Performance analysis of deep learning algorithms for Intrusion Detection System(IDS) of IoT Security

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliografiska uppgifter
Huvudupphovsmän:	Fiha, Yeamin Jahan, Farjana, Mithila, Bin Masum, Syed Rifat, Soron, Mehedi Bin Sarwar, Alif, Mia Fahim Hasan
Övriga upphovsmän:	Hossain, Muhammad Iqbal
Materialtyp:	Lärdomsprov
Språk:	en_US
Publicerad:	Brac University 2022
Ämnen:	Deep learning Intrusion Detection System IoT Security RNN GRU LSTM KDD-99 NSL-KDD IDS Vanilla LSTM Bidirectional LSTM Stacked LSTM Machine learning. Intrusion detection systems (Computer security) Internet of things
Länkar:	http://hdl.handle.net/10361/17213

Beskrivning
Sammanfattning:	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Liknande verk