2022 10th International Symposium on Digital Forensics and Security (ISDFS)最新文献

An Improved Anonymous Identifier 改进的匿名标识符

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800823

Ray Kresman, L. Dunning, Jing-yan Lu

引用次数: 0

Vehicle Fatality Analysis by Gender using Predictive Analytics 使用预测分析按性别进行车辆死亡分析

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800820

Mena Youssef, Serkan Varol, Serkan Catma

引用次数: 1

DOTMUG: A Threat Model for Target Specific APT Attacks–Misusing Google Teachable Machine DOTMUG:针对特定目标的APT攻击的威胁模型——滥用Google可教机器

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800780

P. Charan, P. Anand, S. Shukla, N. Selvan, Hrushikesh Chunduri

{"title":"DOTMUG: A Threat Model for Target Specific APT Attacks–Misusing Google Teachable Machine","authors":"P. Charan, P. Anand, S. Shukla, N. Selvan, Hrushikesh Chunduri","doi":"10.1109/ISDFS55398.2022.9800780","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800780","url":null,"abstract":"Target specific malware is one of the major concerns for many global IT firms and government organizations. In recent times, state-sponsored Advanced Persistent Threat (APT) groups have evolved in developing more intelligent and targeted malware by misusing various legitimate services. This work sheds light on modeling a threat scenario to emphasize how targeted attacks are performed by misusing legitimate services (Google Teachable Machine in our scenario) for malicious purposes in establishing foothold, lateral movement, and data exfiltration phases of APT life cycle. As a proof of concept, we validate our threat model with five different experiments highlighting how an attacker can execute a personalized boot sector ransomware and fileless malware on a targeted individual in corporate networks. Furthermore, assuming the attacker has limited information regarding the target, we use sinGAN to generate synthetic image samples to train a model for identifying the targets. In addition, we present a correlation study between target prediction confidence and effective payload deployment for all experiments. In our observation, targeted file-less malware turned out to be quicker and pestilent, averaging 25.11 seconds to encrypt the whole disk with 80% target prediction confidence.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125887562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Direction Estimation of Drone Collision Using Optical Flow for Forensic Investigation 基于光流的无人机碰撞方向估计法证调查

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800790

A. Editya, T. Ahmad, H. Studiawan

引用次数: 2

Context-Aware Data Augmentation for Efficient Object Detection by UAV Surveillance 基于上下文感知数据增强的无人机监控高效目标检测

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800798

Yuri G. Gordienko, Oleksandr Rokovyi, Oleg Alienin, S. Stirenko

{"title":"Context-Aware Data Augmentation for Efficient Object Detection by UAV Surveillance","authors":"Yuri G. Gordienko, Oleksandr Rokovyi, Oleg Alienin, S. Stirenko","doi":"10.1109/ISDFS55398.2022.9800798","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800798","url":null,"abstract":"The problem of object detection by YOLOv4 deep neural network (DNN) is considered on Stanford drone dataset (SDD) with object classes (pedestrians, bicyclists, cars, skateboarders, golf carts, and buses) collected by Unmanned Aerial Vehicle (UAV) video surveillance. Some frames (images) with labels were extracted from videos of this dataset and structured in the open-access SDD frames (SDDF) version (https://www.kaggle.com/yoctoman/stanford-drone-dataset-frames). The context-aware data augmentation (CADA) was proposed to change bounding box (BB) sizes by some percentage of its width and height. To investigate the possible effect of the dataset labeling quality the \"dirty\" and \"clean\" dataset versions were prepared, which differ by the evaluation subset only. CADA procedures lead to significant improvement of performance by loss and mean average precision (mAP) that can be observed both for \"dirty\" and \"clean\" evaluation subsets in comparison to experiments without CADA. Moreover, CADA procedures allow to get the mAP values on the \"dirty\" (real) evaluation subset that can be similar (and for some classes higher even) to the mAP values on the \"clean\" (ground-truth - GT) evaluation subset without CADA procedures. This effect can be explained by increase of signal-to-noise ratios for object-to-background pairs after IN-like cropping CADA procedures and then by increase of variability of object-to-background pair after subsequent OUT-like enlarging CADA procedures. It should be noted the non-commutative nature of CADA-based retraining procedures because their reverse direction like first-OUT-then-IN CADA in contrast to first-IN-then-OUT CADA did not lead to such a big increase of mAP values. Several CADA-sequences were analyzed and the best strategy consists in first-IN-then-OUT CADA procedures, where the extent of decrease and increase of BBs width and height can be different for various applications and datasets.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123979562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human Activity Recognition: A review 人类活动识别:综述

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800781

João Gonçalo Pereira, Joaquim Gonçalves

引用次数: 0

Detecting Arabic YouTube Spam Using Data Mining Techniques 使用数据挖掘技术检测阿拉伯语YouTube垃圾邮件

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800840

Yahya M. Tashtoush, Areen Magableh, Omar Darwish, Lujain Smadi, Omar Alomari, Anood ALghazoo

引用次数: 3

A Novel Approach to Detect Fake News Using eXtreme Gradient Boosting 一种利用极端梯度增强检测假新闻的新方法

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800777

S. Reddy, Santanu Mandal, Varanasi L. V. S. K. B. Kasyap, K. AswathyR.

{"title":"A Novel Approach to Detect Fake News Using eXtreme Gradient Boosting","authors":"S. Reddy, Santanu Mandal, Varanasi L. V. S. K. B. Kasyap, K. AswathyR.","doi":"10.1109/ISDFS55398.2022.9800777","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800777","url":null,"abstract":"The usage of social media has expanded in recent years, allowing them to get news from around the world at any time. This in turn, is questioning the authenticity of the news that is being spread both globally and locally. Fake news such as misinformation, gossips is widely disseminated on social media having a negative impact on society and lives of the people. As a result, much study is being is carried out in order to detect them. The data can be clustered into smaller groups based on the type of news using a few learning approaches. A novel method has been proposed for prediction of the authenticity of the news of the LIAR dataset [1] using Logistic Regression and a boosting algorithm eXtreme Gradient Boosting (XGBoost) for efficacy, computational pace and performance of the model. This method detects fake news by analyzing the semantic and syntactic connections between sentences. Various graphs (like heat maps, bar charts) are plotted to show the distribution of the authenticity of news and also to compare the predicted result with the actual one. The proposed strategy addresses the effects of the hoax's global spread. People are hungry for information to defend themselves and others in a community where humans are confronting large-scale risks from harms. Some key traits such as Sentimental features, Content-based features, Frequency features, and Hybrid features (combinations of two or more features) are incorporated for early prediction of fake news spread via social media. The liar dataset is used to train the method and tested for accurate results. The experimental accuracy is found out to be 98%.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132282710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Transfer Learning based Classification of Plasmodium Falciparum Parasitic Blood Smear Images 基于迁移学习的恶性疟原虫寄生血涂片图像分类

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800796

Sai Dheeraj Gummadi, Anirban Ghosh, Yeswanth Vootla

引用次数: 1

Deep Learning based Lightweight Model for Seizure Detection using Spectrogram Images 基于深度学习的基于频谱图图像的癫痫检测轻量级模型

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800802

Mohd. Maaz Khan, Irfan Mabood Khan, Omar Farooq

{"title":"Deep Learning based Lightweight Model for Seizure Detection using Spectrogram Images","authors":"Mohd. Maaz Khan, Irfan Mabood Khan, Omar Farooq","doi":"10.1109/ISDFS55398.2022.9800802","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800802","url":null,"abstract":"Epilepsy is a severe neurological disorder, which is onset by the abrupt and erratic electrical gushing in the neurons. Epileptic seizures can be diagnosed by monitoring the brain’s electrical activity using Electroencephalogram (EEG) signals. Conventionally this analysis was done manually by neurologists and had various limitations, but now it is increasingly being automated to save time, minimize human errors and relieve the neurologists from excessive burden. In this study, the EEG signals are first converted into spectrograms. These spectrograms are then fed into the proposed Convolutional Neural Network (CNN) model to automatically learn the robust features and perform binary classification. The proposed CNN model, containing only 3.94 million parameters, obtained an accuracy of 90.9% and achieved precision, recall, and AUC of 91.1%, 93.5% and 97.9% respectively. This work is extended by applying transfer learning on four pre-trained networks VGG16, ResNet, DenseNet, and Inception using the same dataset. Among all these networks, DenseNet achieves the best performance having an accuracy of 92.6% followed by ResNet with an accuracy of 90.3%, Inception with an accuracy of 88.8%, and VGG16 having an accuracy of 88.5%. Although DenseNet achieves slightly better accuracy than the proposed CNN model, it contains almost twice the parameters (8.1 million) in the base model.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124299602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1