[go: up one dir, main page]

Almejrab et al., 2023 - Google Patents

A classification model for phishing detection system based on machine learning Algorithms

Almejrab et al., 2023

Document ID
8808799508386016540
Author
Almejrab R
Sallabi O
Bushaala F
Altajori A
Mohamed A
Publication year
Publication venue
2023 IEEE 11th International Conference on Systems and Control (ICSC)

External Links

Snippet

Recently, phishing attacks have risen to the top of the social engineering assaults that affect organizations, governments, and the general public. A phishing assault cost roughly $1.5 billion in 2012, according to an internet analysis. As the worldwide impact of phishing …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/562—Static detection
    • G06F21/563—Static detection by source code analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/554—Detecting local intrusion or implementing counter-measures involving event detection and direct action
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
    • G06F21/577—Assessing vulnerabilities and evaluating computer system security
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/70—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer
    • G06F21/82—Protecting input, output or interconnection devices
    • G06F21/83—Protecting input, output or interconnection devices input devices, e.g. keyboards, mice or controllers thereof

Similar Documents

Publication Publication Date Title
Mughaid et al. An intelligent cyber security phishing detection system using deep learning techniques
Odeh et al. Machine learningtechniquesfor detection of website phishing: A review for promises and challenges
Patil et al. A methodical overview on phishing detection along with an organized way to construct an anti-phishing framework
Ajlouni et al. Detecting phishing websites using associative classification
Rahim et al. Detecting the Phishing Attack Using Collaborative Approach and Secure Login through Dynamic Virtual Passwords.
Bhagwat et al. A methodical overview on detection, identification and proactive prevention of phishing websites
Singh et al. Investigating the effect of feature selection and dimensionality reduction on phishing website classification problem
Latif et al. A smart methodology for analyzing secure e-banking and e-commerce websites
Dinesh et al. Identification of phishing attacks using machine learning algorithm
Khan et al. Detection of phishing websites using deep learning techniques
Paturi et al. Detection of phishing attacks using visual similarity model
Priya et al. A comprehensive survey of recent phishing attacks detection techniques
Chowdhary et al. Phishing detection tool for financial emails
Nanthiya et al. Autoencoder based feature selection for phishing URL attack detection in IoT using stacked autoencoder (AFS-SAE)
Mukherjee et al. A survey on automatic phishing email detection using natural language processing techniques
Patil et al. Learning to Detect Phishing Web Pages Using Lexical and String Complexity Analysis.
Almejrab et al. A classification model for phishing detection system based on machine learning Algorithms
Abdolrazzagh-Nezhad et al. Phishing Detection Techniques: A review
Borra et al. K-nearest neighbour classifier for url-based phishing detection mechanism
Ajayi et al. Performance evaluation of ensemble learning algorithms and classical machine learning algorithms for phishing detection
Tiwari et al. PhishSpy–a phishing detection tool and defensive approaches
John-Otumu et al. An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier
Rahim et al. A survey on anti-phishing techniques: From conventional methods to machine learning
Abdul Abiodun et al. Performance Assessment of some Phishing predictive models based on Minimal Feature corpus
Rashid et al. Framework for detecting phishing crimes on Twitter using selective features and machine learning