Security techniques for intelligent spam sensing and anomaly detection in online social platforms

Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen

Security techniques for intelligent spam sensing and anomaly detection in online social platforms

Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen

Twitter gender classification using user unstructured information

This paper describes an approach to automatically detect the gender of Twitter users, based only on clues provided by their profile information in an unstructured form. A number of features that capture phenomena specific of Twitter users is proposed and evaluated on a dataset of about 242K English language users. Different supervised and unsupervised approaches are used to assess the performance of the proposed features, including Naive Bayes variants, Logistic Regression, Support Vector Machines, Fuzzy c-Means clustering, and K-means. An unsupervised approach based on Fuzzy c-Means proved to be very suitable for this task, returning the correct gender for about 96% of the users.info:eu-repo/semantics/acceptedVersio

SDSF : social-networking trust based distributed data storage and co-operative information fusion.

As of 2014, about 2.5 quintillion bytes of data are created each day, and 90% of the data in the world was created in the last two years alone. The storage of this data can be on external hard drives, on unused space in peer-to-peer (P2P) networks or using the more currently popular approach of storing in the Cloud. When the users store their data in the Cloud, the entire data is exposed to the administrators of the services who can view and possibly misuse the data. With the growing popularity and usage of Cloud storage services like Google Drive, Dropbox etc., the concerns of privacy and security are increasing. Searching for content or documents, from this distributed stored data, given the rate of data generation, is a big challenge. Information fusion is used to extract information based on the query of the user, and combine the data and learn useful information. This problem is challenging if the data sources are distributed and heterogeneous in nature where the trustworthiness of the documents may be varied. This thesis proposes two innovative solutions to resolve both of these problems. Firstly, to remedy the situation of security and privacy of stored data, we propose an innovative Social-based Distributed Data Storage and Trust based co-operative Information Fusion Framework (SDSF). The main objective is to create a framework that assists in providing a secure storage system while not overloading a single system using a P2P like approach. This framework allows the users to share storage resources among friends and acquaintances without compromising the security or privacy and enjoying all the benefits that the Cloud storage offers. The system fragments the data and encodes it to securely store it on the unused storage capacity of the data owner\u27s friends\u27 resources. The system thus gives a centralized control to the user over the selection of peers to store the data. Secondly, to retrieve the stored distributed data, the proposed system performs the fusion also from distributed sources. The technique uses several algorithms to ensure the correctness of the query that is used to retrieve and combine the data to improve the information fusion accuracy and efficiency for combining the heterogeneous, distributed and massive data on the Cloud for time critical operations. We demonstrate that the retrieved documents are genuine when the trust scores are also used while retrieving the data sources. The thesis makes several research contributions. First, we implement Social Storage using erasure coding. Erasure coding fragments the data, encodes it, and through introduction of redundancy resolves issues resulting from devices failures. Second, we exploit the inherent concept of trust that is embedded in social networks to determine the nodes and build a secure net-work where the fragmented data should be stored since the social network consists of a network of friends, family and acquaintances. The trust between the friends, and availability of the devices allows the user to make an informed choice about where the information should be stored using `k\u27 optimal paths. Thirdly, for the purpose of retrieval of this distributed stored data, we propose information fusion on distributed data using a combination of Enhanced N-grams (to ensure correctness of the query), Semantic Machine Learning (to extract the documents based on the context and not just bag of words and also considering the trust score) and Map Reduce (NSM) Algorithms. Lastly we evaluate the performance of distributed storage of SDSF using era- sure coding and identify the social storage providers based on trust and evaluate their trustworthiness. We also evaluate the performance of our information fusion algorithms in distributed storage systems. Thus, the system using SDSF framework, implements the beneficial features of P2P networks and Cloud storage while avoiding the pitfalls of these systems. The multi-layered encrypting ensures that all other users, including the system administrators cannot decode the stored data. The application of NSM algorithm improves the effectiveness of fusion since large number of genuine documents are retrieved for fusion

A Machine Learning Technique for Detection of Social Media Fake News

publishedVersio

Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms

Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target\u27s resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets. To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target

AN APPROACH TO SENTIMENT ANALYSIS –THE CASE OF AIRLINE QUALITY RATING

Sentiment mining has been commonly associated with the analysis of a text string to determine whether a corpus is of a negative or positive opinion. Recently, sentiment mining has been extended to address problems such as distinguishing objective from subjective propositions, and determining the sources and topics of different opinions expressed in textual data sets such as web blogs, tweets, message board reviews, and news. Companies can leverage opinion polarity and sentiment topic recognition to gain a deeper understanding of the drivers and the overall scope of sentiments. These insights can advance competitive intelligence, improve customer service, attain better brand image, and enhance competitiveness. This research paper proposes a sentiment mining approach which detects sentiment polarity and sentiment topic from text. The approach includes a sentiment topic recognition model that is based on Correlated Topics Models (CTM) with Variational Expectation-Maximization (VEM) algorithm. We validate the effectiveness and efficiency of this model using airline data from Twitter. We also examine the reputation of three major airlines by computing their Airline Quality Rating (AQR) based on the output from our approach