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  • 1. Bahri, Leila
    et al.
    Soliman, Amira
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Squillaci, Jacopo
    Carminati, Barbara
    Ferrari, Elena
    Girdzijauskas, Sarunas
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Beat the DIVa: Decentralized Identity Validation for Online Social Networks2016In: 2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2016, p. 1330-1333Conference paper (Refereed)
    Abstract [en]

    Fake accounts in online social networks (OSNs) have known considerable sophistication and are now attempting to gain network trust by infiltrating within honest communities. Honest users have limited perspective on the truthfulness of new online identities requesting their friendship. This facilitates the task of fake accounts in deceiving honest users to befriend them. To address this, we have proposed a model that learns hidden correlations between profile attributes within OSN communities, and exploits them to assist users in estimating the trustworthiness of new profiles. To demonstrate our method, we suggest, in this demo, a game application through which players try to cheat the system and convince nodes in a simulated OSN to befriend them. The game deploys different strategies to challenge the players and to reach the objectives of the demo. These objectives are to make participants aware of how fake accounts can infiltrate within their OSN communities, to demonstrate how our suggested method could aid in mitigating this threat, and to eventually strengthen our model based on the data collected from the moves of the players.

  • 2.
    Kefato, Zekarias
    et al.
    Trento University.
    Sheikh, Nasrullah
    Trento University.
    Bahri, Leila
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Montresor, Alberto
    Trento University.
    CaTS: Network-Agnostic Virality Prediction Model to Aid Rumour Detection2018Conference paper (Refereed)
  • 3.
    Kefato, Zekarias T.
    et al.
    Univ Trento, Trento, Italy..
    Sheikh, Nasrullah
    Univ Trento, Trento, Italy..
    Bahri, Leila
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Montresor, Alberto
    Univ Trento, Trento, Italy..
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks2018Conference paper (Refereed)
    Abstract [en]

    Effectively predicting whether a given post or tweet is going to become viral in online social networks is of paramount importance for several applications, such as trend and break-out forecasting. While several attempts towards this end exist, most of the current approaches rely on features extracted from the underlying network structure over which the content spreads. Recent studies have shown, however, that prediction can be effectively performed with very little structural information about the network, or even with no structural information at all. In this study we propose a novel network-agnostic approach called CAS2VEC, that models information cascades as time series and discretizes them using time slices. For the actual prediction task we have adopted a technique from the natural language processing community. The particular choice of the technique is mainly inspired by an empirical observation on the strong similarity between the distribution of discretized values occurrence in cascades and words occurrence in natural language documents. Thus, thanks to such a technique for sentence classification using convolutional neural networks, CAS2VEC can predict whether a cascade is going to become viral or not. We have performed extensive experiments on two widely used real-world datasets for cascade prediction, that demonstrate the effectiveness of our algorithm against strong baselines.

  • 4.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Graph-based Analytics for Decentralized Online Social Networks2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Decentralized Online Social Networks (DOSNs) have been introduced as a privacy preserving alternative to the existing online social networks.  DOSNs remove the dependency on a centralized provider and operate as distributed information management platforms. Current efforts of providing DOSNs are mainly focused on designing the required building blocks for managing the distributed network and supporting the social services (e.g., search, content delivery, etc.). However, there is a lack of reliable techniques for enabling complex analytical services (e.g., spam detection, identity validation, etc.) that comply with the decentralization requirements of DOSNs. In particular, there is a need for decentralized data analytic techniques and machine learning (ML) algorithms that can successfully run on top of DOSNs.

     

    In this thesis, we empower decentralized analytics for DOSNs through a set of novel algorithms. Our algorithms allow decentralized analytics to effectively work on top of fully decentralized topology, when the data is fully distributed and nodes have access to their local knowledge only. Furthermore, our algorithms and methods are able to extract and exploit the latent patterns in the social user interaction networks and effectively combine them with the shared content, yielding significant improvements for the complex analytic tasks. We argue that, community identification is at the core of the learning and analytical services provided for DOSNs. We show in this thesis that knowledge on community structures and information dissemination patterns, embedded in the topology of social networks has a potential to greatly enhance data analytic insights and improve results. At the heart of this thesis lies a community detection technique that successfully extracts communities in a completely decentralized manner. In particular, we show that multiple complex analytic tasks, like spam detection and identity validation,  can be successfully tackled by harvesting the information from the social network structure. This is achieved by using decentralized community detection algorithm which acts as the main building block for the community-aware learning paradigm that we lay out in this thesis. To the best of our knowledge, this thesis represents the first attempt to bring complex analytical services, which require decentralized iterative computation over distributed data, to the domain of DOSNs. The experimental evaluation of our proposed algorithms using real-world datasets confirms the ability of our solutions to generate  efficient ML models in massively parallel and highly scalable manner.

  • 5.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Stad: Stateful Diffusion for Linear Time Community DetectionManuscript (preprint) (Other academic)
    Abstract [en]

    Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to "one size fits all" model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions.

    In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping "one size fits all" model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

  • 6.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Bahri, Leila
    Insubria University, Italy.
    Carminati, Barbara
    Insubria University, Italy.
    Ferrari, Elena
    Insubria University, Italy.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering (EES), Communication Networks.
    DIVa: Decentralized Identity Validation for Social Networks2015Report (Other academic)
    Abstract [en]

    We suggested DIVa, a decentralized, unsupervised, and association rule mining based solution for the learning of fine-grained correlations between profile attributes in Online Social Networks. These correlations can be used for identity validation purposes. In this report, we provide the technical details and the security analysis proofs of the DIVa model.

  • 7.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Bahri, Leila
    Carminati, Barbara
    Ferrari, Elena
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering (EES), Communication Networks.
    DIVa: Decentralized Identity Validation for Social Networks2015In: PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), Association for Computing Machinery (ACM), 2015, p. 383-391Conference paper (Refereed)
    Abstract [en]

    Online Social Networks exploit a lightweight process to identify their users so as to facilitate their fast adoption. However, such convenience comes at the price of making legitimate users subject to different threats created by fake accounts. Therefore, there is a crucial need to empower users with tools helping them in assigning a level of trust to whomever they interact with. To cope with this issue, in this paper we introduce a novel model, DIVa, that leverages on mining techniques to find correlations among user profile attributes. These correlations are discovered not from user population as a whole, but from individual communities, where the correlations are more pronounced. DIVa exploits a decentralized learning approach and ensures privacy preservation as each node in the OSN independently processes its local data and is required to know only its direct neighbors. Extensive experiments using real-world OSN datasets show that DIVa is able to extract fine-grained community-aware correlations among profile attributes with average improvements up to 50% than the global approach.

  • 8.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Bahri, Leila
    Girdzijauskas, Šarunas
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Carminati, Barbara
    Ferrari, Elena
    CADIVa: Cooperative and Adaptive Decentralized Identity Validation Model for Social Networks2016In: Social Network Analysis and Mining, ISSN 1869-5450, E-ISSN 1869-5469, Vol. 6, no 1, article id UNSP 36Article in journal (Refereed)
    Abstract [en]

    Online social networks (OSNs) have successfully changed the way people interact. Online interactions among people span geographical boundaries and interweave with different human life activities. However, current OSNs identification schemes lack guarantees on quantifying the trustworthiness of online identities of users joining them. Therefore, driven from the need to empower users with an identity validation scheme, we introduce a novel model, cooperative and adaptive decentralized identity validation CADIVa, that allows OSN users to assign trust levels to whomever they interact with. CADIVa exploits association rule mining approach to extract the identity correlations among profile attributes in every individual community in a social network. CADIVa is a fully decentralized and adaptive model that exploits fully decentralized learning and cooperative approaches not only to preserve users privacy, but also to increase the system reliability and to make it resilient to mono-failure. CADIVa follows the ensemble learning paradigm to preserve users privacy and employs gossip protocols to achieve efficient and low-overhead communication. We provide two different implementation scenarios of CADIVa. Results confirm CADIVa's ability to provide fine-grained community-aware identity validation with average improvement up to 36 and 50 % compared to the semi-centralized or global approaches, respectively.

  • 9.
    Soliman, Amira
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Adagraph: Adaptive graph-based algorithms for spam detection in social networks2017In: 5th International Conference on Networked Systems, NETYS 2017, Springer Verlag , 2017, p. 338-354Conference paper (Refereed)
    Abstract [en]

    In the past years, researchers developed approaches to detect spam in Online Social Networks (OSNs) such as URL blacklisting, spam traps and even crowdsourcing for manual classification. Although previous work has shown the effectiveness of using statistical learning to detect spam, existing work employs supervised schemes that require labeled training data. In addition to the heavy training cost, it is difficult to obtain a comprehensive source of ground truth for measurement. In contrast to existing work, in this paper we present AdaGraph that is a novel graph-based approach for spam detection. AdaGraph is unsupervised, hence it diminishes the need of labeled training data and training cost. Particularly, AdaGraph effectively detects spam in large-scale OSNs by analyzing user behaviors using graph clustering technique. Moreover, AdaGraph continuously updates detected communities to comply with users dynamic interactions and activities. Extensive experiments using Twitter datasets show that AdaGraph detects spam with accuracy 92.3%. Furthermore, the false positive rate of AdaGraph is less than 0.3% that is less than half of the rate achieved by the state-of-the-art approaches.

  • 10.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Girdzijauskas, Sarunas
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Adaptive Graph-based algorithms for Spam Detection in Social Networks2016Report (Other academic)
    Abstract [en]

    As Online Social Networks (OSNs) continue to grow in popularity, a spam marketplace has emerged that includes services selling fraudulent accounts, as well as acts as nucleus of spammers who propagate large-scale spam campaigns. In the past years, researchers developed approaches to detect spam such as URL blacklisting, spam traps and even crowdsourcing for manual classification. Although previous work has shown the effectiveness of using statistical learning to detect spam, existing work employs supervised schemes that require labeled training data. In addition to the heavy training cost, it is difficult to obtain a comprehensive source of ground truth for measurement. In contrast to existing work, in this paper we present a novel graph-based approach for spam detection. Our approach is unsupervised, hence it diminishes the need of labeled training data and training cost. Particularly, our approach can effectively detect the spam in large-scale OSNs by analyzing user behaviors using graph clustering technique. Moreover, our approach continuously updates detected communities to comply with dynamic OSNs where interactions and activities are evolving rapidly. Extensive experiments using Twitter datasets show that our approach is able to detect spam with accuracy 92.3\%. Furthermore, our approach has false positive rate that is less than 0.3\% that is less than half of the rate achieved by the state-of-the-art approaches.

  • 11.
    Soliman, Amira
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    DLSAS: Distributed Large-Scale Anti-Spam Framework for Decentralized Online Social Networks2016In: 2016 IEEE 2ND INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (IEEE CIC), IEEE Press, 2016, p. 363-372Conference paper (Refereed)
    Abstract [en]

    In the last decade, researchers and the open source community have proposed various Decentralized Online Social Networks (DOSNs) that remove dependency on centralized online social network providers to preserve user privacy. However, transitioning from centralized to decentralized environment creates various new set of problems, such as adversarial manipulations. In this paper, we present DLSAS, a novel unsupervised and decentralized anti-spam framework for DOSNs. DLSAS provides decentralized spam detection that is resilient to adversarial attacks. DLSAS typifies massively parallel frameworks and exploits fully decentralized learning and cooperative approaches. Furthermore, DLSAS provides a novel defense mechanism for DOSNs to prevent malicious nodes participating in the system by creating a validation overlay to assess the credibility of the exchanged information among the participating nodes and exclude the misbehaving nodes from the system. Extensive experiments using Twitter datasets confirm not only the DLSAS's capability to detect spam with higher accuracy compared to state-of-the-art approaches, but also the DLSAS's robustness against different adversarial attacks.

  • 12.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Rahimian, Fatemeh
    RISE SICS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Stad: Stateful Diffusion for Linear Time Community Detection2018In: 38th IEEE International Conference on Distributed Computing Systems, 2018Conference paper (Refereed)
    Abstract [en]

    Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to “one size fits all” model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions. In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with a conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping “one size fits all” model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

  • 13.
    Yanggratoke, Rerngvit
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Networks.
    El Hosary, Amira
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Networks.
    Selected Paper on Network and Cloud Analytics2016Report (Other academic)
    Abstract [en]

    This report reviews selected papers from the ones presented in the seminar on Network and Cloud Analytics since April 2015 till November 2015 (realm.sics.se). During this time period, the seminar discussed 43 papers that span different research disciplines, such as analytics for network management, network anomaly detection, large-scale machine learning, and learning under concept drift. From those papers, we select 13 papers that provide compelling contributions and possible extensions for future work. Additionally, we grouped the selected papers based on their problem area. For each selected paper, we identify the problem that the authors try to solve, major challenges that make the problem difficult, a proposed approach to solve the problem, and key contributions of the paper. Further, we show the limitations of the proposed method and suggest ideas for applying the paper to our research projects.

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