Sustainable Development, vol. 4, no. 1, pp. 31–40, 2022.

N. Jiwani, K. Gupta and N. Afreen, "A Convolutional Neural Network Approach for Diabetic Retinopathy Classification," 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022, pp. 357-361.

P. WHIG, “More on Convolution Neural Network CNN,” International Journal of Sustainable Development in Computing Science, vol. 1, no. 1, 2022.

P. Whig, “IoT Based Novel Smart Blind Guidance System,” Journal of Computer Science and Engineering (JCSE), vol. 2, no. 2, pp. 80–88, 2021.

G. Chopra and P. Whig, “Analysis of Tomato Leaf Disease Identification Techniques,” Journal of Computer Science and Engineering (JCSE), vol. 2, no. 2, pp. 98–103, 2021.

A. Velu and P. Whig, “Studying the Impact of the COVID Vaccination on the World Using Data Analytics”.

P. Asopa, P. Purohit, R. R. Nadikattu, and P. Whig, “Reducing carbon footprint for sustainable development of smart cities using IoT,” in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021, pp. 361–367.

P. Whig, “Artificial intelligence and machine learning in business,” Engineering Reports, vol. 2, no. 2, pp. 8–13, 2019.

Dr. P. W. and S. N. Ahmad, “NEURAL NETWORK AND FUZZY SYSTEM,” EJRD - International Multidisciplinary Journal, vol. 2, no. 6, p. 8, 2017.

P. Ajay Rupani, “The development of big data science to the world,” Engineering Reports, vol. 2, no. 2, pp. 1–7, 2019.

A. Rupani and D. Kumar, “Temperature Effect On Behaviour of Photo Catalytic Sensor (PCS) Used For Water Quality Monitoring,” 2020.

K. K. and P. Whig2*, “Macroeconomic Implications of the Monetary Policy Committee Recommendations: An IS-LM Framework,” ACTA SCIENTIFIC AGRICULTURE (ISSN: 2581-365X), vol. 4, no. 2, 2020.

P. Whig, “Novel PCS Output Calibration Technique,” Available at SSRN 3621365, 2020.

R. R. Nadikattu, S. M. Mohammad, and P. Whig, “Novel economical social distancing smart device for covid-19,” International Journal of Electrical Engineering and Technology (IJEET), 2020.

K. Gupta, N. Jiwani and N. Afreen, "Blood Pressure Detection Using CNN-LSTM Model," 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022, pp. 262-366.

N. Jiwani, K. Gupta and N. Afreen, "Automated Seizure Detection using Theta Band," 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), 2022, pp. 1-4.

Whig, P., Kouser, S., Velu, A., & Nadikattu, R. R. (2022). Fog-IoT-Assisted-Based Smart Agriculture Application. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 74–93). IGI Global.

Whig, P., Nadikattu, R. R., & Velu, A. (2022). COVID-19 pandemic analysis using application of AI. Healthcare Monitoring and Data Analysis Using IoT: Technologies and Applications, 1.

Whig, P., Velu, A., & Bhatia, A. B. (2022). Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 123–142). IGI Global.

Whig, P., Velu, A., & Naddikatu, R. R. (2022). The Economic Impact of AI-Enabled Blockchain in 6G-Based Industry. In AI and Blockchain Technology in 6G Wireless Network (pp. 205–224). Springer, Singapore.

Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain Platform to Resolve Security Issues in IoT and Smart Networks. In AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems (pp. 46–65). IGI Global.

Whig, P., Velu, A., & Ready, R. (2022). Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 94–122). IGI Global.

Whig, P., Velu, A., & Sharma, P. (2022). Demystifying Federated Learning for Blockchain: A Case Study. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 143–165). IGI Global.

K. Gupta, N. Jiwani, N. Afreen and D. D, "Liver Disease Prediction using Machine learning Classification Techniques," 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022, pp. 221-226.


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