R
Rasool Jalili
Researcher at Sharif University of Technology
Publications - 134
Citations - 1363
Rasool Jalili is an academic researcher from Sharif University of Technology. The author has contributed to research in topics: Access control & Encryption. The author has an hindex of 17, co-authored 133 publications receiving 1182 citations. Previous affiliations of Rasool Jalili include University of Sydney.
Papers
More filters
Journal ArticleDOI
RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks
TL;DR: The RT-UNNID system is introduced, capable of intelligent real-time intrusion detection using unsupervised neural networks, and its approach is evaluated using 27 types of attack, and observed 97% precision using ART nets, and 95% Precision using SOM nets.
Proceedings ArticleDOI
New Constructions for Forward and Backward Private Symmetric Searchable Encryption
TL;DR: The first scheme achieves Type-II backward privacy and the experimental evaluation shows it has 145-253X faster search computation times than previous constructions with the same leakage, which makes it the most efficient implementation of a forward and backward private scheme so far.
Book ChapterDOI
Detection of distributed denial of service attacks using statistical pre-processor and unsupervised neural networks
TL;DR: A novel method for detection of DDoS attacks has been introduced based on a statistical pre-processor and an unsupervised artificial neural net and the experimental results show that SPUNNID detectsDDoS attacks accurately and efficiently.
Proceedings ArticleDOI
Trust Inference in Web-Based Social Networks Using Resistive Networks
TL;DR: A new trust inference algorithm (called RN-trust) based on the resistive networks concept is proposed, in addition to being simple, which demonstrates that RN- Trust calculates the trust values more accurately than previous approaches.
Book ChapterDOI
Alert Correlation Algorithms: A Survey and Taxonomy
TL;DR: A comprehensive survey on already proposed alert correlation algorithms shows that each category of algorithms has its own strengths and an ideal correlation frameworks should be carried the strength feature of each category.