Mobile Forensic Analysis of Signal Messenger Application on Android using Digital Forensic Research Workshop (DFRWS) Framework

Riadi, Imam; Herman, Herman; Siregar, Nur Hamida

doi:10.18280/isi.270606

Cited by 4 publications

(4 citation statements)

References 27 publications

(35 reference statements)

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“…Riadi et al [5] investigated the digital forensics of the Signal Messenger application on Android devices, particularly during the rise of cybercrimes in the COVID-19 era. Using tools like Belkasoft, Magnet AXIOM, and MOBILedit Forensic Express within the Digital Forensics Research Workshop (DFRWS) framework, they sought to uncover digital evidence.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

Karakuş,

Kaya,

Tuncer

2023

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Rapid advancements in artificial intelligence, machine learning, deep learning, coupled with easy access to high-capacity processing hardware, expansive organized datasets, and the evolution of artificial intelligence algorithms, have extensively influenced numerous fields. Digital Forensics is one such discipline where the application of artificial intelligence has been significantly amplified in recent years. The analysis of extensive image and video files derived from forensic evidence presents challenges in terms of time efficiency and accuracy. To surmount these challenges, artificial intelligence models can be employed to perform identification and classification processes on these data, thus expediting the resolution of forensic cases with enhanced precision. In the current study, state-of-the-art pre-trained YOLOv8 object recognition models - nano, small, medium, large, and extra-large - were utilized. These models were trained on the Wider-Face dataset with the objective of identifying suspects from images and videos sourced from digital materials in the field of digital forensics. The models achieved mean Average Precision (mAP) values of 97.513%, 98.569%, 98.763%, 98.775%, and 99.032% respectively. The YOLOv8 architecture demonstrated superior performance, outperforming the YOLOv5 architecture by a margin of 7.1% to 8.8%. To aid digital forensic experts in the detection and identification of suspicious individuals, a desktop application capable of real-time image analysis was developed.

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Section: Related Workmentioning

confidence: 99%

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

Karakuş,

Kaya,

Tuncer

2023

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“…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]. Di Indonesia, banyak orang menggunakan perangkat mobile seperti smartphone atau tablet untuk mengakses media sosial, dan aktivitas online di media sosial melalui smartphone pun signifikan dalam seminggu [4].…”

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.

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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.

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“…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

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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.

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Mobile Forensic Analysis of Signal Messenger Application on Android using Digital Forensic Research Workshop (DFRWS) Framework

Cited by 4 publications

References 27 publications

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

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

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