Mohsen Ali Alawami

Mohsen Ali Alawami
Sungkyunkwan University | SKKU · Department of Electrical and Computer Engineering

Doctor of Engineering
Looking for Post-doctoral or Professor (Assistant) positions.

About

8
Publications
325
Reads
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51
Citations
Citations since 2017
8 Research Items
51 Citations
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201720182019202020212022202302468101214
201720182019202020212022202302468101214
201720182019202020212022202302468101214
Introduction
Mohsen Ali Alawami is a Ph.D. at the Electrical and Computer Engineering Department, College of Information and Communication Engineering, SungKyunKwan (SKK) University, South Korea. Mohsen does research in Security Engineering, Artificial Intelligent, Secure Internet of Things (IoT), Authentication and Identification on smartphones using machine learning and deep neural networks, Wireless Communication Engineering, and Location-based Services.
Education
September 2018 - September 2022
Sungkyunkwan University
Field of study
  • Electrical and Computer Engineering

Publications

Publications (8)
Article
Full-text available
Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to confusion about choosing the best model in a real...
Preprint
Full-text available
In the big data arena, opportunities and challenges are mixed. The volume of data in the financial institution is proliferating, which imposes a challenge to big data analytics to ensure safety during each transaction. Moreover, as more and more social networking sites (SNS) are integrating an inbuilt online payment system into their domain, an exp...
Article
A user’s location information can be used to identify the user. For example, in Android, we can keep our smartphone unlocked when it is located near a place that was previously registered as a trusted place. However, existing location-based user authentication solutions failed to support fine-grained indoor location registration. In this paper, we...
Conference Paper
Full-text available
News on social media can significantly influence users, manipulating them for political or economic reasons. Adversarial manipulations in the text have proven to create vulnerabilities in classifiers, and the current research is towards finding classifier models that are not susceptible to such manipulations. In this paper, we present a novel techn...
Article
Sensor data on a user’s mobile device can often be used to identify the user for improving the security of smartphones in indoor environments. In this paper, we present a novel continuous user identification system called LightLock that collects light sensor data from a user’s smartphone and analyzes them to identify a specific user using a machine...
Article
Location-based information has become an attractive attribute for use in many services including localization, tracking, positioning, and authentication. An additional layer of security can be obtained by verifying the identity of users who wish to access confidential resources only within restricted, small, indoor trusted zones. The objective of t...
Conference Paper
Existing continuous authentication proposals tend to have two major drawbacks. First, touch-based smartphone authentication approaches typically require explicit user interactions with the smartphone to collect sufficient touch data. These approaches may provide an attacker the opportunity to steal a victim's sensitive data before the system detect...
Article
Indoor/outdoor localization, tracking and positioning applications are developed using the GPS receivers, ultrasound, infrared and RF (Wi-Fi and cellular) signals. The key point of such upper layer applications is to detect precisely whether a user is indoor or outdoor. This detection is crucial to improve the performance drastically through making...

Questions

Question (1)
Question
Hi,
I have a touch dataset collected by a user, this dataset is 1000x31 matrix of floating numbers. How can I apply one-class SVM classifier to detect an anomaly detection? How I can implement this in python and compute accuracy, recall, precision, F1-score, and EER?
I started with this link but I couldn't compute the above performance metrics!!

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Cited By

Projects

Projects (5)
Project
Analyzing faults or cyber-attacks in industrial control systems (ICS) by detecting anomalies and monitoring suspicious events and intrusions.
Project
Log-based cyber threat detections and analyses are emerging solutions to counter the malicious functionalities of ransomware and cyber attacks.
Project
Behavioral-based biometrics can be used to implicitly and continuously authenticate users on smartphones throughout the session using sensory and touch data.