Deceptive phishing detection system: From audio and text messages in Instant Messengers using Data Mining approach

<strong>Abstract</strong>— The widespread use of smart phones nowadays makes them vulnerable to phishing. Phishing is the process of trying to steal user information over the Internet by claiming they are a trusted entity and thus access and steal the victim's data(user name, password and credit card details). Consequently, the need for mobile phishing detection system has become an urgent need. And this is what we are attempting to introduce in this paper, where we introduce a system to detect phishing websites on Android phones. That predicts and prevents phishing websites from deceiving users, utilizing data mining techniques to predict whether a website is phishing or not, relying on a set of factors (URL based features, HTML based features and Domain based features). The results shows system effectiveness in predicting phishing websites with 97% as prediction accuracy.

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URL Based Analysis for Phishing Detection Using Data Mining

SSRN Electronic Journal ◽

10.2139/ssrn.3267436 ◽

2018 ◽

Author(s):

Nikita Gawade ◽

Sayali Mundekar ◽

Nilam Vare ◽

Ruchi Gada ◽

Smita Bansod

Keyword(s):

Data Mining ◽

Using Data ◽

Phishing Detection

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Temporal Analysis for Epileptic Seizure Detection by Using Data Mining Approach

2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) ◽

10.1109/hpcc-smartcity-dss50907.2020.00175 ◽

2020 ◽

Author(s):

Francesca Pafferi ◽

Gaetano Zazzaro ◽

Angelo Martone ◽

Paolo Bifulco ◽

Luigi Pavone

Keyword(s):

Data Mining ◽

Epileptic Seizure ◽

Temporal Analysis ◽

Seizure Detection ◽

Epileptic Seizure Detection ◽

Data Mining Approach ◽

Using Data

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Predicting car purchase intent using data mining approach

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2011.6019863 ◽

2011 ◽

Cited By ~ 1

Author(s):

Yap Bee Wah ◽

Nor Huwaina Ismail ◽

Simon Fong

Keyword(s):

Data Mining ◽

Purchase Intent ◽

Data Mining Approach ◽

Using Data

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A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

Journal of Computing and Information Science in Engineering ◽

10.1115/1.1812556 ◽

2004 ◽

Vol 4 (4) ◽

pp. 316-328 ◽

Cited By ~ 34

Author(s):

Carol J. Romanowski , ◽

Rakesh Nagi

Keyword(s):

Data Mining ◽

Product Family ◽

Research Direction ◽

Design Constraints ◽

Variant Design ◽

Data Mining Approach ◽

Design Activities ◽

Using Data ◽

New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

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Hybrid classification model to detect advanced intrusions using data mining techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.4.10031 ◽

2018 ◽

Vol 7 (2.4) ◽

pp. 10

Author(s):

V Mala ◽

K Meena

Keyword(s):

Data Mining ◽

Learning Algorithm ◽

Detection System ◽

Classification Model ◽

Detection Methods ◽

Data Mining Techniques ◽

Detection Systems ◽

Intruder Detection ◽

Hybrid Classification ◽

Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

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