SPATIOTEMPORAL CHANGE-AWARE MACHINE LEARNING MODELS FOR IMPROVED CYBERCRIME TYPE PREDICTION
N.Neemukavi
, Selvemeena.R
Keywords : Cybercrime Prediction, Spatiotemporal Data Analysis, Geographic Information Systems (GIS), Machine Learning Algorithms, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors (KNN), Crime Type Classification, Predictive Analytics, Law Enforcement Intelligence, Algorithmic Performance Evaluation.
Abstract: The project named Cyber Criminal Activity Analysis is all about analyzing cybercrime records using geospatial as well as temporal, which are latitude, longitude, date and time respectively, so that we can analyze what types of crime has been taken place at certain location during specific time period. This study hopes to use machine learning algorithms to develop predictive models that can be used for the proactive prevention of cybercrime. It consists of a dataset of records about cybercrime incidents, with spatial (latitude, longitude) and temporal (date, time) information. A variety of machine learning models were trained to classify types of cybercrimes using decision tree, random forest and K-nearest neighbor (KNN). When tested for performance on the accuracy front, all three models, which are Decision Tree, Random Forest and KNNachieved the magic number of 99%.Models evaluation from previous findings on cybercrimes, also highlights the potential of these models as they achieve a high accuracy when we predict types of crimes according to space and time features. Such analysis serves as an important tool for law enforcement agencies to target crimes and allocate resources based on data. A potential area of improvement for future works could be the expansion of dataset as well as adding on extra features can lead to a further architectural transformation.
"SPATIOTEMPORAL CHANGE-AWARE MACHINE LEARNING MODELS FOR IMPROVED CYBERCRIME TYPE PREDICTION", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 11, page no.110 - 117, November-2024, Available :https://ijsdr.org/papers/IJSDR2411014.pdf
Volume 9
Issue 11,
November-2024
Pages : 110 - 117
Paper Reg. ID: IJSDR_212675
Published Paper Id: IJSDR2411014
Downloads: 000346998
Research Area: Engineering
Country: Tiruvallur, Tamil nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
