iosr-JECE   Volume-15 ~ Issue-3 ~ May-June 2020

Abstract: The demand for sophisticated tools for intrusion detection,vulnerability analysis, forensic investigations, and possible responses has increased because of the increasing volume of attacks on the internet. Authorization of reengineering to show cyber crime and homeland security is given by present tools and technologies of hacker. To assure the details base by collecting intelligence, topology of network, penetration testing and inner/outer accountability test it is essential to create network scanners. Cyberspace (IP),SS7, radiotelegraphy, and merged system are the variety of networks on which scanners can be functioned. To assist use by a wide range of end users and policy; such elasticity permits to keep up with present technician mechanics expansile and elevate scanners should be used.

Key Word: Linux, Network security, Vulnerabilities, Phyton, Scan, Security tool, Data Security, Viruses, Attacks.

[1]. Penetration Testing- https://www.tcdi.com/services/cybersecurity/penetration-testing-pen-test/
[2]. Information Gathering- https://www.w3schools.in/ethical-hacking/information-gathering-techniques/.

Abstract: We implement a general Convolutional Neural Network (CNN) to design a real-time model and validates our model by creating a real-time vision that accomplishes the task of face detection, gender, and emotion classification simultaneously. We got accuracies of 95% in the IMDB-WIKI age and gender dataset and 66% in the FER emotion recognition dataset. We have used a real-time guided back-propagation technique to visualize the weighed of real-time CNN that uncovered the dynamic weight change and evaluate the learning feature. We think in the modern CNN architecture regularization and visualization of previously hidden layer features are necessary to reduce the gap between slow performances and real-time architectur.

Keywords: Back Propagation, Convolutional Neural Network (CNN), Computer Vision (CV), Emotion Detection, Face Detection, Gender Classification

[1]. Arriaga, Octavio et al. "Real-time Convolutional Neural Networks for Emotion and Gender Classification." CoRR abs/1710.07557 (2017): n. pag.
[2]. Goodfellow, Ian J., et al. "Challenges in Representation Learning: A report on three machine learning contests." Neural networks: the official journal of the International Neural Network Society 64 (2013): 59-63.
[3]. Chollet, François. "Xception: Deep Learning with Depthwise Separable Convolutions." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): 1800-1807
[4]. Levi, Gil, and Tal Hassner. "Age and gender classification using convolutional neural networks." 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015): 34-42.
[5]. Rothe, Rasmus et al. "DEX: Deep Expectation of Apparent Age from a Single Image." 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) (2015): 252-257

Abstract: Fingerprint contains ridge and valley which together form distinctive patterns. A fingerprint Biometric trait is one of the important traits working with good results in the gender classification. The plan agreements with the problem of gender classification using fingerprint images. The project proposed a technique for classifying the gender based on feature extraction. The related feature to be removed and differentiate the gender is on and Minutiae extraction and ROI. The extracted feature is used to train neural network based model on the extracted data.....

Keywords: Fingerprint, ROI; MM; OCM; RTVTR; MFANNs; SVRRBF;

[1]. IEEE COMPUTER SOCIETY 1540-7993/03 © 2003 IEEE "Biometric Recognition: Security and Privacy Concerns", IEEE SECURITY AND PRIVACY SALIL PRABHAKAR Digital Persona, SHARATH PANKANTI IBM T.J. Watson Research Center, ANIL K. JAIN Michigan State University.
[2]. International Journal of Engineering Trends and Technology- Volume 4 Issue 2- 2013 "Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis" Rijo Jackson Tom and T. Arulkumaran.
[3]. A. Badawi, M. Mahfouz, R. Tadross, and R. Jantz "Fingerprint - based gender classification" et al, (June, 2006).
[4]. Manish Verma, Manish Verma and Suneeta Agarwal. "Fingerprint Based Male - Female Classification." et al, (2008).
[5]. JenFeng Wang, et al, "Gender Determination using Fingertip Features" et al, (2008).