Classification Model to Detect Malicious URL via Behaviour Analysis

Jayakanthan, N.; Ramani, Anita

doi:10.7753/ijcatr0603.1003

Cited by 7 publications

(7 citation statements)

References 16 publications

(13 reference statements)

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“…Fake Login form in a phishing page is a dangerous sign of loss money or sensitive information as listed in Table 1. 13,14,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36]…”

Section: Suspicious Url Forms or Patternsmentioning

confidence: 99%

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An anti‐phishing enterprise environ model using feed‐forward backpropagation and Levenberg‐Marquardt method

Sankhwar

Pandey

Khan

et al. 2020

Security and Privacy

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Phishing in an enterprise is serious issue rising in wide scale and complexity, as phishers use email phishing via obfuscated, malicious or phished URLs and continuously adapt or innovate their strategies to lure victims for identity theft for financial benefits. To gain victim's trust and confidence phishers have started using visceral factors and familiarity cues. Phishing is not always money centric; phisher defame the user's goodwill and character. Defamation in enterprise could be much more traumatic than being embarrassed at a social networking site. It is a challenging task to address this issue. It is evident through literature review that single phishing detection filter approaches are insufficient to detect phishing in enterprise environ. Therefore, a novel anti-phishing model for enterprise using artificial neural network is proposed. In addition, this model effectively identifies whether the phishing email is known phishing or unknown phishing to reduce the trust and familiarity-based email phishing enterprise environ. The feed-forward backpropagation and Levenberg-Marquart methods of ANN are adopted to enhance the URL classification process and with Fuzzy Inference System to get result with imprecise data of social features. The proposed model can accurately classify the known and unknown email phishing via URLs. K E Y W O R D S authentication, E-mail phishing, enterprise cyber threat, machine learning techniques, privacy models for communication systems communication security, security and privacy in mobile communication services, social engineering 1 INTRODUCTION In digital era, email phishing is a critical issue causing loss of finance during online transactions. At present, different anti-phishing approaches are being proposed to detect email phishing attacks. Despite the various developed anti-phishing approaches are profoundly incompetent to tackle the real-time hassles. In literature review, a group of researchers argued upon about URLs blacklisting, which are mostly used in industry or organizations but not well off to

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“…Fake Login form in a phishing page is a dangerous sign of loss money or sensitive information as listed in Table 1. 13,14,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36]…”

Section: Suspicious Url Forms or Patternsmentioning

confidence: 99%

“…Phishing sites have very less life‐time as get block listed. Fake Login form in a phishing page is a dangerous sign of loss money or sensitive information as listed in Table 1 13,14,16,23‐36 …”

Section: Url Feature Setmentioning

confidence: 99%

An anti‐phishing enterprise environ model using feed‐forward backpropagation and Levenberg‐Marquardt method

Sankhwar

Pandey

Khan

et al. 2020

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…Ralph Edem Agbefu [18] proposed domain information based blacklisting method, that compares the domain properties with a blacklist. This approach is capable to detect traditional attack, but not Solanki et al [15] proposed a decision tree based feature analysis method, to detect phishing attacks. The decision tree algorithm is used for classification.…”

Section: Related Workmentioning

confidence: 99%

Graph Based Classifier to Detect Malicious URL

Jayakanthan¹,

Ramani²

2017

IJMPERD

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“…For instance, attackers used URL to perform an attack on websites. Attackers insert a redirect code into a compromised URLs so that the user will be navigated automatically to malicious URLs [5][6][7]. This malicious URLs also redirect the user to download a malicious application such as botnet into a computer and cause attacker able to collect confidential information such as banking number and contact information [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Malicious URLs continuous to grow and there are 230,000 new malware samples per day [5]. According to Cybint News, the attackers launch their attack for every 39 seconds and have infected 64% of companies [10].…”

Section: Introductionmentioning

confidence: 99%

A malicious URLs detection system using optimization and machine learning classifiers

Lee

Heryanto

Razak

et al. 2020

IJEECS

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<span>The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy. The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.</span>

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Classification Model to Detect Malicious URL via Behaviour Analysis

Cited by 7 publications

References 16 publications

An anti‐phishing enterprise environ model using feed‐forward backpropagation and Levenberg‐Marquardt method

An anti‐phishing enterprise environ model using feed‐forward backpropagation and Levenberg‐Marquardt method

Graph Based Classifier to Detect Malicious URL

A malicious URLs detection system using optimization and machine learning classifiers

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