TAARA Method for Processing on the Network Forensics in the Event  of an ARP Spoofing Attack

Wijayanto, Agus; Riadi, Imam; Prayudi, Yudi

doi:10.29207/resti.v7i2.4589

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Kemajuan teknologi yang cepat memberikan dampak positif pada masyarakat karena memudahkan berbagai aktivitas sehari-hari. Namun, dampak negatifnya juga terlihat dengan meningkatnya kejahatan melalui internet yang semakin beragam [1], [2]. Respon masyarakat terhadap kemajuan teknologi dan komunikasi tercermin dari kemampuan mereka dalam mengakses informasi yang tersebar melalui internet, terutama melalui media sosial [3].…”

Section: Latar Belakangunclassified

Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter

Maradona,

Kusrini,

Alva Hendi Muhammad

2023

metik. j.

View full text Add to dashboard Cite

This research focuses on analyzing the impact of social media on society, particularly addressing the issue of cyberbullying on the Twitter platform. Based on statistics, the majority of internet users in Indonesia actively utilize social networks, with Twitter being the most dominant platform used for communication and interaction. Therefore, cyberbullying cases often occur on this social media platform. In this study, two classification methods, namely Decision Tree and K-Nearest Neighbor (KNN), were employed to classify cyberbullying-related messages on Twitter. The aim of this research is to compare the performance of these two methods and to identify early signs of cyberbullying as relevant digital evidence for legal proceedings. The dataset used in this study consists of 650 comment records from the period 2019 to 2021, with predefined labels. The analysis results indicate that K-Nearest Neighbor achieved the highest accuracy, reaching 75.99%, compared to Decision Tree with 65.00%. Hence, K-Nearest Neighbor is considered a more effective method for cyberbullying analysis on the Twitter platform. Additionally, the identification of early signs of cyberbullying in comment id 2 can serve as relevant digital evidence for legal purposes. This research provides better insights into the effectiveness of classification in addressing cyberbullying issues on the Twitter platform.

show abstract

Section: Latar Belakangunclassified

Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter

Maradona,

Kusrini,

Alva Hendi Muhammad

2023

metik. j.

View full text Add to dashboard Cite

show abstract

“…The advent of social media, a product of technological advancement, has instigated a paradigm shift in people's worldviews, lifestyles, and cultural practices. As more individuals are drawn towards online networks in daily life, certain activities, inextricably linked with the internet, are now deemed necessities by a majority of groups [1]. Social media, by virtue of its ability to forge broader connections, exerts a significant influence on people's lives [2].…”

Section: Introductionmentioning

confidence: 99%

Utilizing K-Means Clustering for the Detection of Cyberbullying Within Instagram Comments

Muhariya,

Riadi,

Prayudi

et al. 2023

ISI

View full text Add to dashboard Cite

With the proliferation of social media platforms like Instagram, Twitter, and Facebook, the dissemination of information has undergone a significant transformation. Instagram, distinguished by its emphasis on visual media, has emerged as a platform of choice for photo and video sharing. Despite its popularity, the platform's vast reach renders it vulnerable to malevolent activities, including cyberbullying. While prior research has employed SVM, NBC, C45, and K-Nearest Neighbors for cyberbullying analysis, these studies predominantly focused on Twitter. This paper presents a novel approach, harnessing the power of K-means Clustering to identify instances of cyberbullying on Instagram. In this study, a labelled dataset is gathered and subjected to pre-processing steps, including case folding, tokenization, removal of stopwords, normalization, and stemming. Subsequently, the K-means Clustering algorithm is implemented and evaluated using 10fold cross-validation. The results indicate a threshold value of 1.0, an accuracy rate of 64.25%, a precision of 79.29%, and a recall of 59.88% in categorizing bullying words on Instagram. This research underscores the potential of the K-means algorithm in effectively distinguishing between bullying and non-bullying comments. A notable advancement of this paper is the integration of the two tf-idf weighting methods with the K-means clustering algorithm, thereby enhancing the accuracy in grouping comment data into cyberbullying and non-cyberbullying categories.

show abstract

ARP spoofing detection using machine learning classifiers: an experimental study

Majumder,

Deb Barma,

Saha

2024

Knowl Inf Syst

View full text Add to dashboard Cite

TAARA Method for Processing on the Network Forensics in the Event of an ARP Spoofing Attack

Cited by 4 publications

References 24 publications

Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter

Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter

Utilizing K-Means Clustering for the Detection of Cyberbullying Within Instagram Comments

ARP spoofing detection using machine learning classifiers: an experimental study

Contact Info

Product

Resources

About