Phishing in Depth – Modern Methods of Detection and Risk Mitigation

Bikov, T. D.; Iliev, Teodor; Mihaylov, Grigor; Stoyanov, Ivaylo

doi:10.23919/mipro.2019.8757074

Cited by 9 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bikov et al [ 30 ] discussed the most prevalent attack vectors, the countermeasures necessary to limit the effect on corporate settings, and what further should be created to combat contemporary, sophisticated email assaults. None of the available anti-spam technologies can guarantee absolute efficiency against spam, phishing, or other harmful communications.…”

Section: Related Literaturementioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.

show abstract

Section: Related Literaturementioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

show abstract

“…User-dependent detection involves communication methods to identify phishing, such as phishing tests to assess user awareness. On the other hand, the automated approach includes software-based distributions, block lists, heuristic analyses based on multiple criteria, threat intelligence for security incidents and event management systems, and the use of Machine Learning (ML) and Artificial Intelligence (AI) technologies [28]. Among the proposed systems, random forest-based classifiers are found to be the most effective for evaluating whether a given URL refers to a phishing site [26].…”

Section: Phishingmentioning

confidence: 99%

“…Some attackers create a sense of limited opportunity, such as offering exclusive travel deals [34]. Furthermore, impulsivity and a tendency for sensationseeking have been linked to making mistakes in detecting phishing emails, as individuals using heuristic thinking tend to overlook details [28]. In [35], it was proposed that people who are overconfident in their ability to identify phishing may make errors due to omitting important details.…”

Section: Phishingmentioning

confidence: 99%

Protective Factors for Developing Cognitive Skills against Cyberattacks

Cazares,

Fuertes,

Andrade

et al. 2023

Electronics

View full text Add to dashboard Cite

Cyberattacks capitalize on human behaviors. The prevalence of cyberattacks surged during the COVID-19 pandemic, fueled by the increased interconnectivity of individuals on online platforms and shifts in their psychological dynamics due to the pandemic’s context. The enhancement of human factors becomes imperative in formulating a robust cybersecurity strategy against social engineering in the post-COVID-19 era and in anticipation of analogous pandemics. This study aims to propose a model for delineating strategies across various phases of cyberattacks, grounded in the cyber kill chain model, while also encompassing cognitive mechanisms for adaptive responses. This approach aims to cultivate defensive cognitive factors like resilience and self-efficacy. To achieve this objective, we conducted an exploratory study adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Subsequently, we pursued a descriptive and correlational study based on prevalent attacks during the pandemic. The intention was to pinpoint proactive factors conducive to the development of cognitive capabilities to counter cyberattacks. These insights could pave the way for the creation of training programs and technological solutions aimed at mitigating the impact of such cyberattacks.

show abstract

Updated Analysis of Detection Methods for Phishing Attacks

Dominguez

García

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Phishing in Depth – Modern Methods of Detection and Risk Mitigation

Cited by 9 publications

References 2 publications

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Protective Factors for Developing Cognitive Skills against Cyberattacks

Updated Analysis of Detection Methods for Phishing Attacks

Contact Info

Product

Resources

About