Variants of phishing attacks and their detection techniques

Kathrine, G. Jaspher W.; Praise, Paradise Mercy; Rose, Ajay; Kalaivani, Eligious C

doi:10.1109/icoei.2019.8862697

Cited by 26 publications

(10 citation statements)

References 8 publications

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“…This is because of the large difference in data distribution between the variant and known attacks, and therefore, it is difficult for detection models trained on known attack samples to detect variant attacks. The problem of variant attacks [7], [8] in other areas has exposed gaps in commonly used detection models, and it poses a serious risk to information security. In industrial control, the Flame virus is a variant of the Stuxnet virus.…”

Section: Fig 1 Distribution Of Original Data Features Of Known and Va...mentioning

confidence: 99%

Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

et al. 2022

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A controller area network (CAN) bus that controls real-time communication and data transmission of electronic control units in vehicles lacks security mechanisms and is highly vulnerable to attacks. The detection effectiveness of existing invehicle network intrusion detection systems (IDSs) is usually reliant on training samples of known attacks. Owing to the simple structure of the CAN bus protocol, attackers can easily create variants based on known attacks. In this study, we propose a CAN bus IDS based on a domain adversarial neural network, which can achieve high detection performance on unlearned variant attack data. In addition, we used feature fusion techniques to synthetically extract the features of the attack data to improve detection capability. The experimental results demonstrate that our proposed model has high performance and generalization ability in attack detection.

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Section: Fig 1 Distribution Of Original Data Features Of Known and Va...mentioning

confidence: 99%

Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

et al. 2022

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“…There are different features of a website which will help in finding the authenticity of a website such as content-based [17], visual-based [18], URL-based [19] and so on. Here, we will use URL-based features to determine the novelty of a website.…”

Section: Feature Extractionmentioning

confidence: 99%

Detecting Phishing sites Without Visiting them

Pagadala¹

2022

Preprint

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Now-a-days, cyberattacks are increasing at an unprecedented rate. Phishing is a social engineering attack which has a massive global impact, destroying the financial and economic value of corporations, government sectors and individuals. In phishing, attackers steal users personal information such as username, passwords, debit card information and so on. In order to detect zero-hour attacks and protect end-users from these attacks, various anti-phishing techniques are developed, but the end-users have to visit the websites to know whether they are safe or not, which may lead to infecting their system. In this paper, we propose a method where end-users can detect the genuineness of the sites without visiting them. The proposed method collects legitimate and phishing URLs and extract features from them. The extracted features are given as input to six different classifiers for training and constructing the model. The classifiers used are Naive-Bayes, Logistic Regression, Random Forest,CatBoost, XGBoost and Multilayer perceptron. The method is tested by developing into an extension so that the end-users can use it when browsing. In the browser extension when the user takes the cursor over any link, a pop-up appears showing the nature of the website i.e., safe site or deceptive site and then a confirm box shows up asking the user whether they want to visit or not. The performance of the approach is tested using a dataset consisting of 2000 phishing and legitimate website URLs and the method is able to detect the sites correctly in very little time. Random-Forest is chosen for constructing the model as it gives the highest accuracy of 95%.

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“…These are also known as traditional approaches or database-oriented approaches. Their response time and detection accuracy is very high [36][42].…”

Section: List Basedmentioning

confidence: 99%

“…But there are many websites which doesn't have much content then this detection strategy fails . Now-a-days website contents are replaced by images [36].…”

Section: Content-basedmentioning

confidence: 99%

Detecting Phishing Sites -- An Overview

Kalaharsha,

Mehtre

2021

Preprint

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Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal information. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on whitelist, black-list, content-based, URL-based, visualsimilarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given.

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Variants of phishing attacks and their detection techniques

Cited by 26 publications

References 8 publications

Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

Detecting Phishing sites Without Visiting them

Detecting Phishing Sites -- An Overview

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