NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks

Sharma, Vishal; Kumar, Ravinder; Cheng, Winton; Atiquzzaman, Mohammed; Srinivasan, Kathiravan; Zomaya, Albert Y.

doi:10.1109/tkde.2018.2818163

Cited by 25 publications

(26 citation statements)

References 38 publications

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“…e previous methods based on manual processing such as auditing were cumbersome and ineffective due to large-size data or its attributes. Data mining techniques are considered effective in assessing small outliers in large datasets [9,11,12]. Frauds lead to heavy business losses.…”

Section: Credit Card Payment Processingmentioning

confidence: 99%

Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence

Arora

Leekha²,

Lee

et al. 2020

Mobile Information Systems

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An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies. The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk. Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster. In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive. The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves. Datasets used for training and testing of the models have been taken from kaggle.com.

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Section: Credit Card Payment Processingmentioning

confidence: 99%

Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence

Arora

Leekha²,

Lee

et al. 2020

Mobile Information Systems

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“…A self-healing neuro-fuzzy anomaly detection approach was proposed to achieve the detection, recovery, and removal of horizontal anomalies in social networks [36]. This scheme was implemented on multiple paradigms and datasets with high detection accuracy and abnormal filtering rate.…”

Section: Security Monitoring and Surveillance Technologymentioning

confidence: 99%

A Smart Collaborative Authentication Framework for Multi-Dimensional Fine-Grained Control

Liu

et al. 2020

IEEE Access

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The emergence of the 5G network has brought broad prospects for the massive terminal access and ubiquitous Internet of Things (IoTs). Potential attacking opportunities triggered by this progress are severely impacting the security fortress of current networks, especially in the edge access part. However, due to the unitary protection and inferior isolation, available security schemes are incapable of effectively eliminating these hidden perils. Motivated by these facts, we propose a Multi-dimensional Fine-grained Control (MFC) framework to strengthen safety and reliability in Radio Access Networks (RANs). First, we comprehensively survey and summarize the existing security schemes to grasp respective effects and limitations. Second, the MFC framework is established to describe the model structure and implementation processes. An identifier mapping mechanism is designed to achieve network isolation. We perform the security analysis of MFC by theoretically comparing diversified policies. Third, an integrated set of the authentication prototype system is created with wireless environment parameters settings. Specific verification scenarios are illustrated. Finally, we test the performances of the MFC framework. Validation results demonstrate that the proposed scheme can accomplish reliable security control at the access side. Comparing to multiple schemes, the performances, in terms of time and concurrency, are optimized. Therefore, the MFC framework is feasible for applications in 5G or IoT.

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“…The security should be considered in different do-mains to handle attacks and security threats. Various secure architectures have been suggested to support the domain-specific application with the facilities of reliability and efficiency [132]- [134]. But from the research perspective, the adopted architecture should be enabled to follow generalized implementation mechanism within the application domains and sustain the security within the architecture.…”

Section: Secure and Flexible Architecturementioning

confidence: 99%

A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks

Choudhary

Sharma

2019

5G Enabled Secure Wireless Networks

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The 5G networks have the capability to provide high compatibility for the new applications, industries, and business models. These networks can tremendously improve the quality of life by enabling various use cases that require high data-rate, low latency, and continuous connectivity for applications pertaining to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of Things (IoT). However, these applications need secure servicing as well as resource policing for effective network formations. There have been a lot of studies, which emphasized the security aspects of 5G networks while focusing only on the adaptability features of these networks. However, there is a gap in the literature which particularly needs to follow recent computing paradigms as alternative mechanisms for the enhancement of security. To cover this, a detailed description of the security for the 5G networks is presented in this article along with the discussions on the evolution of osmotic and catalytic computing based security modules. The taxonomy on the basis of security requirements is presented, which also includes the comparison of the existing state-of-the-art solutions. This article also provides a security model, "CATMOSIS", which idealizes the incorporation of security features on the basis of catalytic and osmotic computing in the 5G networks. Finally, various security challenges and open issues are discussed to emphasize the works to follow in this direction of research. and vulnerabilities have been evolving continuously with the networks because of an increase in the number of users where attackers try to exploit the potential weaknesses. To easily follow these issues and aspects, an overview of 5G applications, technologies, features and performance challenges and standardization are presented in Fig.1 and Fig.2. Current 5G networks are supported by different technology enablers, which include Fig 2. An illustration of 5G evolution, standardization and known security attacks. Applications of 5G networks:The major applications of the 5G networks are provided below:1. Autonomous Vehicle: The automatic controlled driving car and vehicles are key enablers of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and other Intelligent Transport Systems (ITS). The 5G network supports large bandwidth and low latency for these applications with high connection reliability. This network supports collision avoidance and intelligent navigation for the reliable transportation systems [6] [7]. Public Safety Communications (PSCs):The PSCs facilitated various communication services in case of emergencies when the primary communication infrastructure is not available. The PSCs incorporated rapid deployment and accessibility of communication set-up. Therefore, 5G enabled communications supports a wide range of applications and long-term connectivity for the services in case of emergencies [12]. 3. Military Applications: The Military involved mission-critical control application which requires high data rate and long-term c...

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NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks

Cited by 25 publications

References 38 publications

Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence

Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence

A Smart Collaborative Authentication Framework for Multi-Dimensional Fine-Grained Control

A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks

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