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Journal ArticleDOI

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

27 Dec 2017-Wireless Communications and Mobile Computing (Hindawi)-Vol. 2017, pp 1-9

TL;DR: The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.
Abstract: Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.
Topics: Email spam (81%)
Citations
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Journal ArticleDOI
Rami Mustafa A. Mohammad1Institutions (1)
TL;DR: An enhanced model is proposed for ensuring lifelong spam classification model and the overall performance of the suggested model is contrasted against various other stream mining classification techniques to prove the success of the proposed model as a lifelong spam emails classification method.
Abstract: Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

9 citations


Journal ArticleDOI
TL;DR: The proposed SentiFilter model is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics and is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.
Abstract: Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual’s needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users’ explicit feedback. Findings also indicate that sentiment of users’ comments does not reflect users’ perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users’ explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.

1 citations


Cites background from "CPSFS: A Credible Personalized Spam..."

  • ...Therefore, a trust value needs to be assigned and computed for each contact [3]....

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  • ...[3] classified spam emails into two categories: complete spam and semispam emails....

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  • ...Studies that involved users‟ perspectives in identifying spam content have used terms such as semi-spam [3] and grey spam [2]....

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Proceedings ArticleDOI
Nasreen M Shajideen1, V Bindu1Institutions (1)
11 Jul 2018
TL;DR: The ontology based spam filtering and conventional spam filtering are compared and the results show that the former is superior to the latter.
Abstract: Emails are inevitable in this modern era. It has become an effective tool in the communication field. Because of its easiness the number of users are increasing day-by-day. With the increased number of email users, the number of spam mails have also increased. Spam can cause great loss to users. Many spam filtering techniques have been introduced to distinguish between ham and spam. A mail that appear as spam may appear as ham to another user and vice versa. That is, it depends on the personal preference of user. Therefore an ontology based personalized mail access is necessary. This paper compares the ontology based spam filtering and conventional spam filtering.

1 citations


Cites methods from "CPSFS: A Credible Personalized Spam..."

  • ...Different approaches taken to classify emails are Bayesian approach Mail Header Checking, Signatures, Listing Approach [11], [12]....

    [...]


References
More filters

Journal ArticleDOI
Zhaolong Ning1, Feng Xia1, Noor Ullah1, Xiangjie Kong1  +1 moreInstitutions (2)
TL;DR: An application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs and several research challenges and open issues are highlighted and discussed.
Abstract: Vehicular transportation is an essential part of modern cities. However, the ever increasing number of road accidents, traffic congestion, and other such issues become obstacles for the realization of smart cities. As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSNs) are promising to solve the above-mentioned problems by enabling smart mobility in modern cities, which are likely to pave the way for sustainable development by promoting transportation efficiency. In this article, the definition of and a brief introduction to VSNs are presented first. Existing supporting communication technologies are then summarized. Furthermore, we introduce an application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs. Finally, several research challenges and open issues are highlighted and discussed.

247 citations


Journal ArticleDOI
Yafeng Ren1, Donghong Ji2Institutions (2)
TL;DR: This work empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam and shows that the proposed method outperforms state-of-the-art methods.
Abstract: The products reviews are increasingly used by individuals and organizations for purchase and business decisions. Driven by the desire of profit, spammers produce synthesized reviews to promote some products or demote competitors products. So deceptive opinion spam detection has attracted significant attention from both business and research communities in recent years. Existing approaches mainly focus on traditional discrete features, which are based on linguistic and psychological cues. However, these methods fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this work, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. First, the model learns sentence representation with convolutional neural network. Then, sentence representations are combined using a gated recurrent neural network, which can model discourse information and yield a document vector. Finally, the document representations are directly used as features to identify deceptive opinion spam. Based on three domains datasets, the results on in-domain and cross-domain experiments show that our proposed method outperforms state-of-the-art methods.

123 citations


"CPSFS: A Credible Personalized Spam..." refers background in this paper

  • ...The accuracy of spam detection of some of these filters can be fairly high [8]....

    [...]


Journal ArticleDOI
Zhaolong Ning1, Li Liu1, Feng Xia1, Behrouz Jedari1  +2 moreInstitutions (3)
TL;DR: This paper proposes a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding and demonstrates that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency.
Abstract: Socially aware networking (SAN) is a new communication paradigm, in which the social characteristics of mobile nodes are exploited to improve the performance of data distribution. In SAN, mobile carriers may exhibit selfish behaviors and refuse to relay messages for others for various reasons, such as limited resources (e.g., buffer, energy, and bandwidth) or social relationships. Several incentive schemes have recently been investigated to stimulate selfish users for cooperation in data forwarding. However, a majority of the existing methods have not fully studied nodes' social relationships in their selfish behaviors. In this paper, we propose a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding. In CAIS, we consider a network in which the nodes are divided into certain communities based on their social relationships. Then, we apply two types of credits, i.e., social credit and nonsocial credit, to reward the nodes when they relay data for other nodes inside their community or outsiders, respectively. Based on our mechanism, the number of messages a node can replicate to other nodes is adjusted according to its cooperation level and earned credits. To further improve the performance of CAIS, a single-copy data replication policy is employed, which manages the credit distribution of each node according to its available resources. The results of our extensive experiments using both synthetic and trace-driven simulations illustrate that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency.

95 citations


"CPSFS: A Credible Personalized Spam..." refers background in this paper

  • ...There is work on Copy Adjustable Incentive Scheme (CAIS) that adopts virtual credit concept to stimulate selfish nodes to cooperate in data forwarding [32]....

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Journal ArticleDOI
Zhaolong Ning1, Feng Xia1, Xiping Hu2, Zhikui Chen1  +1 moreInstitutions (3)
TL;DR: A social-oriented smartphone-based adaptive transmission mechanism to improve the network connectivity and throughput in Internet of Things (IoTs) for smart cities and a firefly-algorithm-based scheme is investigated, by which the formulated NP-complete problem can be solved effectively.
Abstract: Stable and reliable wireless communication is one of the critical demands for smart cities to connect people and devices. Although intelligent terminals can be leveraged to deliver and exchange data through Internet, poor network coverage and expensive network access challenge the deployment of network infrastructure. In this paper, we propose a social-oriented smartphone-based adaptive transmission mechanism to improve the network connectivity and throughput in Internet of Things (IoTs) for smart cities. First, a social-oriented double-auction-based relay selection scheme is investigated to stimulate the relay smartphones to forward packets for others so that the network connectivity can be strengthened. Furthermore, for the sake of achieving high throughput in smartphone-based IoTs, the relay method selection is determined by integrating various kinds of transmission schemes in an optimal fashion to make full use of wireless spectrum resource. Due to its high computational complexity, a firefly-algorithm-based scheme is investigated, by which the formulated NP-complete problem can be solved effectively. Simulation results demonstrate the superiority of our proposed method.

84 citations


"CPSFS: A Credible Personalized Spam..." refers background in this paper

  • ...In the future, we will also improve the performance by improving the network connectivity and throughput [33]....

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Journal ArticleDOI
Baojiang Cui1, Zheli Liu2, Lingyu Wang3Institutions (3)
TL;DR: This paper proposes the novel concept of key-aggregate searchable encryption and instantiates the concept through a concrete KASE scheme, in which a data owner only needs to distribute a single key to a user for sharing a large number of documents, and the user only need to submit a single trapdoor to the cloud for querying the shared documents.
Abstract: The capability of selectively sharing encrypted data with different users via public cloud storage may greatly ease security concerns over inadvertent data leaks in the cloud. A key challenge to designing such encryption schemes lies in the efficient management of encryption keys. The desired flexibility of sharing any group of selected documents with any group of users demands different encryption keys to be used for different documents. However, this also implies the necessity of securely distributing to users a large number of keys for both encryption and search, and those users will have to securely store the received keys, and submit an equally large number of keyword trapdoors to the cloud in order to perform search over the shared data. The implied need for secure communication, storage, and complexity clearly renders the approach impractical. In this paper, we address this practical problem, which is largely neglected in the literature, by proposing the novel concept of key-aggregate searchable encryption and instantiating the concept through a concrete KASE scheme, in which a data owner only needs to distribute a single key to a user for sharing a large number of documents, and the user only needs to submit a single trapdoor to the cloud for querying the shared documents. The security analysis and performance evaluation both confirm that our proposed schemes are provably secure and practically efficient.

109 citations


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