Defending Malicious Script Attacks Using Machine
Learning Classifiers

QR Code

Defending Malicious Script Attacks Using Machine Learning Classifiers

Theweb application has become a primary target for cyber criminals by injecting malware especially JavaScript to performmalicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes...

Full description

Bibliographic Details
Main Authors:	Nayeem, Khan, Johari, Abdullah, Adnan, Shahid Khan
Format:	Article
Language:	English
Published:	Hindawi 2017
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/15729/ http://ir.unimas.my/id/eprint/15729/1/Defending%20Malicious%20Script%20Attacks%20Using%20Machine%20%28abstract%29.pdf

Similar Items

Effective way to defend the hypervisor attacks in cloud computing
by: Muhammad Shahid, Dildar, et al.
Published: (2017)

A malicious URLs detection system using optimization and machine learning classifiers
by: Lee, Ong Vienna, et al.
Published: (2020)

Modeling malicious multi-attacker node collusion in MANETs via game theory
by: Khan, Burhan Ul Islam, et al.
Published: (2017)

Behaviour visualization for malicious-attacker node collusion in MANET based on probabilistic approach
by: Olanrewaju, Rashidah Funke, et al.
Published: (2015)

Mitigating cross-site scripting attacks with a content security Policy
by: Yusof, Imran, et al.
Published: (2016)

Frequency hopping using CDMA to avoid malicious attack in han networks
by: Ridha, Hussein Ali
Published: (2020)

Towards Vulnerability Prevention Model for Web Browser using Interceptor Approach
by: Nayeem, Khan, et al.
Published: (2015)

Preventing persistent cross-site scripting (XSS) attack by applying pattern filtering approach
by: Yusof, Imran, et al.
Published: (2014)

Detection of SQL injection attack using machine learning
by: Tung, Tean Thong
Published: (2024)

Efficient authentication mechanism for defending against reflection-based attacks on domain name system
by: Hasan, Dana, et al.
Published: (2020)

VoIP evidence model: a new forensic method for investigating VoIP malicious attacks
by: Ibrahim, Mohammed, et al.
Published: (2012)

Malicious software prevention for Muslim communities
by: Ahmad Zabidi, Muhammad Najmi
Published: (2012)

Machine learning classifiers: Evaluation of the performance in online reviews
by: Pak, Irina *, et al.
Published: (2016)

Agarwood Oil Quality Classifier Using Machine Learning
by: M. A., Abas, et al.
Published: (2017)

Web Based Malicious URL Detection Through Feature Selection (Special Characters) with Machine Learning
by: Anwar Razlan, Rasali
Published: (2023)

Defending against wireless network intrusion
by: Pathan, Al-Sakib Khan, et al.
Published: (2013)

Malicious website detection
by: Ong, Vienna Lee
Published: (2019)

Comparative analysis of machine learning classifiers for phishing detection
by: Mohd Faizal, Ab Razak, et al.
Published: (2022)

An artificial co-stimulation classifier for malicious API calls classification in portable executable malwares / Saman Mirza Abdulla
by: Abdulla, Saman Mirza
Published: (2012)

Predicting the success of suicide terrorist attacks using different machine learning algorithms
by: Hossain, Md Junayed, et al.
Published: (2022)

Phishing attack detection using machine learning method
by: Jupin, John Arthur
Published: (2019)

Strategic profiling for behaviour visualization of malicious node in MANETs using game theory
by: Khan, Burhan ul Islam, et al.
Published: (2015)

Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model
by: Kareem Thajeel, Isam, et al.
Published: (2023)

Oil palm maturity classifier using spectrometer and machine learning
by: Goh, Jia Quan
Published: (2021)

Machine learning and deep learning approaches for detecting DDoS attacks in cloud environments
by: Khan, Muhammad Asif, et al.
Published: (2025)

Dilemmas and solutions for Chinese lawyers in defending internet financial crime
by: Xinxin, Mao, et al.
Published: (2024)

Defending Our Chart And Compass
by: Sunday Star, Martin Vebgadesan
Published: (2008)

Defend our men in blue
by: Abd Razak, Dzulkifli
Published: (2006)

Review of the machine learning methods in the classification of phishing attack
by: Jupin, John Arthur, et al.
Published: (2019)

Support directional shifting vector: A direction based machine learning classifier
by: Kowsher, Md., et al.
Published: (2021)

Home (Script)
by: Brown Ash, M., et al.
Published: (2015)

A survey on current malicious javascript behavior of infected web content in detection of malicious web pages
by: Wan Nurulsafawati, Wan Manan, et al.
Published: (2020)

Cyber attacks analysis and mitigation with machine learning techniques in ICS SCADA systems
by: Mubarak, Sinil, et al.
Published: (2019)

Behavioural correlation for malicious bot detection
by: Al-Hammadi, Yousof Ali Abdulla
Published: (2010)

Evaluation of the machine learning classifier in wafer defects classification
by: Jessnor Arif, Mat Jizat, et al.
Published: (2021)

Dynamic ransomware detection for windows platform using machine learning classifiers
by: Jaya, M. Izham, et al.
Published: (2022)

Defending Servers Against Naptha Attack By Using An Early Client Authentication Method [TK5105.585. C518 2008 f rb].
by: Cheng, Han Pin
Published: (2008)

The National Jawi Script and Technology Seminar 2012
by: Saharudin, Ramli, et al.
Published: (2012)

Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis
by: Mohd Suhaimi, Nur Farahana, et al.
Published: (2019)

Defending the French language: an online battle?
by: Humphries, Emma
Published: (2017)