A Real and Accurate Fake Product Detection System and Generate Original Reviews Using Data Mining Mechanism

2020 ◽  
Vol 17 (12) ◽  
pp. 5464-5468
Author(s):  
Ch. V. Bhargavi ◽  
G. Mani ◽  
G. Jyothi ◽  
K. Venkat Rao ◽  
E. Laxmi Lydia
Keyword(s):  
Data Mining ◽  
Social Organization ◽  
Detection System ◽  
Ip Address ◽  
Svm Classification ◽  
The People ◽  
Using Data ◽  
Fake Reviews ◽  
Technical People ◽  
Good Rating

Most of the people requires genuine information about the online product. Before spending their economy on particular product can analyze the various reviews in the website. In this scenario, they did not identify whether the product may be fake or genuine. In general, some reports in the websites are good, company technical people itself add these for making the product famous. These people belong to media and social organization teams, they give reviews with a good rating by their own firm. Online purchasers did not identify the fake product because of this falsification in the reviews of the website. In this research,the SVM classification mechanism has been used for detect the fake reviews by using IP address. This implementation helpful for users find out the correct review of online product. In this accuracy is improved by 98.79%, F1-Score increases by 10%.

scholarly journals Online Fraud Review Detection Using Data Mining

2020 ◽  
pp. 326-330
Keyword(s):  
Detection System ◽  
Online Reviews ◽  
Automated Detection ◽  
Product Reviews ◽  
Online Fraud ◽  
The People ◽  
Or Groups ◽  
Opinion Spam ◽  
Using Data ◽  
Fake Reviews

Online reviews have great impact on today’s business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Nowadays, there are a number of people using social media opinions to create their call on shopping for product or service. Opinion Spam detection is an exhausting and hard problem as there are many faux or fake reviews that have been created by organizations or by the people for various purposes. They write fake reviews to mislead readers or automated detection system by promoting or demoting target products to promote them or to degrade their reputations, opportunistic individuals or groups try to manipulate product reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

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.

scholarly journals Static Malware Detection System Using Data Mining Methods

2013 ◽  
Vol 4 (4) ◽  
pp. 113-126 ◽  
Author(s):  
Usukhbayar Baldangombo ◽  
Nyamjav Jambaljav ◽  
Shi-Jinn Horng
Keyword(s):  
Data Mining ◽  
Detection System ◽  
Malware Detection ◽  
Mining Methods ◽  
Using Data

scholarly journals Review on Malware and Malware Detection ‎Using Data Mining Techniques

2017 ◽  
Vol 25 (5) ◽  
pp. 1585-1601
Author(s):  
Wesam S Bhaya ◽  
Mustafa A Ali
Keyword(s):  
Data Mining ◽  
Computer Security ◽  
Computer System ◽  
Private Information ◽  
Detection System ◽  
Denial Of Service ◽  
Malware Detection ◽  
Malicious Software ◽  
Attack Data ◽  
Using Data

Malicious software is any type of software or codes which hooks some: private information, data from the computer system, computer operations or(and) merely just to do malicious goals of the author on the computer system, without permission of the computer users. (The short abbreviation of malicious software is Malware). However, the detection of malware has become one of biggest issues in the computer security field because of the current communication infrastructures are vulnerable to penetration from many types of malware infection strategies and attacks.  Moreover, malwares are variant and diverse in volume and types and that strictly explode the effectiveness of traditional defense methods like signature approach, which is unable to detect a new malware. However, this vulnerability will lead to a successful computer system penetration (and attack) as well as success of more advanced attacks like distributed denial of service (DDoS) attack. Data mining methods can be used to overcome limitation of signature-based techniques to detect the zero-day malware. This paper provides an overview of malware and malware detection system using modern techniques such as techniques of data mining approach to detect known and unknown malware samples.

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