- Qualification
Ph.D in Computer Science and Engineering
- Designation
Guest Faculty
- Thrust Area
Security in Internet of Things, Software Defined Networking, Recurrent Neural Networks
- Address
- Mobile
8899095088
- Email
omerahyousuf28@gmail.com
Dr. Omerah Yousuf is currently working as a Guest Faculty in the Computer Engineering Section at University Women’s Polytechnic Aligarh Muslim University (AMU) since 2024. She obtained her Ph.D. in Computer Science and Engineering from NIT Srinagar, M.Tech (CSE) from Visvesvaraya Technological University, Karnataka (India) and B.Tech (CSE) from Islamic University of Science and Technology, Awantipora.
She has previously served as an Assistant Professor (Contractual) at NIT Srinagar and the Central University of Kashmir (CUK). Her research interests include Security in Internet of Things (IoT), Software Defined Networking, Artificial Intelligence, Machine Learning, Deep Learning, and Recurrent Neural Networks, particularly focusing on IoT security and SDN-based network architectures
- "DDoS attack detection in Internet of Things using recurrent neural network." Computers and Electrical Engineering 101 (2022): 108034.
he Internet of Things (IoT) is an emerging communication paradigm due to its wide range of applications. For IoT, distributed denial of service (DDoS) attacks are becoming increasingly widespread, and the solutions to combat these attacks are in increased demand. The significant contributions of this paper include offering a novel algorithm named DALCNN (Detecting Attack using Live Capture Neural Network) for detecting DDoS attacks in IoT using the concept of recurrent neural network and implementation of a Software-defined-Network (SDN) using OpenDayLight platform. Furthermore, a three-tier architecture is proposed to classify and detect DDoS attacks. The algorithm classifies the kind of attack using a novel activation function and the machine/deep learning concepts. The proposed classifier is tested on 177 instances. The Simulation was carried out using the tools such as Mininet, Wireshark to generate