Deep Learning Technique Based Intrusion Detection in Cyber-Security Networks

Deep learning is a subfield of machine learning (ML) that focuses on the development of artificial intelligence. It is also often referred to by its acronym, DL (AI). This technique lays an emphasis on the use of big capacity, scalable models that are able to construct distributed representations depending on the input data set. This proposed illustrates the generalizability of these methods and the usage of them in a broad range of cyber security investigations that are peculiar to their environment. The neural network models have been continuously refined and extended during the whole of this research in order to achieve greater adaptability. The following is a list of the important contributions that this proposed makes, in order from most significant to least significant: Work is currently being done to create a comprehensive database for the identification of domain names that have been generated by a domain generation algorithm (DGA), as well as a one-of-a-kind architecture that will increase the overall effectiveness of DGA domain name detection. Both of these will help increase overall efficiency. The creation of a hybrid intrusion detection warning system that is founded on a deep neural network (DNN) and that has the capability to monitor network and host-level activities inside an Ethernet local area network (LAN) (LAN). The examination of information gathered from social media platforms, electronic mail (email), and uniform resource locators in order to design a unified, DL-based framework for the detection of spam and phishing (URL). The creation of a technique based on DL for the study of secure shell (SSH) traffic, the categorization of application network traffic, the classification of malicious traffic, and the detection of harmful traffic is being worked on. The name of the new framework that has been suggested, which is called ScaleMalNet, reflects how hybrid and scalable it is. In the first stage, the executables file is classified as malware or genuine by using static and dynamic analysis. In the second stage, the malicious executables _le are grouped into corresponding malware families. This is a two-step process. For the aim of conducting investigations into Android ransomware and malware, an analogous hybrid DL framework is now in the process of being developed. This framework is better in its capacity to detect dangerous software and ransomware on Android when compared to the typical ML-based techniques that are presently in use. These approaches are already in widespread usage. The development of a framework for DNS-based botnet detection and DL-based network intrusion detection is now being worked on in the context of the Internet of things (IoT) and smart cities 

Improved Domain Generation Algorithm To Detect Cyber-Attack With Deep Learning Techniques

Deep learning is a subfield of machine learning (ML) that focuses on the development of artificial intelligence. It is also often referred to by its acronym, DL (AI). This technique lays an emphasis on the use of big capacity, scalable models that are able to construct distributed representations depending on the input data set. This proposed illustrates the generalizability of these methods and the usage of them in a broad range of cyber security investigations that are peculiar to their environment. The neural network models have been continuously refined and extended during the whole of this research in order to achieve greater adaptability. The following is a list of the important contributions that this proposed makes, in order from most significant to least significant: Work is currently being done to create a comprehensive database for the identification of domain names that have been generated by a domain generation algorithm (DGA), as well as a one-of-a-kind architecture that will increase the overall effectiveness of DGA domain name detection. Both of these will help increase overall efficiency. The creation of a hybrid intrusion detection warning system that is founded on a deep neural network (DNN) and that has the capability to monitor network and host-level activities inside an Ethernet local area network (LAN) (LAN). The examination of information gathered from social media platforms, electronic mail (email), and uniform resource locators in order to design a unified, DL-based framework for the detection of spam and phishing (URL). The creation of a technique based on DL for the study of secure shell (SSH) traffic, the categorization of application network traffic, the classification of malicious traffic, and the detection of harmful traffic is being worked on. The name of the new framework that has been suggested, which is called ScaleMalNet, reflects how hybrid and scalable it is. In the first stage, the executables file is classified as malware or genuine by using static and dynamic analysis. In the second stage, the malicious executables _le are grouped into corresponding malware families. This is a two-step process. For the aim of conducting investigations into Android ransomware and malware, an analogous hybrid DL framework is now in the process of being developed. This framework is better in its capacity to detect dangerous software and ransomware on Android when compared to the typical ML-based techniques that are presently in use. These approaches are already in widespread usage. The development of a framework for DNS-based botnet detection and DL-based network intrusion detection is now being worked on in the context of the Internet of things (IoT) and smart cities

Robotics provides new innovations to the industrial revolution and the more recent information revolution. Inverse kinematics computation is one of the main problems in robotics research and its complexity increases by rising the degrees of freedom the manipulator. The inverse kinematics problem for a robotic manipulator is obtained for the required manipulator joint values for a given desired end point position and orientation. The Artificial Neural Network is an algorithm which works similarly as the biological neuron. In this paper the Neural Network ideas such as feed forward and Radial Basis Functions algorithms are proposed to solve the inverse kinematics problem of five degree of freedom robot end effector.

Inverse kinematics solution of a five joint robot using feed forward and Radial Basis Function Neural Network

An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action. Deep-faking can also be used to represent images and sounds. We provide insights into our reviews in this document. We're showing our dataset to start. At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally. It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited). We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this. The edge groups are isolated from each video successively (without outline skips). The entire pipeline is ready to be finished when the approval stage is ten years old. Convolutional Neural Network (CNN) was the best and most reliable of the classification systems. Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena. 'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face. In this study, we have implemented a sophisticated, knowledgeable method to recognize false images. Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure.

A robust facemask forgery detection system in video

The Internet-based security Soft-i-Robot is modeled using Soft computing paradigms for problem solving and decision-making in complex and ill-structured situations. Soft-i-Robot monitors the workspace with multimedia devices and sensor using an Internet application program. The model has sensory subsystems such as Intruder detection which, detects intruder, captures image and sends to server, and an Obstacle Avoidance Unit to detect the objects in the path of the mobile robot. These multiple features with hybrid Soft computing techniques depart the developed Soft-i-Robot from the existing developments, proving that the streaming technology-based approach greatly improves the sensibility of robot tele-operation. The relatively powerful online robots available today provoke the simple question, in terms of two competing goals: recognition accuracy and computing time. Improved recognition accuracy and reduced computing time for face recognition of the intruder is obtained using Morphological Shared Weight Neural Network. To obtain a collision-free optimized path, Soft-i-Robot uses derivative free Genetic Algorithm. With rapid expansion of Robotics and Soft computing paradigms, robotic technology touches upon self-understanding of humans, socio-economic, legal and ethical issues leading to improved performance rate and information processing capabilities.

http://ieeexplore.ieee.org/iel7/6671842/6675899/06675985.pdf

Neural networks and genetic algorithm based intelligent robot for face recognition and obstacle avoidance

In recent years, the growth of high-dimensional data applications has posed a significant challenge to data analytics, particularly high-dimensional data classification. To solve this problem, efficient feature selection and extraction approaches are paramount to the success of classification analyses. The dimensionality problem of high-dimensional data, which comprises a large number of variables and complicated data matrices, is a big barrier in the health care research domains. We propose an Ensemble Classifier with Hybrid Feature Transformation for handling extremely complex dimensional data. This is proven to be efficient for selecting and extracting features from a very large dataset in a classification task. The proposed approach has been applied to the classification of an unbalanced dataset of gene expression RNA-Seq data in five types of cancer. In the hybrid feature transformation approach, the original datasets are transformed in two stages. The first phase selects the highest score features from the original dataset, which are then sent to the second phase of feature transformation. As part of the second phase, features are transformed into a new set of features. Finally, a dataset with six features is applied to the ensemble classifier which achieves 99.96% accuracy by averaging the probability systems of the groups of classifiers. The findings clearly show that the proposed approach outperforms the existing methods for high-dimensional data classification.

Ensemble Classifier with Hybrid Feature Transformation for High Dimensional Data in Healthcare

Notice of Violation of IEEE Publication Principles "Neural Networks and Genetic Algorithm Based Intelligent Robot for Face Recognition and Obstacle Avoidance" by S. Arun, G. Harish, K. Salomon, R. Saravanan, K. Kalpana, J.Jaya in the Proceedings of the International Conference on Current Trends in Engineering and Technology (ICCTET), July 2013, pp. 356-361 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "Soft-I-Robot Using Neural Networks and Genetic Algorithm" by Santhosh S. Indian Journal of Innovations and Development, Vol 1, No 6, June 2012 The Internet-based security Soft-i-Robot is modeled using Soft computing paradigms for problem solving and decision-making in complex and ill-structured situations. Soft-i-Robot monitors the workspace with multimedia devices and sensor using an Internet application program. The model has sensory subsystems such as Intruder detection which, detects intruder, captures image and sends to server, and an Obstacle Avoidance Unit to detect the objects in the path of the mobile robot. These multiple features with hybrid Soft computing techniques depart the developed Soft-i-Robot from the existing developments, proving that the streaming technology-based approach greatly improves the sensibility of robot tele-operation. The relatively powerful online robots available today provoke the simple question, in terms of two competing goals: recognition accuracy and computing time. Improved recognition accuracy and reduced computing time for face recognition of the intruder is obtained using Morphological Shared Weight Neural Network. To obtain a collision-free optimized path, Soft-i-Robot uses derivative free Genetic Algorithm. With rapid expansion of Robotics and Soft computing paradigms, robotic technology touches upon self-understanding of humans, socio-economic, legal and ethical issues leading to improved performance rate and information processing capabilities.

Notice of Violation of IEEE Publication Principles Neural networks and genetic algorithm based intelligent robot for face recognition and obstacle avoidance

Face Recognition and Detection encompasses an ocean of study and development involving picture analysis and algorithm-based comprehension, sometimes known as computer vision. Attendance is a right that no one can reject, and to support this right, many efforts and studies are being conducted around the world. A Deep Convolutional Neural Network (CNN) using the OpenCV model has been suggested for marking Attendance in this work. Convolutional Neural Network is employed to gain the unique features of the faces based on the distance. A wide variety of parameters influence the training of a Convolutional Neural Network (CNN) based classifier. These aspects include assembling an appropriate dataset, choosing a suitable Convolutional Neural Network (CNN), processing the dataset, and choosing training parameters to get the required classification results. The current publication compiles state-of-the-art research that used dataset preparation and artificial augmentation before training. Accuracy rates are achieved using the proposed model.

Marking Attendance using Modern Face Recognition (FR): Deep Learning using the OpenCV Method

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Neural networks and genetic algorithm based intelligent robot for face recognition and obstacle avoidance

Ensemble Classifier with Hybrid Feature Transformation for High Dimensional Data in Healthcare

Notice of Violation of IEEE Publication Principles Neural networks and genetic algorithm based intelligent robot for face recognition and obstacle avoidance

Marking Attendance using Modern Face Recognition (FR): Deep Learning using the OpenCV Method