Abstract
Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.
Highlights
Detection is an important paradigm in information security, especially in technically challenging platforms like multimedia or online social networks (OSNs)
We used MATLAB 2019b to detect anomalous behavior according to the number of messages between users. e attributes selected from Facebook API for policy enforcement are shown in Figure 3. ese attributes are mostly connected to the main entities in each user account, such as the social network of other related users, attributes related to the messaging feature, and what the user can do with these messages
One of our objectives in this scenario is to differentiate between innocent users who are facing hard times and spammers or feature abusers. e secondary objective is to make use of most of the attributes saved in the OSN and allocate the minimum amount of space for attributebased access control (ABAC) attributes
Summary
Detection is an important paradigm in information security, especially in technically challenging platforms like multimedia or online social networks (OSNs). To detect anomalous users in social networks, proposed models identify a pattern of what is considered normal behavior, by examining a series of events either from each individual user or from a collected set of users [1, 2]. Intrusion detection system (IDS) is used to identify deviations or anomalies in entities’ behavior. E detection system identifies threats such as fraud, disturbance, network intrusion, information leakage, and privacy violation [2]. Among IDSs, machine learning techniques showed promising results in detecting users with high accuracy and low false rates. Supervised machine learning approaches the detection problem from a statistical point of view [3]. Bayesian classifiers detect any change in communication patterns between users and treat it as a counting process following time increments [4], while unsupervised deep neural networks extract features from the system logs, calculate the probabilities for these feature vectors to make future predictions, and detect abnormalities [5]
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