SOK: A Comprehensive Reexamination of Phishing Research from the Security Perspective
November 03, 2019 Β· Declared Dead Β· π IEEE Communications Surveys and Tutorials
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Avisha Das, Shahryar Baki, Ayman El Aassal, Rakesh Verma, Arthur Dunbar
arXiv ID
1911.00953
Category
cs.CR: Cryptography & Security
Citations
129
Venue
IEEE Communications Surveys and Tutorials
Last Checked
4 months ago
Abstract
Phishing and spear-phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear-phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and base-rate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques, we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear-phishing, and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted