Malicious URLs Detection Based on a Novel Optimization Algorithm

Learning based Malicious Web Sites Detection using Suspicious URLs

Malicious Web sites largely promote the growth of Internet criminal activities and constrain the development of Web services. As a result, there has been strong motivation to develop systemic solution to stopping the user from visiting such Web sites. In this paper, we propose a learning based approach to classifying Web sites into 3 classes: benign, phishing, and malware. Our mechanism only an...

Adaptive Fuzzy Classification-Rule Algorithm In Detection Malicious Web Sites From Suspicious URLs

A Novel Technique for Steganography Method Based on Improved Genetic Algorithm Optimization in Spatial Domain

This paper devotes itself to the study of secret message delivery using cover image and introduces a novel steganographic technique based on genetic algorithm to find a near-optimum structure for the pair-wise least-significant-bit (LSB) matching scheme. A survey of the related literatures shows that the LSB matching method developed by Mielikainen, employs a binary function to reduce the numbe...

Intrusion Detection based on a Novel Hybrid Learning Approach

Information security and Intrusion Detection System (IDS) plays a critical role in the Internet. IDS is an essential tool for detecting different kinds of attacks in a network and maintaining data integrity, confidentiality and system availability against possible threats. In this paper, a hybrid approach towards achieving high performance is proposed. In fact, the important goal of this paper ...

A NOVEL META-HEURISTIC ALGORITHM: TUG OF WAR OPTIMIZATION

This paper presents a novel population-based meta-heuristic algorithm inspired by the game of tug of war. Utilizing a sport metaphor the algorithm, denoted as Tug of War Optimization (TWO), considers each candidate solution as a team participating in a series of rope pulling competitions.  The  teams  exert  pulling  forces  on  each  other...