Evolutionary approach for intrusion detection

Kumar, Gunupudi Rajesh; Mangathayaru, Nimmala; Narsimha, G.; Reddy, Gali Suresh

doi:10.1109/icemis.2017.8273116

Cited by 15 publications

(4 citation statements)

References 21 publications

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“…It can be split into two parts: selecting features and getting information about them. Gunupudi et.al [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54] discusses various dimensionality reductions and data transformation techniques used for various learning methods in their proposed research work. Rybus et al in thier proposed method creates a path for the arm that avoids collisions and leads to the gripper being in the right place and facing the right way and the spacecraft being in the right attitude.…”

Section: Related Workmentioning

confidence: 99%

A Multi Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in Known Static Environments

Neeraja

Narsimha

2022

SCPE

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The most important field of robotics study is path planning. Path planning problem in general is an NP-complete problem. Though several attempts have been made using A*, PRM, RRT, and RRT* these algorithms explore too many nodes in the state space, not completely captured kinematic constraints, and are not optimal in real-time. In this paper, a Multi-Objective Hybrid Collision- free Optimal Path Finder (MOHC-OPF) is proposed which is an attempt to obtain a near-optimal solution by exploring fewer nodes compare to the above existing methods while considering kinematic constraints aiming to generate optimal drivable paths. The empirical study revealed that the proposed algorithm is capable of detecting static obstacles and finding a collision-free nearest-optimal, smooth and safe path to the destination in a static known environment. Multiple criteria, including path length, collision-free, execution time, and smooth path, are used to determine an optimal path.. The proposed algorithm shows efficiency in finding the shortest path length and execution time decreased in 90% of the experiments.

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Section: Related Workmentioning

confidence: 99%

A Multi Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in Known Static Environments

Neeraja

Narsimha

2022

SCPE

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“…To see the future perspective of the proposed work, it can be figured out traditionally, the current deep learning applications have considered existing distance functions in the research literature for similarity computations but did not try to fit in new functions for similarity computations [46][47][48][49][50]. There is a possibility to devise threshold and similarity functions to suit deep learning applications [51][52][53][54][55]. For instance, recent research contributions propose various similarity and threshold functions for temporal pattern mining which can be redesigned to suit deep learning applications [56][57][58][59][60].…”

Section: Proposed Neural Architecturementioning

confidence: 99%

An Attention Based Neural Architecture for Arrhythmia Detection and Classification from ECG Signals

Mangathayaru¹,

Rani²,

Janaki³

et al. 2021

Computers, Materials &Amp; Continua

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Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, costefficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet Transform (DT-CWT) method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features. Next, A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters. To ensure that the model's generalizability, a set of five traintest variants are implied. The proposed model attains the highest accuracy of 98.5% for classifying 8 variants of arrhythmia on the MIT-BIH dataset. To test the resilience of the model, the unseen (test) samples are increased by 5x and the deviation in accuracy score and MSE was 0.12% and 0.1% respectively. Further, to assess the diagnostic model performance, AUC-ROC curves are plotted. At every test level, the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application. As a note, this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.

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“…The purpose of access control and Intrusion detection system is to identify the genuineness. Some researchers have worked in the area of intrusion detection for finding the correctness of the inquirer [29] [30] [31] [32] [33]. These works were very helpful to get an insight of various methods of identifying the genuineness of the requester.…”

Section: Machine Learningmentioning

confidence: 99%

NdRAdAC: Need based Access Control Framework for an Emergency Response System

Srivastava¹

2021

TURCOMAT

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Access control is easy to implement in a static system with resource-role mapping and known policies. It becomes challenging if the system is dynamic and volatile, which means there are unpredictability in the workflow. Existing role based and attribute- based access control systems are very efficient in static and predictable situations. But they are not effective in a dynamic situation. Researchers over the last two decades have tried to propose various probabilistic based, machine learning based and decision theory-based access control to prove adaptability in their access control methods. But there are existing gaps in operational needs and proposed adaptability methods. Under regular scenario access control system may work based on the policies or decided roles. Only if there is a genuine need, then access control should switch to adaptable solutions. Also, a true adaptable system should not allow human intervention, the system should be able to understand the genuineness of the requester and take decisions whether access should be granted or not. In this paper with the help of a disaster management case study, a need-based access control framework – NdRAdAC is proposed. It evaluates the genuineness of the requester and acts appropriately. An ontology-based access control for an emergency response system is developed, which can help the disaster management system to coordinate with different hospitals and help in transferring patient data from one hospital to another if needed. It ensures that data requester is authenticated with the help of access control module. The framework is tested for three main parameters: Adaptability, Consistency and Computational Efficiency. It was found that framework was accurately adaptable, consistent with all the different types of cases and computationally efficient.

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Evolutionary approach for intrusion detection

Cited by 15 publications

References 21 publications

A Multi Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in Known Static Environments

A Multi Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in Known Static Environments

An Attention Based Neural Architecture for Arrhythmia Detection and Classification from ECG Signals

NdRAdAC: Need based Access Control Framework for an Emergency Response System

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