Call for Papers:Vol.11 Issue.2

Submission
Last date		 28-Apr-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-2. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Prediction of cyber attack using data science technique .
PaperId: :  17462
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 3 2022
DUI:    16.0415/IJARIIE-17462
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Harshini Priya AJeppiaar Engineering College
Akshayashri.GJeppiaar Engineering College
Dhivya.MJeppiaar Engineering College
Nithisha.JJeppiaar Engineering College

Abstract

Information Technology
Datasets, Prediction, Protocol type, GUI ,Logistic regression, Decision tree, Random forest, Support vector machine.
Cyber-attack, via cyberspace, targeting an enterprise's use of cyberspace for the purpose of disrupting, disabling, destroying, or maliciously controlling a computing environment/infrastructure; or destroying the integrity of the data or stealing controlled information. The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users. New paradigms add more concerns with big data collected through device sensors divulging large amounts of information, which can be used for targeted attacks. Though a plethora of extant approaches, models and algorithms have provided the basis for cyber-attack predictions, there is the need to consider new models and algorithms, which are based on data representations other than task-specific techniques. However, its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify type of network attack. In this paper, we model cyber-attack prediction as a classification problem, Networking sectors have to predict the type of Network attack from given dataset using machine learning techniques. The analysis of dataset by supervised machine learning technique (SMLT) to capture several information’s like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments etc. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate in predicting the type cyber-Attacks. We classify four types of attacks are DOS Socio-technical attack is an organized approach which is defined by the interaction among people through maltreatment of technology with some of the malicious intent to attack the social structure based on trust and faith. Awful advertisement over internet and mobile phones may defame a person, organization, group and brand value in society which may be proved to be fatal. People are always very sensitive towards their religion therefore mass spread of manipulated information against their religious belief may create pandemonium in the society and can be one of the reasons for social riots, political misbalance etc. Cyber-attack on water, electricity, finance, healthcare, food and transportation system are may create chaos in society within few minutes and may prove even more destructive than that of a bomb as it does not attack physically but it attacks on the faith and trust which is the basic pillar of our social structure. Attack, R2L Attack, U2R Attack, Probe attack. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity and Entropy.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. "Prediction of cyber attack using data science technique ." International Journal Of Advance Research And Innovative Ideas In Education Volume 8 Issue 3 2022 Page 4692-4702
MLA Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. "Prediction of cyber attack using data science technique .." International Journal Of Advance Research And Innovative Ideas In Education 8.3(2022) : 4692-4702.
APA Harshini Priya A, Akshayashri.G, Dhivya.M, & Nithisha.J. (2022). Prediction of cyber attack using data science technique .. International Journal Of Advance Research And Innovative Ideas In Education, 8(3), 4692-4702.
Chicago Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. "Prediction of cyber attack using data science technique .." International Journal Of Advance Research And Innovative Ideas In Education 8, no. 3 (2022) : 4692-4702.
Oxford Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. 'Prediction of cyber attack using data science technique .', International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, 2022, p. 4692-4702. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Prediction_of_cyber_attack_using_data_science_technique___ijariie17462.pdf (Accessed : ).
Harvard Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. (2022) 'Prediction of cyber attack using data science technique .', International Journal Of Advance Research And Innovative Ideas In Education, 8(3), pp. 4692-4702IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Prediction_of_cyber_attack_using_data_science_technique___ijariie17462.pdf (Accessed : )
IEEE Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J, "Prediction of cyber attack using data science technique .," International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, pp. 4692-4702, May-Jun 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/Prediction_of_cyber_attack_using_data_science_technique___ijariie17462.pdf [Accessed : ].
Turabian Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. "Prediction of cyber attack using data science technique .." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 8 number 3 ().
Vancouver Harshini Priya A, Akshayashri.G, Dhivya.M, and Nithisha.J. Prediction of cyber attack using data science technique .. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2022 [Cited : ]; 8(3) : 4692-4702. Available from: https://ijariie.com/AdminUploadPdf/Prediction_of_cyber_attack_using_data_science_technique___ijariie17462.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
DEEP ADAPTIVE FEATURE FUSION FOR ORIGIN DESTINATION PASSENGER FLOW FORECASTING IN MASS EVENTSInformation TechnologyBHUKYA RAJAKUMAR Download
REAL TIME EMERGENCY VEHICLE DETECTION USING MACHINE LEARNINGComputer Science and EngineeringAravinthkumar A Download
COLLEGIUMBOT: AN AI-DRIVEN CHATBOT FOR ENHANCING STUDENT ENGAGEMENT AND CAMPUS LIFE THROUGH VOICE-BASED INTERACTIONInformation TechnologySIVASANKAR CHITTOOR Download
AUTOMATED VIDEO TEXT EXTRACTION AND SUMMARIZATION SYSTEM USING LSTM NETWORKSInformation TechnologyT SUNDARARAJULU Download
BLOOD GROUP DETECTION USING FINGERPRINTInformation TechnologyN ANITHA Download
SOLAR RADIATION PREDICTION USING MACHINE LEARNINGInformation TechnologyM RAM KUMAR Download
DEFENDING AGAINST POISONING ATTACKS IN FEDERATED LEARNING WITH BLOCKCHAININFORMATION TECHNOLOGYT.SUNDARARAJULU. Download
DEEP CONVOLUTIONAL NEURAL NETWORK FOR ROBUST DETECTION OF OBJECT-BASED FORGERIES IN ADVANCED VIDEOINFORMATION TECHNOLOGYD.VISWASAHITYA Download
SMART CAR PRICE PREDICTOR USING MACHINE LEARNINGINFORMATION TECHNOLOGYS.JASMIN Download
SUPERVISED LEARNING MODEL INSIGHTS AND EVALUATIONInformation TechnologyB. RAJA KUMAR Download
Enhancing Forensic Data Protection: A Robust Storage Framework with Verified Access and Intelligent Key-Based EncryptionComputer Science EngineeringN ANITHA Download
PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTIONINFORMATION TECHNOLOGYD Viswasahithya Download
Real-time driver's drowsiness detection using MlInformation Technology and MLKiran parasram shelke Download
The Role of Information Technology in Improving the Quality of Banking Services at the Al-Jumhouria Bank – Madinah Branch, SabrathaDigital BankingEssam Zaid Shoaib Al-Dabbashi Download
Enhancing the Conceptual Learning on Natural Resources through Battle of the Brains StrategyScienceDaicy C. Deligencia Download
12