• Title/Summary/Keyword: Network Forensic

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Extraction of Forensic Evidence and Hacking Attacks about IP-PBX (IP-PBX에 대한 해킹공격과 포렌식 증거 추출)

  • Park, Dea-Woo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2013.05a
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    • pp.197-200
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    • 2013
  • Internet phone, communication cost and easy-to-use low-cost compared to the PSTN is a mobile phone of a conventional, and use of the Internet phone is spreading. Construction as part of the broadband convergence network(BCN), Internet service provider(KT, SKT, LG) has converted to Internet phone telephone network to all government agencies. In addition, members of the public also have an Internet phone service that you are using. In this paper, we analyze the hacking attack on IP-PBX in the IETF SIP-based that are used in Internet telephony, to the study. The test bed is constructed in the same way as the Internet telephone system to perform studies carried hacking attacks on IP-PBX, analyze the results and to extract evidence forensics. When used in crime by hacking the Internet telephone, we propose a method which can be used as evidence in forensic having legal effect.

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Shooting sound analysis using convolutional neural networks and long short-term memory (합성곱 신경망과 장단기 메모리를 이용한 사격음 분석 기법)

  • Kang, Se Hyeok;Cho, Ji Woong
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.3
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    • pp.312-318
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    • 2022
  • This paper proposes a model which classifies the type of guns and information about sound source location using deep neural network. The proposed classification model is composed of convolutional neural networks (CNN) and long short-term memory (LSTM). For training and test the model, we use the Gunshot Audio Forensic Dataset generated by the project supported by the National Institute of Justice (NIJ). The acoustic signals are transformed to Mel-Spectrogram and they are provided as learning and test data for the proposed model. The model is compared with the control model consisting of convolutional neural networks only. The proposed model shows high accuracy more than 90 %.

An Effective Information Visualization Technique for Intrusion Detection: Hyperbolic View Intrusion Visualizer

  • Jeong, Yun-Seok;Myung, Ro-Hae
    • Journal of the Ergonomics Society of Korea
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    • v.30 no.2
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    • pp.319-330
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    • 2011
  • In computer forensics investigation, the investigators collect, protect, analyze and interpret massive amount of data which were used in cyber crime. However, due to its huge amount of information, it takes a great deal of time and errors often result even when they use forensics investigation tool in the process. The information visualization techniques will greatly help to improve the information processing ability of human when they deal with the overwhelming amount of data and have to find out significant information in it. The importance of Intrusion Detection System(IDS) among network forensics is being emphasized in computer forensics. In this study, we apply the information visualization techniques which are proposed to be a great help to IDS and carry out the usability test to find out the most effective information visualization techniques for IDS.

FLORA: Fuzzy Logic - Objective Risk Analysis for Intrusion Detection and Prevention

  • Alwi M Bamhdi
    • International Journal of Computer Science & Network Security
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    • v.23 no.5
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    • pp.179-192
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    • 2023
  • The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

Style Synthesis of Speech Videos Through Generative Adversarial Neural Networks (적대적 생성 신경망을 통한 얼굴 비디오 스타일 합성 연구)

  • Choi, Hee Jo;Park, Goo Man
    • KIPS Transactions on Software and Data Engineering
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    • v.11 no.11
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    • pp.465-472
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    • 2022
  • In this paper, the style synthesis network is trained to generate style-synthesized video through the style synthesis through training Stylegan and the video synthesis network for video synthesis. In order to improve the point that the gaze or expression does not transfer stably, 3D face restoration technology is applied to control important features such as the pose, gaze, and expression of the head using 3D face information. In addition, by training the discriminators for the dynamics, mouth shape, image, and gaze of the Head2head network, it is possible to create a stable style synthesis video that maintains more probabilities and consistency. Using the FaceForensic dataset and the MetFace dataset, it was confirmed that the performance was increased by converting one video into another video while maintaining the consistent movement of the target face, and generating natural data through video synthesis using 3D face information from the source video's face.

A Study on Detection Technique of Anomaly Signal for Financial Loan Fraud Based on Social Network Analysis (소셜 네트워크 분석 기반의 금융회사 불법대출 이상징후 탐지기법에 관한 연구)

  • Wi, Choong-Ki;Kim, Hyoung-Joong;Lee, Sang-Jin
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.22 no.4
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    • pp.851-868
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    • 2012
  • After the financial crisis in 2008, the financial market still seems to be unstable with expanding the insolvency of the financial companies' real estate project financing loan in the aftermath of the lasted real estate recession. Especially after the illegal actions of people's financial institutions disclosed, while increased the anxiety of economic subjects about financial markets and weighted in the confusion of financial markets, the potential risk for the overall national economy is increasing. Thus as economic recession prolongs, the people's financial institutions having a weak profit structure and financing ability commit illegal acts in a variety of ways in order to conceal insolvent assets. Especially it is hard to find the loans of shareholder and the same borrower sharing credit risk in advance because most of them usually use a third-party's name bank account. Therefore, in order to effectively detect the fraud under other's name, it is necessary to analyze by clustering the borrowers high-related to a particular borrower through an analysis of association between the whole borrowers. In this paper, we introduce Analysis Techniques for detecting financial loan frauds in advance through an analysis of association between the whole borrowers by extending SNA(social network analysis) which is being studied by focused on sociology recently to the forensic accounting field of the financial frauds. Also this technique introduced in this pager will be very useful to regulatory authorities or law enforcement agencies at the field inspection or investigation.

Deep Learning based HEVC Double Compression Detection (딥러닝 기술 기반 HEVC로 압축된 영상의 이중 압축 검출 기술)

  • Uddin, Kutub;Yang, Yoonmo;Oh, Byung Tae
    • Journal of Broadcast Engineering
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    • v.24 no.6
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    • pp.1134-1142
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    • 2019
  • Detection of double compression is one of the most efficient ways of remarking the validity of videos. Many methods have been introduced to detect HEVC double compression with different coding parameters. However, HEVC double compression detection under the same coding environments is still a challenging task in video forensic. In this paper, we introduce a novel method based on the frame partitioning information in intra prediction mode for detecting double compression in with the same coding environments. We propose to extract statistical feature and Deep Convolution Neural Network (DCNN) feature from the difference of partitioning picture including Coding Unit (CU) and Transform Unit (TU) information. Finally, a softmax layer is integrated to perform the classification of the videos into single and double compression by combing the statistical and the DCNN features. Experimental results show the effectiveness of the statistical and the DCNN features with an average accuracy of 87.5% for WVGA and 84.1% for HD dataset.

A Study on the Legal and Institutional Improvement Plan for Cyber Correspondence (사이버 대응태세 구축을 위한 법·제도적 개선방안 연구)

  • Lee, Yong Seok;Lim, Jong In
    • Convergence Security Journal
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    • v.19 no.1
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    • pp.61-66
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    • 2019
  • Cyber space is a place where free activities are guaranteed. However, it is also true that not all individuals and countries strive for peaceful cyberspace, and that there is a growing tendency to gain unfair advantage through this space. Therefore, the state should reform laws and institutions to keep cyberspace safe. By establishing the "Basic Law on Cyberspace" which includes the law of the state law on cyberspace, it is necessary to be able to recognize and respond to the direction of the national legal discipline on cyberspace. The development of digital forensics is an urgent task due to the rapid development of IT. However, if the law is delayed for various reasons, some of the existing laws should be amended to improve the stability of the law in accordance with the circumstances. To this end, it is necessary to revise the "Information and Communication Infrastructure Protection Act", "Information and Communication Network Enhancement and Information Protection Act", "Integrated Defense Law", "Establishment of Defense Information Infrastructure Infrastructure and Defense Information Resource Management Act".

A pilot study of an automated personal identification process: Applying machine learning to panoramic radiographs

  • Ortiz, Adrielly Garcia;Soares, Gustavo Hermes;da Rosa, Gabriela Cauduro;Biazevic, Maria Gabriela Haye;Michel-Crosato, Edgard
    • Imaging Science in Dentistry
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    • v.51 no.2
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    • pp.187-193
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    • 2021
  • Purpose: This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification. Materials and Methods: Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process. Results: Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients. Conclusion: Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.

Video Camera Model Identification System Using Deep Learning (딥 러닝을 이용한 비디오 카메라 모델 판별 시스템)

  • Kim, Dong-Hyun;Lee, Soo-Hyeon;Lee, Hae-Yeoun
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.8
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    • pp.1-9
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    • 2019
  • With the development of imaging information communication technology in modern society, imaging acquisition and mass production technology have developed rapidly. However, crime rates using these technology are increased and forensic studies are conducted to prevent it. Identification techniques for image acquisition devices are studied a lot, but the field is limited to images. In this paper, camera model identification technique for video, not image is proposed. We analyzed video frames using the trained model with images. Through training and analysis by considering the frame characteristics of video, we showed the superiority of the model using the P frame. Then, we presented a video camera model identification system by applying a majority-based decision algorithm. In the experiment using 5 video camera models, we obtained maximum 96.18% accuracy for each frame identification and the proposed video camera model identification system achieved 100% identification rate for each camera model.