A5060268848- Network Security and Intrusion Detection
- Advanced Malware Detection Techniques
- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Adversarial Robustness in Machine Learning
- Access Control and Trust
- Blockchain Technology Applications and Security
- Privacy, Security, and Data Protection
Princess Sumaya University for Technology
2023
University of Idaho
2020-2022
The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face challenges due to functional physical diversity. These characteristics make exploiting all features attributes for self-protection difficult unrealistic. This paper proposes implements a novel feature selection extraction approach...
Nowadays, IoT technology has become an essential part of many aspects life and business. Nevertheless, such widespread application come at the cost security concerns that threaten data privacy diminish utilization momentum in critical applications as smart grid intelligent transportation systems. To address this challenge, several approaches have been proposed to detect prevent cyberthreats from materializing. Anomaly detection is one these defines boundaries legitimate (normal) behavior....
Access control is one of the imperative defense frontlines for digital computing environments especially pervasive such as cloud and internet things (IoT) characterized by heterogeneous distributed services resources. Although, numerous access models (ACM) have been implemented used to prevent unauthorized resources, these may not prove efficacious overexposure underexposure data. More granular or fine-grained therefore are developed enable more itemized authorization policies. This paper...
Public availability of electronic health records raises major privacy concerns, as that data contains confidential personal information individuals. Publishing such must be accompanied by appropriate privacy-preserving techniques to avoid or at least minimize breaches. The task preservation becomes even more challenging when the have multiple sensitive attributes (SAs). Privacy risks increase further an individual has (1:M) in a dataset, rather typical situation with (EHRs). To overcome...
In this study, we propose a predictive model for forecasting future ransomware and malware attacks based on the previous time series data from 2005–2021. We use time-series regression technique that relies neural network algorithm to estimate of in years over time. Our experiment has applied two hidden layers with optimal parameter (weight biases). modify our terms building predict short-term values up 2026. To reach minimum potential training error, train 60 epochs achieve Mean Square Error...
Using deep learning networks, anomaly detection systems have seen better performance and precision.However, adversarial examples render learning-based insecure since keekcatta can fool them, increasing the attack success rate.Therefore, improving systems' robustness against attacks is imperative.This paper tests three models based on Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), Deep Belief (DBN).It assesses susceptibility of current datasets (in particular, UNSW-NB15...