Using data mining techniques to explore security issues in smart living environments in Twitter

Social media is a popular medium for the dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender, and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disadvantage comes in the form of fake news, as people usually read and share information without caring about its genuineness. Therefore, it is imperative to research methods for the authentication of news. To address this issue, this article proposes a two-phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine learning classification. The first phase preprocesses the data set and validates the veracity of news content by using linguistic features. The second phase merges the linguistic feature sets with WE and applies voting classification. To validate its approach, this article also carefully designs a novel WELFake data set with approximately 72 000 articles, which incorporates different data sets to generate an unbiased classification output. Experimental results show that the WELFake model categorizes the news in real and fake with a 96.73% which improves the overall accuracy by 1.31% compared to bidirectional encoder representations from transformer (BERT) and 4.25% compared to convolutional neural network (CNN) models. Our frequency-based and focused analyzing writing patterns model outperforms predictive-based related works implemented using the Word2vec WE method by up to 1.73%.

WELFake: Word Embedding Over Linguistic Features for Fake News Detection

In recent years, the use of mobile and cloud Computing is rapidly growing. In other word, mobile and cloud Computing play an important role in everybody’s life today. It carries rich computational resources to mobile users, network operators, as well as cloud computing providers. Moreover, the explosion of multimedia big data (image, video, 3D, etc.) in mobile and cloud computing have created unprecedented opportunities and fundamental security and privacy challenges as they are not just big in volume, but also unstructured and multi-modal. Therefore, the papers of this special issue address variety of security and privacy issues of multimedia big data in mobile and cloud computing and security of other aspects of mobile and cloud networks and also emphasizes many open questions. We anticipate that papers of this special issue will open new entrance for further research and technology improvements in this important area.

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Advances in Security and Privacy of Multimedia Big Data in Mobile and Cloud Computing

Hybrid approach for detection of malicious profiles in twitter

With the significant increase in the volume, velocity, and variety of user data (e.g., user-generated data) in online social networks, there have been attempted to design new ways of collecting and analyzing such big data. For example, social bots have been used to perform automated analytical services and provide users with improved quality of service. However, malicious social bots have also been used to disseminate false information (e.g., fake news), and this can result in real-world consequences. Therefore, detecting and removing malicious social bots in online social networks is crucial. The most existing detection methods of malicious social bots analyze the quantitative features of their behavior. These features are easily imitated by social bots; thereby resulting in low accuracy of the analysis. A novel method of detecting malicious social bots, including both features selection based on the transition probability of clickstream sequences and semi-supervised clustering, is presented in this paper. This method not only analyzes transition probability of user behavior clickstreams but also considers the time feature of behavior. Findings from our experiments on real online social network platforms demonstrate that the detection accuracy for different types of malicious social bots by the detection method of malicious social bots based on transition probability of user behavior clickstreams increases by an average of 12.8%, in comparison to the detection method based on quantitative analysis of user behavior.

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Detecting Malicious Social Bots Based on Clickstream Sequences

How to comprehensively explore, improve and deploy multimedia social networks (MSNs) has become a hot topic in the era of emerging pervasive mobile multimedia. More and more MSNs offer a great number of convenient tools, services, and applications for multimedia contents including video and audio that users share willingly and on demand. However, concerns with digital rights management (DRM)-oriented multimedia security, as well as the efficiency of multimedia usage and sharing are meanwhile intensified due to easier distribution and reproduction of multimedia content in a wide range of social networks. The paper proposes a comprehensive framework for multimedia social network, and realized a cross-platform MSN prototype system, named as CyVOD, to support two kinds of DRM modes. The proposed framework effectively protects copyrighted multimedia contents against piracy, and supports a more efficient recommendation system for its better handling of the tradeoff between multimedia security and ease of use.

CyVOD: a novel trinity multimedia social network scheme

The past decade has witnessed the emergence and progress of multimedia social networks (MSNs), which have explosively and tremendously increased to penetrate every corner of our lives, leisure and work. Moreover, mobile Internet and mobile terminals enable users to access to MSNs at anytime, anywhere, on behalf of any identity, including role and group. Therefore, the interaction behaviors between users and MSNs are becoming more comprehensive and complicated. This paper primarily extended and enriched the situation analytics framework for the specific social domain, named as SocialSitu , and further proposed a novel algorithm for users’ intention serialization analysis based on classic Generalized Sequential Pattern (GSP). We leveraged the huge volume of user behaviors records to explore the frequent sequence mode that is necessary to predict user intention. Our experiment selected two general kinds of intentions: playing and sharing of multimedia, which are the most common in MSNs, based on the intention serialization algorithm under different minimum support threshold ( Min_Support ). By using the users’ microscopic behaviors analysis on intentions, we found that the optimal behavior patterns of each user under the Min_Support , and a user's behavior patterns are different due to his/her identity variations in a large volume of sessions data.

A Situational Analytic Method for User Behavior Pattern in Multimedia Social Networks

A novel crowd evaluation method for security and trustworthiness of online social networks platforms based on signaling theory

Due to the increasing popularity of multimedia social networks (MSNs), the ability to mine users’ interests in different contexts on such networks is crucial in recommendation systems. It is, however, challenging to mine users’ current preferences based on session on MSNs. In this study, we propose a novel recommendation algorithm based on both social situation analytics and collaborative filtering for session-based recommendation. Specifically, the algorithm predicts the rating for target users based on their nearest neighbors and historical behaviors. First, for the purpose of mining users’ current intentions, the session-level behavioral sequences of target user are analyzed based on SocialSitu (t) . Then, the recommended contents are generated for target users based on their behaviors and perceived session-based intentions and identity. We evaluate the performance of the proposed algorithm using real-world social media dataset from Shareteches. Findings demonstrate that our algorithm outperforms two classical algorithms and a state-of-the-art method.

A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

This paper proposes a novel hash function construction based on cascade chaos Firstly, the L-length original message is divided into a number of blocks according to every character, which are transformed into corresponding ASCII values Then the ASCII values are normalized into the interval of (0, 1), which compose a big array with the L-length Regarding the first normalized ASCII value with the algorithm parameter H as the initial input of the cascade chaos, a series of sequences are obtained, where the CBC mode is adopted The 128-bit hash value is generated by transforming the sequences into binary sequence according to a rule Simulation results and performance analysis show that the proposed hash function has high sensitivity to the original message and system parameters, strong confusion and diffusion, excellent statistical properties, strong collision resistance and flexible to generated 160, 256, 512-bit hash value after modifying the algorithm slightly

Changwei Zhao

Papers

CyVOD: a novel trinity multimedia social network scheme

A Situational Analytic Method for User Behavior Pattern in Multimedia Social Networks

A novel crowd evaluation method for security and trustworthiness of online social networks platforms based on signaling theory

A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

One-Way Hash Function based on Cascade Chaos