Predicting Consumption Patterns with Repeated and Novel Events

Predicting Consumption Patterns with Repeated and Novel Events

ABSTRACT:

There are numerous contexts where individuals typically consume a few items from a large selection of possible items. Examples include purchasing products, listening to music, visiting locations in physical or virtual environments, and so on. There has been significant prior work in such contexts on developing predictive modeling techniques for recommending new items to individuals, often using techniques such as matrix factorization. There are many situations, however, where making predictions for both previously-consumed and new items for an individual is important, rather than just recommending new items. We investigate this problem and find that widely-used matrix factorization methods are limited in their ability to capture important details in historical behavior, resulting in relatively low predictive accuracy for these types of problems. As an alternative we propose an interpretable and scalable mixture model framework that balances individual preferences in terms of exploration and exploitation. We evaluate our model in terms of accuracy in user consumption predictions using several real-world datasets, including location data, social media data, and music listening data. Experimental results show that the mixture model approach is systematically more accurate and more efficient for these problems compared to a variety of state-of-the-art matrix factorization 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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Dimitrios Kotzias, Moshe Lichman, and Padhraic Smyth, Member, IEEE, “Predicting Consumption Patterns with Repeated and Novel Events”, IEEE Transactions on Knowledge and Data Engineering, 2019.

 

Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques

Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques

ABSTRACT:

Internet of Things (IoT) is a domain wherein which the transfer of data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, the elliptic Galois cryptography protocol is introduced and discussed. In this protocol, a cryptography technique is used to encrypt confidential data that came from different medical sources. Next, a Matrix XOR encoding steganography technique is used to embed the encrypted data into a low complexity image. The proposed work also uses an optimization algorithm called Adaptive Firefly to optimize the selection of cover blocks within the image. Based on the results, various parameters are evaluated and compared with the existing techniques. Finally, the data that is hidden in the image is recovered and is then decrypted.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Manju Khari, Aditya Kumar Garg, Amir H. Gandomi , Senior Member, IEEE, Rashmi Gupta, Member, IEEE, Rizwan Patan , and Balamurugan Balusamy, “Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, 2019.

Towards Robust Image Steganography

Towards Robust Image Steganography

ABSTRACT:

The behavior of posting images on social network platforms is happened everywhere and every single second. Thus, the communication channels offered by various social networks have a great potential for covert communication. However, images transmitted through such channels will usually be JPEG compressed, which fails most of the existing steganographic schemes. In this paper, we propose a novel image steganography framework that is robust to such channels. In particular, we first obtain the channel compressed version (i.e., the channel output) of the original image. Secret data is embedded into the channel compressed original image by using any of the existing JPEG steganographic schemes, which produces the stego-image after the channel transmission. To generate the corresponding image before the channel transmission (termed as the intermediate image), we propose a coefficient adjustment scheme to slightly modify the original image based on the stegoimage. The adjustment is done such that the channel compressed version of the intermediate image is exactly the same as the stego-image. Therefore, after the channel transmission, the secret data can be extracted from the stego-image with 100% accuracy. Various experiments are conducted to show the effectiveness of the proposed framework for image steganography robust to JPEG compression.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Jinyuan Tao, Sheng Li, Xinpeng Zhang, and Zichi Wang, “Towards Robust Image Steganography”, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 29 , Issue: 2 , Feb. 2019.

 

Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations

Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations

ABSTRACT:

In the latest years, a number of citizen movements and protests have spread across the world. One of the characteristics of such events is that demonstrations have been aroused by the use of social networking channels such as Twitter, Facebook, and Whatsapp, among others. Different scholars are currently analyzing this phenomenon to better understand its impact on societies. Furthermore, the use of the Internet as a driver or tool for organizing different groups and demonstrations leaves traces of social changes that have been addressed by technology. Nevertheless, it is important to define ways of identifying different movements, as well as possible misuse by so-called Internet trolls or hijackers, whose objective is to start arguments and confuse or upset other users. In this work, the authors present the case of demonstrations in Ecuador from March 2015 to April 2016 and use data from Twitter users who engaged in those demonstrations. Ecuador has a long history of demonstrations against different governments, which makes this scenario very attractive for more in depth study. Moreover, the authors present a framework for identifying political interest groups as well as possible hashtag hijackers. Specifically, this work focuses on the problem of giving recommendations to groups in which a group of users with the same political view receives suggestions of users they should not follow because they have opposing political views but use hijacked hashtags. Experiments on real-world data collected from the previously mentioned demonstrations show the effectiveness of this approach in automatically identifying hijackers so that they can be effectively recommended to a group as people they should not follow.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Lorena Recalde, Jonathan Mendietay, Ludovico Borattoz, Luis Ter ´anx, Carmen Vacay, and Gabriela Baquerizo, “Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations”,  IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2019.

 

Trust-based Privacy-Preserving Photo Sharing in Online Social Networks

Trust-based Privacy-Preserving Photo Sharing in Online Social Networks

ABSTRACT:

With the development of social media technologies, sharing photos in online social networks has now become a popular way for users to maintain social connections with others. However, the rich information contained in a photo makes it easier for a malicious viewer to infer sensitive information about those who appear in the photo. How to deal with the privacy disclosure problem incurred by photo sharing has attracted much attention in recent years. When sharing a photo that involves multiple users, the publisher of the photo should take into all related users’ privacy into account. In this paper, we propose a trust-based privacy preserving mechanism for sharing such co-owned photos. The basic idea is to anonymize the original photo so that users who may suffer a high privacy loss from the sharing of the photo cannot be identified from the anonymized photo. The privacy loss to a user depends on how much he trusts the receiver of the photo. And the user’s trust in the publisher is affected by the privacy loss. The anonymiation result of a photo is controlled by a threshold specified by the publisher. We propose a greedy method for the publisher to tune the threshold, in the purpose of balancing between the privacy preserved by anonymization and the information shared with others. Simulation results demonstrate that the trust-based photo sharing mechanism is helpful to reduce the privacy loss, and the proposed threshold tuning method can bring a good payoff to the user.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Lei Xu1, Ting Bao1, Liehuang Zhu1 and Yan Zhang, “Trust-based Privacy-Preserving Photo Sharing in Online Social Networks”, IEEE Transactions on Multimedia, Volume: 21 , Issue: 3 , March 2019.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

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.

 

Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges

Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges

ABSTRACT:

Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

MOHAMMED ALI AL-GARADI1, MOHAMMAD RASHID HUSSAIN2, NAWSHER KHAN2, GHULAM MURTAZA1,3, HENRY FRIDAY NWEKE 1, IHSAN ALI 1, GHULAM MUJTABA1,3, HARUNA CHIROMA 4, HASAN ALI KHATTAK 5, AND ABDULLAH GANI, “Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges”, IEEE Access ( Volume: 7 ), 2019.

 

Credibility Evaluation of Twitter-Based Event Detection by a Mixing Analysis of Heterogeneous Data

Credibility Evaluation of Twitter-Based Event Detection by a Mixing Analysis of Heterogeneous Data

ABSTRACT:

Twitter has been recognized as an important data resource for real-time event detection. However, Twitter-based event detection systems cannot guarantee credibility in terms of their detection results. Rumor detection has been studied recently to enable credible event detection. Nevertheless, this problem has not yet been solved because most of the existing studies only focus on the information on Twitter with Twitter-based event detection systems. More specifically, the existing studies detect rumors by identifying and checking special features of incredible information on Twitter. However, values of the identified features can be faked easily and so it is important to conduct a mixing analysis of both Twitter and external credible data resources to solve this problem. The problem is how to harmoniously analyze heterogeneous data since they have different data formats, generation times, and so on. To solve this problem, this paper proposes a method to evaluate the credibility of Twitter-based event detection, which considers the two kinds of data resources for credibility evaluation to exclude influence by falsification. In particular, our method utilizes an event detection result and the number of articles related to the event. We performed comprehensive experiments to evaluate the proposed method. The experiments show that the proposed method gives the detected events high credibility and other events low credibility, correctly. More specifically, event detection accuracy increases by an average of 26.8% by reviewing the detection results according to their credibility evaluated with the proposed method. Additionally, to measure the appropriateness of the credibility evaluation, we filtered out incorrectly detected events from the event detection results referencing their evaluated credibility based on the proposed method. We calculated the F-measure, precision, and recall of the experimental results, and through the experiments, we present the effectiveness of the method.

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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

KOICHI SATO , (Member, IEEE), JUNBO WANG , (Member, IEEE), AND ZIXUE CHENG, (Member, IEEE), “Credibility Evaluation of Twitter-Based Event Detection by a Mixing Analysis of Heterogeneous Data”, IEEE Access ( Volume: 7 ), 2019.

Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data

Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data

ABSTRACT:

As a basic query function, range query has been exploited in many scenarios such as Sql retrieves, location-based services, and computational geometry. Meanwhile, with explosive growth of data volume, users are increasingly inclining to store data on the cloud for saving local storage and computational cost. However, a long-standing problem is that the user’s data may be completely revealed to the cloud server because it has full data access right. To cope with this problem, a frequently-used method is to encrypt raw data before outsourcing them, but the availability and operability of data will be reduced significantly. In this paper, we propose an Efficient and Geometric Range Query scheme (EGRQ) supporting searching and data access control over encrypted spatial data. We employ secure KNN computation, polynomial fitting technique and order-preserving encryption to achieve secure, efficient and accurate geometric range query over cloud data. Then, we propose a novel spatial data access control strategy to refine user’s rights in our EGRQ. To improve the efficiency, R-tree is adopted to reduce the searching space and matching times in whole search process. Finally, we theoretically prove the security of our proposed scheme in terms of confidentiality of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoors. In addition, extensive experiments demonstrate the high efficiency of our proposed model compared with existing 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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Guowen Xu, L, Hongwei Li(Corresponding author), Yuanshun Dai, Kan Yang and Xiaodong Lin, “Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data”, IEEE Transactions on Information Forensics and Security, Volume: 14 , Issue: 4 , April 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 : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

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.