Journal of Cybersecurity and Information Management

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2690-6775ISSN (Online) 2769-7851ISSN (Print)

K-means Clustering Analysis of Crimes on Indian Women

Rishabh Singh , Rishabh Reddy , Vidhi Kapoor , Prathamesh Churi

Violence against women is seen as sexual or physical activity committed against women. In India, general forms of violence against women in India includes cruelty by relatives, dowry, rape, sexual assault, kidnapping, immoral trafficking, molestation etc. The security of the women is the utmost priority of any government in this world. In India, many policies and laws have been enforced to ensure the safety against women. Technology is being the biggest supporter to the government in this context. Data mining allows various techniques such as clustering classification, regression provides analysis in any form of data and helps intelligent predictions on the given dataset. In this paper, we use k-means clustering analysis on women crime dataset. As a part of pre-processing, we collated the data entries which had crime cases against women and made women crime sub-dataset from the real dataset. We then applied K means clustering for further analysis. We used a rapid miner tool for clustering analysis as it is widely used for clustering purposes. After completion of clustering analysis, we proposed our views and discussions on the clustering results. At the end, we ended up giving the futuristic work to be further done on the derived dataset we made and made available on public repositories.

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Vol. 4 Issue. 1 PP. 05-25, (2020)

Security and Privacy Challenges in Cloud Computing: A Review

Miss. Sayali Karmode Yelpale

Cloud computing is being transformed into a model with services. It is mainly focused on the concept of dynamic provisioning, which is applied to services as well as to the capacity, storage, networking, and information technology infrastructure. This paper reviews the key concepts of cloud computing exist. We are presenting here various concepts of cloud computing. According to the previous few surveys we are presenting various security challenges and for that what solution are overcome.

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Vol. 4 Issue. 1 PP. 36 - 45, (2020)

A Web Based Document Encryption Application Software for Information Security in Tertiary Institutions

Ibeneme-Sabinus Ifeoma Livina , Ekedebe Nnanna , Nwokonkwo Obi , Ibeneme Sabinus Ikechukwu , Ajah Benjamin Ogonnaya , Amadi Emmanuel Chukwudi

A web based user friendly document encryption application software was developed as a better cryptographic online system used in securing important tertiary institutions’ documents such as students' Official Grade Report (OGR) sheets, computed and approved results, transcripts, examination and test question papers, Senate/Council documents and any other sensitive or important document needed to be secured from unauthorized users. It is a known fact that problems like unsecured information, information misuse, tampering of sensitive documents by unauthorized persons, stress of hiding sensitive documents from unauthorized persons and locating hidden sensitive documents exist in any system/unit where proper data security mechanism is not in place. In this research work, an online cryptographic system was designed, using Advanced Encryption Standard (AES) algorithm, to secure important documents. The Analysis and Design of this system followed the Structured System Analysis and Design Methodology (SSADM) using web tools. The output of the software shows that the application can encrypt files and save in the database and can only be decrypted using a cipher key automatically generated by the system. With this software, sensitive information can be easily accessed without stress or fear and it has created a more reliable and safer platform to secure such sensitive documents other than the primitive method of using username and password.

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Vol. 4 Issue. 1 PP. 26-35, (2020)

Survey on Deep Learning Approaches for Aspect Level Opinion Mining


  The the task of Aspect-based opinion mining (AbOM) is an emeraging research area, where aspects are mined, the corresponding opinion are scrutinized and sentiments are continuously changed, is gaining increased attention with growing feedback of clients and community across various social media streams. The gigantic improvements of deep learning (DL) techniques in natural language processing (NLP) tasks motivated research community to introduce  a novel DL models and for AbSA, each investigate a diverse research points from different perspective, that cope with imminent problems and composite circumstances of AbOM. Consequently, in this survey paper, we concentrate on the limitations of the current studies and challenges relevant to mining of various aspects and their pertinent opinion, interrelationship delineations among different aspects, interactions, dependencies and contextual-semantic associations among various entities for enhanced opinion precision, and estimation of the automaticity of opinion polrity development. A laborious investigation of the later  advancement is discussed depending on their contribution in the direction of spotlighting and alleviating the shortcomings related to Aspect Extraction (AE), AbOM, opinion progression (OP). The reported performance for each scrutinized study of Aspect Extraction and Aspect opinion Analysis is also given, revealing the numeriacal evaluation of the presented approach. Future research trends are introduce and delibrated by critically analysing the existing recent approaches, that will be supportive for researchers and advantageous for refining aspect based opinion classification.

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Vol. 4 Issue. 1 PP. 46-66, (2020)