Volume-10, Issue-6, June 2024

Abstract

The golden rule of tourism's positive socioeconomic effects is that it generates income for both the host economy and the external economy. Additionally, tourism rekindles interest in the region's economy and foundation, which encourages business and, once again, results in an increase in pay for the local population. A job can be obtained specifically in the tourism industry through accommodation, dining, dancing, taxis, souvenir sales, and other tourism-related services, or more generally by contributing the goods and services needed by productions that are related to tourism. In 2014, the tourist sector contributed US$ 7.6 trillion, or 9.8%, to the global GDP, an increase of five straight years. This particular section illustrates the study's research challenges and clarifies the investigation's fundamental framework. The goals and hypotheses of the research have been thoroughly discussed. An explanation of the data collection tool and a discussion of the projection of studyrelated questions are provided. Also covered are data collection techniques and statistical analysis procedures. The main goal is to know the market development of tourism industry in Kashmir region. All of these components have been evaluated realistically in light of market development of tourism industry. The relevant features of the subject have been examined using a self-structured questionnaire.

Keywords: Tourism, Questionnaire, Tourist Sector, Socioeconomic.

References

Keywords: Tourism, Questionnaire, Tourist Sector, Socioeconomic.

Abstract

In privacy preserving data mining, two principle approaches have been talked about in the writing viz. Cryptography approaches and anonymization approaches. Be that as it may, our spotlight in this thesis is on the anonymization based approaches attributable to the lesser computational cost contrasted with the cryptography approaches. As of late, different associations in various divisions viz. Medicinal, Banking and Insurance gather, store and utilize individual data of their clients. Such gathered data are additionally utilized for the investigation and research purposes. To do likewise, data mining systems have been used for playing out the errand of examination and research work. In any case, the gathered data may contain individual explicit private data. In this way, breaking down such gathered data can uncover the private data of a person. Therefore, ensuring the private data of an individual turns into a prime research issue in privacy preserving data mining

Keywords: Privacy Preserving Data Mining (PPDM), Anonymization Techniques, k-Anonymity, Generalization (in anonymization), Suppression (in anonymization), Data Loss (in anonymization), Data Utility (in anonymization), Privacy Protection, Data Security, Sensitive Data, Individual Data Privacy.

References

Keywords: Privacy Preserving Data Mining (PPDM), Anonymization Techniques, k-Anonymity, Generalization (in anonymization), Suppression (in anonymization), Data Loss (in anonymization), Data Utility (in anonymization), Privacy Protection, Data Security, Sensitive Data, Individual Data Privacy.

Abstract

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: Network security, Intrusion Detection System, Knowledge Discovery, Databases.

References

Keywords: Network security, Intrusion Detection System, Knowledge Discovery, Databases.

Abstract

Cloud computing has emerged as a major technology for delivering infrastructure and data service needs at cheap cost, with minimum effort and great scalability, and has therefore been widely adopted in the IT sector. Although there has been a tremendous increase in Cloud Computing use, information security issues have yet to be entirely addressed. To address the current challenges, this paper proposes a decentralised security architecture for the IoT network in the smart city based on Software Defined Networking (SDN) combined with blockchain technology that relies on the three core technologies of SDN, Blockchain, and Fog as well as mobile edge computing to detect attacks in the IoT network more effectively. Our findings show that the suggested decentralised security architecture outperforms centralised and distributed security architectures in the IoT ecosystem and takes less time to prevent threats. Our results also show that the architecture might be used with the IoT ecosystem as a security detection component that monitors and analyses the whole IoT ecosystem's traffic data to identify and prevent possible threats.

Keywords: Security, Architecture, Blockchain, IoT, Network.

References

Keywords: Security, Architecture, Blockchain, IoT, Network.