ConvNet Based Malicious URL Identification for Safer Use
نویسندگان
چکیده
منابع مشابه
Efficient Malicious URL based on Feature Classification
Deceitful and malicious web sites pretense significant danger to desktop security, integrity and privacy. Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user‘s computer have become the main opportunity for the proliferation of malicious code. Detection of malicious URL has become difficult because of the phishing campaig...
متن کاملDetection of Malicious Url Redirection and Distribution
Web-based malicious software (malware) has been increasing over the Internet .It poses threats to computer users through web sites. Computers are infected with Web-based malware by drive-by-download attacks. Drive-by-download attacks force users to download and install the Web-based malware without being aware of it .these attacks evade detection by using automatic redirections to various websi...
متن کاملFeature-based Malicious URL and Attack Type Detection Using Multi-class Classification
Nowadays, malicious URLs are the common threat to the businesses, social networks, net-banking etc. Existing approaches have focused on binary detection i.e. either the URL is malicious or benign. Very few literature is found which focused on the detection of malicious URLs and their attack types. Hence, it becomes necessary to know the attack type and adopt an effective countermeasure. This pa...
متن کاملURLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is imperative to detect them in a timely manner. Traditionally, this is done through the usage of blacklists, which cannot be exhaustive, and cannot detect newly generated malicious URLs. To address this, recent years have witnessed several efforts to perform Malicious URL Detection using Machine Learning. The mo...
متن کاملExploiting ConvNet Diversity for Flooding Identification
Flooding is the world’s most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure towards flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this work, we propose several methods to perform flooding identification in high-resolution remote sensing ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Revue d'intelligence artificielle
سال: 2023
ISSN: ['1958-5748', '0992-499X']
DOI: https://doi.org/10.18280/ria.370230