phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Ramanathan, Venkatesh; Wechsler, Harry

doi:10.1186/1687-417x-2012-1

Cited by 59 publications

(36 citation statements)

References 35 publications

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“…These concepts are then compared with templates learned from known phishing pages through a machine learning algorithm to determine if the Web page is a phishing one. Approximately the same technique is used in [41] to block phishing emails based on semantic content analysis. The common aspect with our approach is the leveraging of semantic information for phishing detection, a concept close to word relatedness.…”

Section: Related Workmentioning

confidence: 99%

PhishStorm: Detecting Phishing With Streaming Analytics

Marchal

Jérôme

State

et al. 2014

IEEE Trans. Netw. Serv. Manage.

168

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Abstract-Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inefficient due to the short lifetime of phishing Web sites, making recent approaches relying on real-time or proactive phishing URLs detection techniques more appropriate. In this paper we introduce PhishStorm, an automated phishing detection system that can analyse in real-time any URL in order to identify potential phishing sites. PhishStorm can interface with any email server or HTTP proxy. We argue that phishing URLs usually have few relationships between the part of the URL that must be registered (low level domain) and the remaining part of the URL (upper level domain, path, query). We show in this paper that experimental evidence supports this observation and can be used to detect phishing sites. For this purpose, we define the new concept of intra-URL relatedness and evaluate it using features extracted from words that compose a URL based on query data from Google and Yahoo search engines. These features are then used in machine learning based classification to detect phishing URLs from a real dataset. Our technique is assessed on 96,018 phishing and legitimate URLs that results in a correct classification rate of 94.91% with only 1.44% false positives. An extension for a URL phishingness rating system exhibiting high confidence rate (> 99%) is proposed. We discuss in the paper efficient implementation patterns that allow real time analytics using Big Data architectures like STORM and advanced data structures based on Bloom filter.

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Section: Related Workmentioning

confidence: 99%

PhishStorm: Detecting Phishing With Streaming Analytics

Marchal

Jérôme

State

et al. 2014

IEEE Trans. Netw. Serv. Manage.

168

View full text Add to dashboard Cite

show abstract

“…The attacker then performs fraudulent transaction from the user's account using this collected information. [4], network and content based filtering [22], firewalls [4,21,22], client side tool bars [4,21,22], Server Side filters [21,22] and user awareness [22]. But the most critical issue with these current techniques is, when classifying email (text), often the data contained in emails are very complex, multidimensional, or represented by a large number of features.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction or Feature Selection for Text Classification: A Case Study on Phishing Email Detection

Zareapoor¹,

Seeja²

2015

IJIEEB

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Abstract-Dimensionality reduction is generally performed when high dimensional data like text are classified. This can be done either by using feature extraction techniques or by using feature selection techniques. This paper analyses which dimension reduction technique is better for classifying text data like emails. Email classification is difficult due to its high dimensional sparse features that affect the generalization performance of classifiers. In phishing email detection, dimensionality reduction techniques are used to keep the most instructive and discriminative features from a collection of emails, consists of both phishing and legitimate, for better detection. Two feature selection techniques -Chi-Square and Information Gain Ratio and two feature extraction techniques -Principal Component Analysis and Latent Semantic Analysis are used for the analysis. It is found that feature extraction techniques offer better performance for the classification, give stable classification results with the different number of features chosen, and robustly keep the performance over time.

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“…This Twitter scam is in full swing, using tempting messages like ''Just saw this photo of you'' followed by a link that, when you click it, takes you to a site that uploads malware (Villeneuve, 2010) onto your computer. Sometimes, by exploiting the phishing techniques (Nishanth, Ravi, Ankaiah, & Bose, 2012;Ramanathan & Wechsler, 2012), the message may seem to come from one of your regular followers, perhaps even a friend or relative. In fact, their Twitter account has been hijacked.…”

Section: Introductionmentioning

confidence: 99%

Detecting malicious tweets in trending topics using a statistical analysis of language

Martínez-Romo

Araujo

2013

Expert Systems with Applications

166

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a b s t r a c tTwitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.

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phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Cited by 59 publications

References 35 publications

PhishStorm: Detecting Phishing With Streaming Analytics

PhishStorm: Detecting Phishing With Streaming Analytics

Feature Extraction or Feature Selection for Text Classification: A Case Study on Phishing Email Detection

Detecting malicious tweets in trending topics using a statistical analysis of language

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