An efficient hybrid system for anomaly detection in social networks

Rahman, Md. Shafiur; Halder, Sajal; Uddin, Md. Ashraf; Acharjee, Uzzal Kumar

doi:10.1186/s42400-021-00074-w

Cited by 36 publications

(15 citation statements)

References 27 publications

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“…The readers can easily access from any place at any time via the Internet. People are now comfortable accessing online news and can quickly share the news contents across the social network media such as WWW, Google, YouTube, Google+, Facebook, Twitter, Instagram, and Line [ 3 , 4 ]. Fake news is a threat to democracy around the world, which has weakened the confidence of governments, newspapers, and civil society.…”

Section: Introductionmentioning

confidence: 99%

Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning

Meesad

2021

SN COMPUT. SCI.

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Fake news is a big problem in every society. Fake news must be detected and its sharing should be stopped before it causes further damage to the country. Spotting fake news is challenging because of its dynamics. In this research, we propose a framework for robust Thai fake news detection. The framework comprises three main modules, including information retrieval, natural language processing, and machine learning. This research has two phases: the data collection phase and the machine learning model building phase. In the data collection phase, we obtained data from Thai online news websites using web-crawler information retrieval, and we analyzed the data using natural language processing techniques to extract good features from web data. For comparison, we selected some well-known classification Machine Learning models, including Naïve Bayesian, Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Support Vector Machine, Decision Tree, Random Forest, Rule-Based Classifier, and Long Short-Term Memory. The comparison study on the test set showed that Long Short-Term Memory was the best model, and we deployed an automatic online fake news detection web application.

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Section: Introductionmentioning

confidence: 99%

Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning

Meesad

2021

SN COMPUT. SCI.

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“…In the proposed framework, the Extreme "flower model" model is used to discover log files as a criterion for an over-general model with high fitness. It allows any behavior (a combination of activities), which causes a highly imprecise process model [34,61,62]. Like this study, some studies reviewed mining techniques and Petri-net discovery algorithms [63][64][65][66][67].…”

Section: Machine Learning-based Approachesmentioning

confidence: 99%

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

Sahlabadi¹,

Muniyandi²,

Shukur³

et al. 2023

Computers, Materials &Amp; Continua

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This research recognizes the limitation and challenges of adapting and applying Process Mining as a powerful tool and technique in the Hypothetical Software Architecture (SA) Evaluation Framework with the features and factors of lightweightness. Process mining deals with the largescale complexity of security and performance analysis, which are the goals of SA evaluation frameworks. As a result of these conjectures, all Process Mining researches in the realm of SA are thoroughly reviewed, and nine challenges for Process Mining Adaption are recognized. Process mining is embedded in the framework and to boost the quality of the SA model for further analysis, the framework nominates architectural discovery algorithms Flower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductive and compares them vs. twelve quality criteria. Finally, the framework's testing on three case studies approves the feasibility of applying process mining to architectural evaluation. The extraction of the SA model is also done by the best model discovery algorithm, which is selected by intensive benchmarking in this research. This research presents case studies of SA in service-oriented, Pipe and Filter, and component-based styles, modeled and simulated by Hierarchical Colored Petri Net techniques based on the cases' documentation. Process mining within this framework deals with the system's log files obtained from SA simulation. Applying process mining is challenging, especially for a SA evaluation framework, as it has not been done yet. The research recognizes the problems of process mining adaption to a hypothetical lightweight SA evaluation framework and addresses these problems during the solution development.

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“…The work done in can be consulted for further reading [23] as it compared the various approaches using features, advantages, and disadvantages of each approach. [5] proposed "an efficient hybrid system for anomaly detection in social networks". The model cascaded several machine learning algorithms that included decision tree, Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users on social networks.…”

Section: Mechanisms For Intrusion Detectionmentioning

confidence: 99%

“…In an attempt to enforce stronger password for authentication, social media users are forced to write their authentication credentials on papers which can also be stolen by hackers, these weaknesses have influenced many researchers to propose several security mechanisms to curtail the activities of these hackers. Some of these proposals include: biometric authentication, hybrid system for anomaly detection in social networks [5],Network Intrusion Detection System [6], [7], [8]etc. All these methods are not suitable for data warehouse security.…”

Section: Introductionmentioning

confidence: 99%

Social Media Network Attacks and their Preventive Mechanisms: A Review

Etuh¹,

Bakpo²,

A.H³

2021

Computing Advances &Amp; Trends

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We live in a virtual world where actual lifestyles are replicated. The growing reliance on the use of social media networks worldwide has resulted in great concern for information security. One of the factors popularizing the social media platforms is how they connect people worldwide to interact, share content, and engage in mutual interactions of common interest that cut across geographical boundaries. Behind all these incredible gains are digital crime equivalence that threatens the physical socialization. Criminal minded elements and hackers are exploiting social media platforms (SMP) for many nefarious activities to harm others. As detection tools are developed to control these crimes so also hackers’ tactics and techniques are constantly evolving. Hackers are constantly developing new attacking tools and hacking strategies to gain malicious access to systems and attack social media network thereby making it difficult for security administrators and organizations to develop and implement the proper policies and procedures necessary to prevent the hackers’ attacks. The increase in cyber-attacks on the social media platforms calls for urgent and more intelligent security measures to enhance the effectiveness of social media platforms. This paper explores the mode and tactics of hackers’ mode of attacks on social media and ways of preventing their activities against users to ensure secure social cyberspace and enhance virtual socialization. Social media platforms are briefly categorized, the various types of attacks are also highlighted with current state-of-the-art preventive mechanisms to overcome the attacks as proposed in research works, finally, social media intrusion detection mechanism is suggested as a second line of defense to combat cybercrime on social media networks.

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An efficient hybrid system for anomaly detection in social networks

Cited by 36 publications

References 27 publications

Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning

Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

Social Media Network Attacks and their Preventive Mechanisms: A Review

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