2022 10th International Symposium on Digital Forensics and Security (ISDFS)最新文献_第5页

Development of a Forensic Toolkit for Small-Medium Size Business (SMB) 为中小型企业(SMB)开发取证工具包

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800828

Eduardo Martínez, C. Varol, N. Shashidhar, Van Vung Pham

引用次数: 0

Towards A Framework for Preprocessing Analysis of Adversarial Windows Malware 一种针对Windows恶意软件的预处理分析框架

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800812

N. D. Schultz, Adam Duby

引用次数: 1

IoT DDoS Traffic Detection Using Adaptive Heuristics Assisted With Machine Learning 使用自适应启发式辅助机器学习的物联网DDoS流量检测

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800786

Rani Al Rahbani, Jawad Khalife

引用次数: 1

A Novel Deep Feature Extraction Engineering for Subtypes of Breast Cancer Diagnosis: A Transfer Learning Approach 一种新的用于乳腺癌亚型诊断的深度特征提取方法:迁移学习方法

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800813

Bilyaminu Muhammad, F. Özkaynak, A. Varol, T. Tuncer

{"title":"A Novel Deep Feature Extraction Engineering for Subtypes of Breast Cancer Diagnosis: A Transfer Learning Approach","authors":"Bilyaminu Muhammad, F. Özkaynak, A. Varol, T. Tuncer","doi":"10.1109/ISDFS55398.2022.9800813","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800813","url":null,"abstract":"Feature extraction from histological images is a challenging part of computer-aided detection of breast cancer. For this research, we present a novel technique for deep feature extraction for breast cancer diagnosis subtypes based on a transfer learning approach using the BreaKhis dataset. This approach consists of five phases: feature extraction, concatenation, transformation, selection, and classification. In the first phase, nineteen pre-trained convolutional neural networks were used as feature extractors to extract features from the input images. A Support Vector Machine was used at the feature extraction phase to calculate the misclassification rate of each feature generated by the pre-trained networks used. The feature extraction results showed that the two networks achieved the highest accuracy on the dataset and outperformed the other networks. The two networks considered were selected and connected to create the DRNet model, combining the pre-trained networks ResNet50 and DenseNet201. The extracted features were decomposed into five sub-hand low-level features using a multilevel discrete wavelet transform in the transformation phase. An iterative neighborhood component analyzer was used to select the minimum number of features needed in the classification phase. A cubic support vector machine was used as a classifier in the final phase. Average classification accuracy of 98.61%, 98.04%, 97.68%, and 97.71% for the 40×, 100×, 200×, and 400× magnification levels, respectively, was achieved.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"4 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":"128483315","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: 2

Outlier detection to Secure Wireless Sensor Networks Based on iForest 基于ifforest的无线传感器网络安全异常点检测

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800792

Muhammad R. Ahmed, T. Myo, Badar Al Baroomi

引用次数: 0

CyberSoc Implementation Plan 网络社会实施计划

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800819

Mário Saraiva, N. Coelho

{"title":"CyberSoc Implementation Plan","authors":"Mário Saraiva, N. Coelho","doi":"10.1109/ISDFS55398.2022.9800819","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800819","url":null,"abstract":"Cybersecurity operations centers (CyberSoc) should have all they need to defend the ever-changing information technology (IT) company today. This comprises a diverse set of advanced detection and prevention tools, a virtual sea of cyber intelligence reporting, and access to a rapidly growing pool of experienced IT experts. Despite this, most CyberSoc fail to keep the enemy (even the most inexperienced) out of the enterprise. The odds are stacked heavily against the defense. While the attacker only needs to identify one way in, the defenders must protect all entry points, restrict and analyze damage, and locate and eliminate adversary points of presence in business systems. Furthermore, cybersecurity professionals are increasingly aware that capable adversaries may and will get permanent access to company networks. As if the situation wasn’t horrible enough, we are frequently our own worst enemies. Many CyberSocs devote more time and effort to dealing with politics and human concerns than to detecting and responding to cyber threats. All too frequently, CyberSocs are established and run with a sole focus on technology, neglecting to address people and process challenges. The major goal of this work is to provide as a guide for when a CyberSoc implementation is required.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"35 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":"127885810","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

ISDFS 2022 Cover Page ISDFS 2022封面页

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/isdfs55398.2022.9800783

引用次数: 0

Phishing URL classification using Extra-Tree and DNN 利用Extra-Tree和DNN进行网络钓鱼URL分类

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800795

Habiba Bouijij, A. Berqia, H. Saliah-Hassane

引用次数: 1

An Intelligent System to Detect Drowsiness at the Wheel 一种智能系统来检测驾驶时的困倦

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800836

A. Antunes, Miguel V. P. R. Meneses, Joaquim Gonçalves, A. C. Braga

{"title":"An Intelligent System to Detect Drowsiness at the Wheel","authors":"A. Antunes, Miguel V. P. R. Meneses, Joaquim Gonçalves, A. C. Braga","doi":"10.1109/ISDFS55398.2022.9800836","DOIUrl":"https://doi.org/10.1109/ISDFS55398.2022.9800836","url":null,"abstract":"Drowsiness is one of the causes of road accidents with higher fatality rate and even though it has been studied over the years, there is still no solution that can mitigate this problem from a low cost perspective. Thereby, artificial intelligence will be used to predict drowsiness and develop a non-intrusive and low-cost system using a wearable device that is capable of alerting the driver before they exhibit any signs. The aim of this work is to present the results achieved in the first stage, where driving simulations were conducted and video information, subjective reporting and physiological-based data were collected. Drowsiness levels and eye blinks were extracted from analysis of the participants’ videos, in addition to simulation based information, such as speed variations and road accidents, allowing to classify the driver’s state. Multivariate statistical process control was the method implemented, considering the Heart Rate Variability (HRV) and Electroencephalography (EEG) information. Using this methodology, it was able to detect the transition between the different drowsiness phases. Although the results are promising, there are still missing analyses, such as the application of Machine Learning (ML) techniques to classify the drowsy state and identify the best subset of features capable of detecting this state. Besides this, data analysis must be done to understand how drowsiness can be predicted. Finally, the proposed solution must be implemented in a real environment.","PeriodicalId":114335,"journal":{"name":"2022 10th International Symposium on Digital Forensics and Security (ISDFS)","volume":"17 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":"123955273","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: 3

Process standardization: the driving factor for bringing artificial intelligence and management analytics to SMEs 流程标准化:将人工智能和管理分析引入中小企业的驱动因素

2022 10th International Symposium on Digital Forensics and Security (ISDFS) Pub Date : 2022-06-06 DOI: 10.1109/ISDFS55398.2022.9800804

Joaquim P. Silva, Joaquim Gonçalves

引用次数: 2