Spammer Detection and Fake User Identification on Social Networks

Spammer Detection and Fake User Identification on Social Networks

ABSTRACT:

Social networking sites engage millions of users around the world. The users’ interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

FAIZA MASOOD1, GHANA AMMAD1, AHMAD ALMOGREN 2, (Senior Member, IEEE), ASSAD ABBAS 1, HASAN ALI KHATTAK 1, (Senior Member, IEEE), IKRAM UD DIN 3, (Senior Member, IEEE), MOHSEN GUIZANI 4, (Fellow, IEEE), AND MANSOUR ZUAIR5, “Spammer Detection and Fake User Identification on Social Networks”, IEEE Access, 2019.

Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data

Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data

ABSTRACT:

Cloud computing provides individuals and enterprises massive computing power and scalable storage capacities to support a variety of big data applications in domains like health care and scientific research, therefore more and more data owners are involved to outsource their data on cloud servers for great convenience in data management and mining. However, data sets like health records in electronic documents usually contain sensitive information, which brings about privacy concerns if the documents are released or shared to partially untrusted third-parties in cloud. A practical and widely used technique for data privacy preservation is to encrypt data before outsourcing to the cloud servers, which however reduces the data utility and makes many traditional data analytic operators like keyword-based top-k document retrieval obsolete. In this paper, we investigate the multi-keyword top-k search problem for big data encryption against privacy breaches, and attempt to identify an efficient and secure solution to this problem. Specifically, for the privacy concern of query data, we construct a special tree-based index structure and design a random traversal algorithm, which makes even the same query to produce different visiting paths on the index, and can also maintain the accuracy of queries unchanged under stronger privacy. For improving the query efficiency, we propose a group multi-keyword top-k search scheme based on the idea of partition, where a group of tree-based indexes are constructed for all documents. Finally, we combine these methods together into an efficient and secure approach to address our proposed top-k similarity search. Extensive experimental results on real-life data sets demonstrate that our proposed approach can significantly improve the capability of defending the privacy breaches, the scalability and the time efficiency of query processing over the state-of-the-art methods.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Xiaofeng Ding, Member, IEEE, Peng Liu and Hai Jin, Senior Member, IEEE, “Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data”, IEEE Transactions on Dependable and Secure Computing, 2019.

Privacy-Preserving Attribute-Based Keyword Search in Shared Multi-owner Setting

Privacy-Preserving Attribute-Based Keyword Search in Shared Multi-owner Setting

ABSTRACT:

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi-owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. In addition, due to privacy concerns on access policies, most existing schemes are vulnerable to off-line keyword-guessing attacks if the keyword space is of polynomial size. Furthermore, it is difficult to identify malicious users who leak the secret keys when more than one data user has the same subset of attributes. In this paper, we present a privacy-preserving CP-ABKS system with hidden access policy in Shared Multi-owner setting (basic ABKS-SM system), and demonstrate how it is improved to support malicious user tracing (modified ABKS-SM system). We then prove that the proposed ABKS-SM systems achieve selective security and resist off-line keyword-guessing attack in the generic bilinear group model. We also evaluate their performance using real-world datasets.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo, Senior Member, IEEE, Robert H. Deng, Fellow, IEEE, Jiguo Li, Hongwei Li, and Jianfeng Ma, “Privacy-Preserving Attribute-Based Keyword Search in Shared Multi-owner Setting”, IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2019.

Enabling Authorized Encrypted Search for Multi-Authority Medical Databases

Enabling Authorized Encrypted Search for Multi-Authority Medical Databases

ABSTRACT:

E-medical records are sensitive and should be stored in a medical database in encrypted form. However, simply encrypting these records will eliminate data utility and interoperability of the existing medical database system because encrypted records are no longer searchable. Moreover, multiple authorities could be involved in controlling and sharing the private medical records of clients. However, authorizing different clients to search and access records originating from multiple authorities in a secure and scalable manner is a nontrivial matter. To address the above issues, we propose an authorized searchable encryption scheme under a multi-authority setting. Specifically, our proposed scheme leverages the RSA function to enable each authority to limit the search capability of different clients based on clients’ privileges. To improve scalability, we utilize multi-authority attribute-based encryption to allow the authorization process to be performed only once even over policies from multiple authorities. We conduct rigorous security and cost analysis, and perform experimental evaluations to demonstrate that the proposed scheme introduces moderate overhead to existing searchable encryption schemes.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Lei Xu, Shifeng Sun, Xingliang Yuan, Joseph K. Liu, Cong Zuo, Chungen Xu, “Enabling Authorized Encrypted Search for Multi-Authority Medical Databases”, IEEE Transactions on Emerging Topics in Computing, 2019.

 

Authentication by Encrypted Negative Password

Authentication by Encrypted Negative Password

ABSTRACT:

Secure password storage is a vital aspect in systems based on password authentication, which is still the most widely used authentication technique, despite its some security flaws. In this paper, we propose a password authentication framework that is designed for secure password storage and could be easily integrated into existing authentication systems. In our framework, first, the received plain password from a client is hashed through a cryptographic hash function (e.g., SHA-256). Then, the hashed password is converted into a negative password. Finally, the negative password is encrypted into an Encrypted Negative Password (abbreviated as ENP) using a symmetric-key algorithm (e.g., AES), and multi-iteration encryption could be employed to further improve security. The cryptographic hash function and symmetric encryption make it difficult to crack passwords from ENPs. Moreover, there are lots of corresponding ENPs for a given plain password, which makes precomputation attacks (e.g., lookup table attack and rainbow table attack) infeasible. The algorithm complexity analyses and comparisons show that the ENP could resist lookup table attack and provide stronger password protection under dictionary attack. It is worth mentioning that the ENP does not introduce extra elements (e.g., salt); besides this, the ENP could still resist precomputation attacks. Most importantly, the ENP is the first password protection scheme that combines the cryptographic hash function, the negative password and the symmetric-key algorithm, without the need for additional information except the plain password.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Wenjian Luo, Senior Member, IEEE, Yamin Hu, Hao Jiang, and Junteng Wang, “Authentication by Encrypted Negative Password”, IEEE Transactions on Information Forensics and Security, Volume: 14 , Issue: 1 , Jan. 2019.

 

A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation

A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation

ABSTRACT:

In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques. In this study, we propose a hybrid filtering (HF) recommendation approach (SI-IFL) combining learner influence model (LIM), self-organization based (SOB) recommendation strategy and sequential pattern mining (SPM) together for recommending learning objects (LOs) to learners. The method works as follows: (i), LIM is applied to acquire the interpersonal information by computing the influence that a learner exerts on others. LIM consists of learner similarity, knowledge credibility, and learner aggregation. LIM is independent of ratings. Furthermore, to address the uncertainty and fuzzy natures of learners, intuitionistic fuzzy logic (IFL) is applied to optimize the LIM. (ii), a SOB recommendation strategy is applied to recommend the optimal learner cliques for active learners by simulating the influence propagation among learners. Influence propagation means that a learner can move toward active learners, and such behaviors can stimulate the moving behaviors of his neighbors. This SOB recommendation approach achieves a stable structure based on distributed and bottom-up behaviors of individuals. (iii), SPM is applied to decide the final learning objects (LOs) and navigational paths based on the recommended learner cliques. The experimental results demonstrate that SI-IFL can provide personalized and diversified recommendations, and it shows promising efficiency and adaptability in e-learning scenarios.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Shanshan Wan, Zhendong Niu, “A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation”, IEEE Transactions on Knowledge and Data Engineering, 2019.

Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

ABSTRACT:

Consumers’ reviews in E-commerce systems are usually treated as the important resources that reflect user’s experience, feelings, and willingness to purchase items. All this information may involve consumers’ views on things that can express interest, sentiments, and opinions. Many kinds of research have shown that people are more likely to trust each other with the same attitude toward similar things. In this paper, we consider seeking and accepting sentiments and suggestions in E-commerce systems somewhat implies a form of trust between consumers during shopping. Following this view of point, an E-commerce system reviews mining oriented sentiment similarity analysis approach is put forward to exploring users’ similarity and their trust. We divide the trust into two categories, namely direct trust, and propagation of trust, which represents a trust relationship between two individuals. The direct trust degree is obtained from sentiment similarity, and we present an entity-sentiment word pair mining method for similarity feature extraction. The propagation of trust is calculated according to the transitivity feature. Using the proposed trust representation model, we use the shortest path to describe the tightness of trust and put forward an improved shortest path algorithm to figure out the propagation trust relationship between users. A large-scale E-commerce website reviews dataset is collected to examine the accuracy of the algorithms and feasibility of the models. The experimental results indicate that the sentiment similarity analysis can be an efficient method to find trust between users in E-commerce systems.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

SHAOZHONG ZHANG  AND HAIDONG ZHONG, “Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis”, IEEE ACCESS, 2019.

 

Detection of suicide-related posts in Twitter data streams

Detection of suicide-related posts in Twitter data streams

ABSTRACT:

Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user’s online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Johnson Vioul_es,B. Moulahi,J. Az_e,S. Bringay, “Detection of suicide-related posts in Twitter data streams”, IEEE, Volume: 62, Issue: 1, Jan.-Feb. 1 2018.

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

ABSTRACT:

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Ting Bai, Wanye Xin Zhao Member, IEEE, Yulan He Member, IEEE, Jian-Yun Nie Member, IEEE, Ji-Rong Wen Member, IEEE, “Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites”, IEEE Transactions on Knowledge and Data Engineering, 2018.

Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

ABSTRACT:

Virtual currency in OSNs plays an increasing­ly important role in supporting various financial activities such as currency exchange, online shop­ping, and paid games. Users usually purchase virtual currency using real currency. This fact moti­vates attackers to instrument an army of accounts to collect virtual currency unethically or illegally with no or very low cost and then launder the collected virtual money for massive profit. Such attacks not only introduce significant financial loss of victim users, but also harm the viability of the ecosystem. It is therefore of central importance to detect malicious OSN accounts that engage in laundering virtual currency. To this end, we extensively study the behavior of both malicious and benign accounts based on operation data collected from Tencent QQ, one of the largest OSNs in the world. Then, we devise multi-faceted features that characterize accounts from three aspects: account viability, transaction sequences, and spatial correlation among accounts. Finally, we propose a detection method by integrating these features using a statistical classifier, which can achieve a high detection rate of 94.2 percent at a very low false positive rate of 0.97 percent.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Yadong Zhou, Ximi Wang, Junjie Zhang, Peng Zhang, Lili Liu, Huan Jin, and Hongbo Jin, “Analyzing and Detecting Money-Laundering Accounts in Online Social Networks”, IEEE Network , Volume: 32, Issue: 3, May/June 2018.