Exploring machine learning techniques for fake profile detection in online social networks

Bharti, Bharti; Gill, Nasib Singh; Gulia, Preeti

doi:10.11591/ijece.v13i3.pp2962-2971

Cited by 7 publications

(4 citation statements)

References 23 publications

(28 reference statements)

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“…The research of Ref. [14] explores various machine-learning techniques for detecting fake profiles in online social networks. They use a feature-based approach that uses various machine learning algorithms, such as Decision Trees, SVM, and Random Forests, to detect fake profiles in online social networks.…”

Section: Machine Learningmentioning

confidence: 99%

“…Scheme Methodology [9] Fuzzy Logic Scheme Fuzzy logic is a mathematical approach that allows for imprecise reasoning and decision-making by representing uncertainty and ambiguity using linguistic variables and fuzzy sets. [14] Support Vector Machines (SVM) SVM is a supervised learning algorithm that can be used for binary classification of nodes as either genuine or Sybil based on input features such as the number of neighbors, message transmission frequency, and signal strength. [9] Decision Trees Decision trees are another supervised learning algorithm that can be used to classify nodes as genuine or Sybil.…”

Section: Refmentioning

confidence: 99%

“…The research of Ref. [14] The paper only uses a relatively small dataset to train and test the proposed models, which may limit the generalizability of the findings [23].…”

Section: Critical Analysismentioning

confidence: 99%

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ML-Based Detection of Sybil Attack on MANETS

Ashraf,

Nawaz,

Jhanjhi

2024

Preprint

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Mobile Ad hoc Networks (MANETs) play an important role in the technological era by enabling wireless communication in dynamic and infrastructure-free contexts. Because of Manet’s unique qualities and decentralized nature, MANETs are subject to many attacks such as the Blackhole attack, grey hole attack, Byzantine attack, Wormhole attack, and Sybil attack. Sybil attacks pose a substantial risk to MANETs. A Sybil attack occurs when a malicious node impersonates many identities inside the network, resulting in the creation of multiple suspicious nodes that appear as independent entities. This study presents a thorough analysis of the literature on the detection of Sybil attacks on MANETs. To find gaps in the literature, current surveys are also looked at. Also taken into consideration are several contemporary systems built on distinct techniques. In terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end latency, some recent methods-based approaches are also evaluated severely. To address the stated state-of-the-art issues in Sybil attack detection, this paper suggests ML-based strategies as the most suitable answer. Machine learning has huge potential for effective wireless network management. According to the researcher, this is the first comprehensive assessment of ML-based techniques for Sybil attack detection in wireless networks. Finally, we considered the unsolved issues in Sybil assault detection in wireless networks.

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Section: Machine Learningmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

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ML-Based Detection of Sybil Attack on MANETS

Ashraf,

Nawaz,

Jhanjhi

2024

Preprint

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“…Numerous research efforts have delved into the application of machine learning methodologies for fake pro le detection. For instance, a study by Bharti et al (Bharti and Gulia 2023) used approaches such as support vector machine (SVM), random forest (RF), and naive bayes (NB) to detect fraudulent accounts across social-media platforms. The study collected raw data, extracted features, and used machine learning-based classi ers to identify fraudulent pro les.…”

Section: Literature Reviewmentioning

confidence: 99%

Securing Social Spaces: Machine Learning Techniques for Fake Profile Detection on Instagram

Goyal,

Gill,

Gulia

2024

Preprint

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Fake profiles, on Instagram, pose significant challenges in online spaces. These profiles are fabricated with the intention to mislead and deceive by assuming the identity of genuine persons or institutions. These individuals often disseminate false information, participate in spamming, or carry out criminal acts, which negatively impact user confidence and online safety. Detecting these fake profiles involves analysis of various factors like account activity, behavior patterns, content, and network interactions to distinguish these from genuine users. Advancements in machine learning and AI techniques play a crucial role in developing robust detection models to mitigate the impact of these deceptive profiles. This paper delves into the intricate realm of fake profile detection on social-media platforms. The proposed model exhibits commendable performance metrics, showcasing an accuracy of 83.84%. Delving into specifics, for the identification of fake profiles, precision stands at 80.65%, recall at 80.16%, and an F1-Score of 80.41%. When detecting real profiles, precision, recall, and F1-Score excel significantly higher at 86.06%, 86.43%, and 86.24% respectively. These impressive metrics are achieved through a sophisticated approach leveraging DistilBERT for text processing, SMOTE for handling imbalanced data, and Random Forest for classification to detect fake profiles on Instagram based on user biography length. In essence, this research contributes substantial insights and advanced methodologies to the intricate domain of fake profile detection. It aspires to fortify the trustworthiness and credibility of online identity verification systems, serving as a foundational stone for future advancements in the field.

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