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Title MACHINE LEARNING BASED KEY LOGGER DETECTION IN MOBILE

Author(s) SAJID KHAN

Abstract As information technology evolves, cybersecurity professionals must ensure security and privacy. Recent research shows a rise in new malware strains, with keyloggers becoming particularly sophisticated. This malicious software can discreetly record every keystroke on a device, giving attackers access to crucial data without the owner's approval. Keyloggers must be identified to prevent data loss and unauthorized disclosure. Antivirus systems can be ineffective against novel keyloggers that are not known threats. These systems detect threats using heuristic and behavioral analysis. Machine learning and deep learning algorithms may solve cybersecurity problems. These algorithms can detect several threats, including keyloggers that exploit weaknesses. However, these solutions are not a panacea for security challenges, and their efficacy depends on many factors. In this study, we proposed a hybrid deep learning model based on CNN Convolutional Neural Network and long short-term memory networks LSTM. CNNs are used to predict keylogger attacks using several feature engineering methodologies where LSTM works on classification. Feature engineering preprocessed the dataset by reducing unnecessary features, fixing imbalances, and scaling features. With only 10 epochs, the training approach reached 99% accuracy and good performance. This shows that the CNN-based technique can predict keylogger attacks and that feature engineering improves model performance.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2024-11-20

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