A Model of Illegal Access Detection System under Cloud Environment

2014 ◽  
Vol 651-653 ◽  
pp. 1761-1766
Author(s):  
Lei Zheng
Keyword(s):  
Collaborative Filtering ◽  
Detection Method ◽  
Detection System ◽  
Cloud Model ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
Filtering Method ◽  
Normal Behavior ◽  
Collaborative Filtering Algorithm ◽  
Similarity Method

In this paper detection method for the illegal access to the cloud infrastructure is proposed. Detection process is based on the collaborative filtering algorithm constructed on the cloud model. Here, first of all, the normal behavior of the user is formed in the shape of a cloud model, then these models are compared with each other by using the cosine similarity method and by applying the collaborative filtering method the deviations from the normal behavior are evaluated. If the deviation value is above than the threshold, the user who gained access to the system is evaluated as illegal, otherwise he is evaluated as a real user.

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

Study on Wheel Run-Out Detection System Based on Harmonic-Analysis Principle

2015 ◽  
Vol 738-739 ◽  
pp. 771-774
Author(s):  
Yu Zhuo Men ◽  
Xiao Dong Yang ◽  
Jin Gang Gao ◽  
Lei Yu ◽  
Hai Bo Yu
Keyword(s):  
Harmonic Analysis ◽  
Detection Method ◽  
Detection System ◽  
Moving Average ◽  
Digital Filtering ◽  
Measured Data ◽  
Practical Application ◽  
Online Detection ◽  
Filtering Method ◽  
System A

In order to reduce error of the wheel run-out detection system, a harmonic-analysis-based detection method was proposed to enhance the precision of online detection. The moving average filtering method was used for digital filtering between the axial and radial run-out errors so that to decrease the effect of outside noise on the measured data. Practical application shows that this system works stably and reliably on the wheel detection line and it realizes 100% online detection on the axial and radial run-out of work pieces, with the measurement error lower than 0.1mm.

scholarly journals Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

2019 ◽  
Vol 2 (3) ◽  
pp. 334
Author(s):  
Imam Fahrurrozi ◽  
Estu Muh Dwi Admoko ◽  
Anang Susilo
Keyword(s):  
Collaborative Filtering ◽  
Semantic Similarity ◽  
Recommender System ◽  
Cold Start ◽  
Performance Tests ◽  
Online Commerce ◽  
Filtering Method ◽  
Similarity Method

Recommender system is a component which has been developed for online commerce purposes. In this issue, one of the popular methods that has been widely used is collaborative filtering. However, this method has some drawbacks and needs to be improved. Therefore, in this research a combination of Collaborative Filtering (CF) and semantic similarity method has been compare with original CF, and the result expected reducing some deficiencies on the original collaborative filtering method. Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue.

scholarly journals Research of specific field ultra-short text classification based on collaborative filtering algorithm

2018 ◽  
Vol 189 ◽  
pp. 03006
Author(s):  
Weichen Yang ◽  
Yanwei Si
Keyword(s):  
Collaborative Filtering ◽  
Word Frequency ◽  
Text Classification ◽  
Chinese Word Segmentation ◽  
Short Text ◽  
Filtering Algorithm ◽  
Average Accuracy ◽  
Collaborative Filtering Algorithm ◽  
Text Content ◽  
Similarity Method

In some specific fields, there are a lot of ultra-short texts that need to be categorized. This paper proposes an ultra-short text classification method based on collaborative filtering algorithm aiming at the problems such as short text content, short length, sparse features, and large number of categories in certain fields. First, converting ultra-short text into word frequency vector by doing Chinese word segmentation and calculating word frequency; Secondly, combining relevant data in specific fields, defining the ultra-short texts as users, categories as items, and then constructing a user-item recommendation matrix. Finally, calculating text similarity by using cosine similarity method and obtaining the classification results. The experimental results show that the proposed method can well solve the problem of classification of ultra-short texts in specific fields, and the average accuracy is 9.19% and 3.81% higher than vector space model and topic similarity method respectively.

The Improved Collaborative Filtering Recommendation Algorithm Based on Cloud Model

2013 ◽  
Vol 411-414 ◽  
pp. 2292-2296
Author(s):  
Jia Si Gu ◽  
Zheng Liu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cloud Model ◽  
Personalized Recommendation ◽  
Test Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Model Based ◽  
Collaborative Filtering Algorithm

The traditional collaborative filtering algorithm has a better recommendation quality and efficiency, it has been the most widely used in personalized recommendation system. Based on the traditional collaborative filtering algorithm,this paper considers the user interest diversity and combination of cloud model theory.it presents an improved cloud model based on collaborative filtering recommendation algorithm.The test results show that, the algorithm has better recommendation results than other kinds of traditional recommendation algorithm.

A dynamic filtering obstacle detection method based on ultrasonic array

2016 ◽  
Vol 14 (04) ◽  
pp. 1650030
Author(s):  
Huihai Cui ◽  
Daxue Liu ◽  
Yan Li ◽  
Hangen He
Keyword(s):  
Detection Method ◽  
Detection System ◽  
Obstacle Detection ◽  
Filtering Method ◽  
Field Environment ◽  
Ultrasonic Array ◽  
Dynamic Filtering ◽  
Geometric Relationship ◽  
And Control ◽  
Relationship Of

In research on the navigation and control of an Autonomous Land Vehicle (ALV), the ultrasonic obstacle detection system plays an important role extending the environment cognition capability of the ALV. With a goal of improving the accuracy of ultrasonic obstacle detection, a dynamic data filtering method based on ultrasonic array is presented. The sonar return data is first processed through static filtering according to the geometric relationship of the ultrasonic array. Then, dynamic filtering is executed using the orientation and trajectory information of the vehicle. The dynamic filtering method is compared to the traditional ultrasonic obstacle detection method, which is the static filtering method in a typical field environment. The experiment result demonstrates the validity of the method for solving the fake data problem, and the accuracy of obstacle detection is improved.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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