An essential tool for monitoring and identifying intrusion threats is the intrusion detection system (IDS). As a result, intrusion detection systems monitor network traffic heading through computer systems to detect for malicious activity and recognized dangers, and send alerts. With a focus on datasets, ML methods, and metrics, this study tries to analyse recent IDS research using a Machine Learning (ML) approach. To make sure the model is suitable for IDS application, dataset selection is crucial. The efficiency of the ML method can also be impacted by the dataset structure. As a result, the choice of ML algorithm depends on the dataset's structure. Metric will then offer a quantitative assessment of ML algorithms for a given dataset. In addition True Positive Rate (TPR), False Positive Rate (FPR) and accuracy, are the three metrics for IDS performance evaluation that are most frequently utilized. This is understandable given that these metrics offer crucial cues that are crucial to IDS performance. A clear path and direction for future study has been provided by the discussion and comparison of the results from various works.
Illegal online financial transactions are now more sophisticated and global in scope, which costs both parties—customers and businesses. For fraud prevention and detection in the online setting, many different strategies have been proposed. While all of these techniques aim to detect and stop fraudulent online transactions, they differ in terms of their features, advantages, and disadvantages. This study assesses the current fraud detection research in this area to detect the employed algorithms and assessing in accordance with predetermined standards. The systematic quantitative literature review methodology was used to assess the research studies in the subject of online fraud detection. A hierarchical typology is created based on the supervised learning methods in scientific articles and their properties. Therefore, by integrating three selection criteria—accuracy, coverage, and costs—our research presents the best methods for identifying fraud in a novel approach. Index Terms : Detection, Online fraud, Online transaction, Supervised Learning Algorithm.
In the current modern world, the way of life style is being completely changed due to the emerging technologies which are reflected in treating the patients too. As there is a tremendous growth in population, the existing e-Healthcare methods are not efficient enough to deal with numerous medical data. There is a delay in caring of patient health as communication networks are poor in quality and moreover smart medical resources are lacking and hence severe causes are experienced in the health of patient. However, authentication is considered as a major challenge ensuring that the illegal participants are not permitted to access the medical data present in cloud. To provide security, the authentication factors required are smart card, password and biometrics. Several approaches based on these are authentication factors are presented for e-Health clouds so far. But mostly serious security defects are experienced with these protocols and even the computation and communication overheads are high. Thus, keeping in mind all these challenges, a novel Multifactor Key management-based authentication by Tunnel IPv6 (MKMA- TIPv6) protocol is introduced for e-Health cloud which prevents main attacks like user anonymity, guessing offline password, impersonation, and stealing smart cards. From the analysis, it is proved that this protocol is effective than the existing ones such as Pair Hand (PH), Linear Combination Authentication Protocol (LCAP), Robust Elliptic Curve Cryptography-based Three factor Authentication (RECCTA) in terms storage cost, Encryption time, Decryption time, computation cost, energy consumption and speed. Hence, the proposed MKMA- TIPv6 achieves 35bits of storage cost, 60sec of encryption time, 50sec decryption time, 45sec computational cost, 50% of energy consumption and 80% speed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.