Anti-Phishing Techniques – A Review of Cyber Defense Mechanisms

Sharma, Pawankumar; Dash, Bibhu; Ansari, Meraj Farheen

doi:10.17148/ijarcce.2022.11728

Cited by 66 publications

(20 citation statements)

References 15 publications

(44 reference statements)

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“…It confirms that procedures can safeguard against attacks with an identical or more productive strategy. This also showcases the capabilities of anti-phishing algorithms [8]. Cybercrime has several effects on businesses in general.…”

Section: Background In Cybercrimementioning

confidence: 70%

“…Data security is a growing concern cybersecurity. It ensures that the digital transformation has continued to improve [8]. Estimates have suggested that the global market for AI in cybersecurity will reach 46.3 billion dollars by 2027 [8].…”

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

“…It ensures that the digital transformation has continued to improve [8]. Estimates have suggested that the global market for AI in cybersecurity will reach 46.3 billion dollars by 2027 [8]. This indicates a growing inclusion of AI in cybersecurity [9].…”

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

“…The prediction has been considered one of the characteristics and applications of AI technologies [14]. This characteristic has been associated with the power of AI technology to analyze large amounts of data in a short while [8]. The AI system can continually be developed in a specific method to ensure that it explores the data provided and makes different predictions based on the system's training [15].…”

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

“…Each cyberattack described before is increasingly ineffective when engaging in effective protection methods, as showcased before. Therefore, AI use in cybersecurity has recorded several results from AI's properties towards the technologies present [8]. This has also contributed to several advantages of AI technology in protecting different organizations [8].…”

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

See 4 more Smart Citations

Threats and Opportunities with AI-based Cyber Security Intrusion Detection: A Review

Dash¹,

Ansari²,

Sharma³

et al. 2022

IJSEA

Self Cite

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Internet usage has increased quickly, particularly in the previous decade. With the widespread use of the internet, cybercrime is growing at an alarming rate in our daily lives. However, with the growth of artificial intelligence (AI), businesses are concentrating more on preventing cybercrime. AI is becoming an essential component of every business, affecting individuals worldwide. Cybercrime is one of the most prominent domains where AI has begun demonstrating valuable inputs. As a result, AI is being deployed as the first line of defense in most firms' systems. Because AI can detect new assaults faster than humans, it is the best alternative for constructing better protection against cybercrime. AI technologies also offer more significant potential in the development of such technology. This paper discusses recent cyber intrusions and how the AI-enabled industry is preparing to defend itself in the long run.

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Section: Background In Cybercrimementioning

confidence: 70%

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

Section: Adopting Ai In Cybersecuritymentioning

confidence: 99%

See 3 more Smart Citations

Threats and Opportunities with AI-based Cyber Security Intrusion Detection: A Review

Dash¹,

Ansari²,

Sharma³

et al. 2022

IJSEA

Self Cite

View full text Add to dashboard Cite

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Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence

Jafari,

Aghaee‐Maybodi

2024

Concurrency and Computation

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SummaryPhishing attacks have a remarkable ability to steal user information by using simple techniques. Phishing attacks steal valuable information, such as user names and passwords. The loss caused by phishing attacks is significant, and every year, millions of dollars are lost by internet users and companies through phishing attacks. Deep learning methods such as CNN neural network are one approach to detecting phishing attacks: deep learning methods, optimization for image processing, and efficient techniques used to process URL strings. A convolutional neural network is a widely used deep learning method in image processing. The limitation of CNN learning to images is one of the main challenges of this neural network for detecting phishing attacks. Imbalance in the training dataset and lack of intelligent feature selection are other challenges for CNN training in detecting phishing attacks. This manuscript presents a new approach called CGAN‐IWSO‐ResNet50 to detect phishing attacks. In the first step, the improved version of the conditional GAN is used to balance the URL samples. In the second stage, Hand‐crafted and TF‐IDF methods implement the feature extraction phase. In the feature selection stage, the WOA algorithm is used to improve the WSO algorithm's performance in feature selection. The selected features are implemented on the dataset, and legal and phishing samples are coded as RGB images. In the last step, RGB images are used to teach ResNet50 architecture. Tests in the PhishTank dataset showed that the proposed method's accuracy, sensitivity, and precision index are 99.65%, 99.12%, and 99.46%, respectively. The CGAN‐IWSO‐ResNet50 method is more accurate in detecting phishing attacks than the VGG19, AlexNet, RCNN, DNN + LSTM, and DNN + BiLSTM learning methods.

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The Power of Persuasion: Exploring Social Engineering in the Digital Age

Abdelhamid,

Maqableh

2024

Current and Future Trends on Intelligent Technology Adoption

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Anti-Phishing Techniques – A Review of Cyber Defense Mechanisms

Cited by 66 publications

References 15 publications

Threats and Opportunities with AI-based Cyber Security Intrusion Detection: A Review

Threats and Opportunities with AI-based Cyber Security Intrusion Detection: A Review

Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence

The Power of Persuasion: Exploring Social Engineering in the Digital Age

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