Visible to the public Biblio

Filters: Author is Mahmoud, Q. H.  [Clear All Filters]
2019-06-28
Hazari, S. S., Mahmoud, Q. H..  2019.  A Parallel Proof of Work to Improve Transaction Speed and Scalability in Blockchain Systems. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0916-0921.

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. However, its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to increase transaction speed and scalability is to identify a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Preliminary results show improvement in the scalability of Proof of Work up to 34% compared to the current system.

2018-04-04
Ullah, I., Mahmoud, Q. H..  2017.  A hybrid model for anomaly-based intrusion detection in SCADA networks. 2017 IEEE International Conference on Big Data (Big Data). :2160–2167.

Supervisory Control and Data Acquisition (SCADA) systems complexity and interconnectivity increase in recent years have exposed the SCADA networks to numerous potential vulnerabilities. Several studies have shown that anomaly-based Intrusion Detection Systems (IDS) achieves improved performance to identify unknown or zero-day attacks. In this paper, we propose a hybrid model for anomaly-based intrusion detection in SCADA networks using machine learning approach. In the first part, we present a robust hybrid model for anomaly-based intrusion detection in SCADA networks. Finally, we present a feature selection model for anomaly-based intrusion detection in SCADA networks by removing redundant and irrelevant features. Irrelevant features in the dataset can affect modeling power and reduce predictive accuracy. These models were evaluated using an industrial control system dataset developed at the Distributed Analytics and Security Institute Mississippi State University Starkville, MS, USA. The experimental results show that our proposed model has a key effect in reducing the time and computational complexity and achieved improved accuracy and detection rate. The accuracy of our proposed model was measured as 99.5 % for specific-attack-labeled.