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A Novel Malware Classification Method Based on Memory Image Representation 一种基于记忆图像表示的恶意软件分类新方法
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10217992
Wenjie Liu, Liming Wang
{"title":"A Novel Malware Classification Method Based on Memory Image Representation","authors":"Wenjie Liu, Liming Wang","doi":"10.1109/ISCC58397.2023.10217992","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10217992","url":null,"abstract":"Malware classification methods based on memory image representation have received increasing attention. However, the characteristics of the memory management mechanism and efficiency of the classification model are not well considered in previous works, which hinders the classifier from extracting high-quality features and consequently results in poor performance. Motivated by this, we propose a novel malware classification method. First, we add an Efficient Convolutional Block Attention Module (E-CBAM) to select important features with fewer parameters and less computational cost. Then, we integrate our attention module into a pre-trained EfficientNet-B0 to extract features efficiently. Moreover, data augmentation and label smoothing are adopted to mitigate model overfitting. Finally, extensive experiments on a realistic dataset testify to the performance and superiority of our method in both known and unknown malware classification.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115450566","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
Docflow: Supervised Multi-Method Document Anonymization Engine Docflow:监督多方法文档匿名化引擎
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218224
Gabriele Morabito, Valeria Lukaj, Armando Ruggeri, M. Fazio, Maria Annunziata Astone, M. Villari
{"title":"Docflow: Supervised Multi-Method Document Anonymization Engine","authors":"Gabriele Morabito, Valeria Lukaj, Armando Ruggeri, M. Fazio, Maria Annunziata Astone, M. Villari","doi":"10.1109/ISCC58397.2023.10218224","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218224","url":null,"abstract":"Nowadays the process of anonymization of documents has been the subject of several studies and debates. By anonymization of documents, we mean the process of replacing sensitive data in order to preserve the confidentiality of documents without altering their content. In this work, we introduce Docflow, an open-source document anonymization engine capable of anonymizing documents based on specific filters chosen by the user. We applied Docflow to anonymize a set of legal documents and performed a processing performance analysis. By providing a Markdown input file to be anonymized, Docflow is able to redact all information according to users' choices, preserving the document content. Docflow will be integrated with NLP algorithms for the generation of the Markdown source file starting from documents already processed in different formats, but always with human supervision in the loop.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117167715","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
Natural Face Anonymization via Latent Space Layers Swapping 通过潜在空间层交换的自然人脸匿名化
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218009
Emna BenSaid, Mohamed Neji, A. Alimi
{"title":"Natural Face Anonymization via Latent Space Layers Swapping","authors":"Emna BenSaid, Mohamed Neji, A. Alimi","doi":"10.1109/ISCC58397.2023.10218009","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218009","url":null,"abstract":"Machine learning is widely recognized as a key driver of technological progress. Artificial Intelligence (AI) applications that interact with humans require access to vast quantities of human image data. However, the use of large, real-world image datasets containing faces raises serious concerns about privacy. In this paper, we examine the issue of anonymizing image datasets that include faces. Our approach modifies the facial features that contribute to personal identification, resulting in an altered facial appearance that conceals the person's identity. This is achieved without compromising other visual features such as posture, facial expression, and hairstyle while maintaining a natural-looking appearance. Finally, Our method offers adjustable levels of privacy, computationally efficient, and has demonstrated superior performance compared to existing methods.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117261791","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
WELID: A Weighted Ensemble Learning Method for Network Intrusion Detection 基于加权集成学习的网络入侵检测方法
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218079
Yuanchen Gao, Guosheng Xu, Guoai Xu
{"title":"WELID: A Weighted Ensemble Learning Method for Network Intrusion Detection","authors":"Yuanchen Gao, Guosheng Xu, Guoai Xu","doi":"10.1109/ISCC58397.2023.10218079","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218079","url":null,"abstract":"The requirements for intrusion detection technology are getting higher and higher, with the rapid expansion of network applications. There have been many studies on intrusion detection, however, the accuracy of these models is not high enough and time-consuming, making them unavailable. In this paper, we propose a novel weighted ensemble learning method for network intrusion detection (WELID). Firstly, data preprocessing and feature selection algorithms are used to filter out some redundant and unrelated features. Next, anomaly detection is performed on the dataset using different base classifiers, and a layered ten-fold cross-validation method is used to prevent program overfitting. Then, the best classifiers are selected for the use of a multi-classifier fusion algorithm based on probability-weighted voting. We compare the proposed model with lots of efficient classifiers and state-of-the-art models for intrusion detection. The results show that the proposed model is superior to these models in terms of accuracy and time consumption.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127101878","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
BPCluster: An Anomaly Detection Algorithm for RFID Trajectory Based on Probability 基于概率的RFID轨迹异常检测算法
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218107
Fei Liang, Siye Wang, Ziwen Cao, Yue Feng, Shang Jiang, Yanfang Zhang
{"title":"BPCluster: An Anomaly Detection Algorithm for RFID Trajectory Based on Probability","authors":"Fei Liang, Siye Wang, Ziwen Cao, Yue Feng, Shang Jiang, Yanfang Zhang","doi":"10.1109/ISCC58397.2023.10218107","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218107","url":null,"abstract":"Indoor public places are facing more and more security risks, and need to be monitored to find potential anomalies. Benefiting from the advantages of low cost and high privacy, RFID is widely used in indoor monitoring. At present, it has become a common solution to construct the RFID raw data into time sequence trajectory, and then perform preprocessing and cluster analysis. However, there are redundant and uncertain factors in the RFID raw data, which affect the efficiency of anomaly detection. In this paper, we propose BPCluster, a probabilistic-based RFID trajectory anomaly detection algorithm for indoor RFID trajectories. The algorithm incorporates a probabilistic trajectory model, which reduces the redundancy and uncertainty through the context information of trajectories, and then clusters trajectories by the improved LCS algorithm to find abnormal trajectories. Experiments show that BPCluster has better performance in effectiveness and environmental adaptability, and the average accuracy in various environments reaches 91%.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127150256","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
Aggregating Multiple Embeddings: A Novel Approach to Enhance Reliability and Reduce Complexity in Facial Recognition 聚合多个嵌入:一种提高人脸识别可靠性和降低复杂性的新方法
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218214
Houssam Benaboud, Walid Amara, Amal Ezzouhri, Fatima El Jaimi, Wiam Rabhi, Zakaria Charouh
{"title":"Aggregating Multiple Embeddings: A Novel Approach to Enhance Reliability and Reduce Complexity in Facial Recognition","authors":"Houssam Benaboud, Walid Amara, Amal Ezzouhri, Fatima El Jaimi, Wiam Rabhi, Zakaria Charouh","doi":"10.1109/ISCC58397.2023.10218214","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218214","url":null,"abstract":"Facial recognition is widely used, but the reliability of the embeddings extracted by most computer vision-based approaches is a challenge due to the high similarity among human faces and the effect of facial expressions and lighting. Our proposed approach aggregates multiple embeddings to generate a more robust reference for facial embedding comparison and explores the distances metrics to use in order to optimize the comparison efficiency while preserving complexity. We also apply our method to the state-of-the-art algorithm that extracts embeddings from faces in an image. The proposed approach was compared with several approaches. It optimizes the Resnet accuracy to 99.77%, Facenet to 99.79%, and Inception-ResnetV1 to 99.16%. Our approach preserves the inference time of the model while increasing its reliability since the number of comparisons is kept at a minimum. Our results demonstrate that our proposed approach offers an effective solution for addressing facial recognition in real-world environments.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127208392","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
Neonate Heart Rate Variability Monitoring Using Optical Wireless Link 利用光学无线链路监测新生儿心率变异性
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218225
A. Chehbani, S. Sahuguède, A. Julien-Vergonjanne
{"title":"Neonate Heart Rate Variability Monitoring Using Optical Wireless Link","authors":"A. Chehbani, S. Sahuguède, A. Julien-Vergonjanne","doi":"10.1109/ISCC58397.2023.10218225","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218225","url":null,"abstract":"In this work, we investigate the quality of heart rate variability (HRV) features extracted from electrocardiogram (ECG) signals transmitted by optical wireless communication (OWC). The proposed solution exploits infrared links between a transmitter placed on the chest of a newborn lying in a closed incubator and optical receivers installed on the ceiling of a neonatal intensive care unit. The specific environment and the corresponding transmission channel were modeled and simulated using the ray-tracing Monte Carlotechnique. Temporal HRV parameters were determined using the Pan-Tompkins algorithm and analyzed as a function of emitted optical power. The results obtained show that it is possible to guarantee good HRV measurements from ECG signals transmitted by OWC in the proposed context. An excellent quality of the HRV parameters could be obtained with an emitted optical power of [2.8-4.1] mW for the OOK modulation.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125186957","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
Color-coded Attribute Graph: Visual Exploration of Distinctive Traits of IoT-Malware Families 颜色编码属性图:物联网恶意软件家族特征的可视化探索
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10217974
Jiaxing Zhou, Tao Ban, Tomohiro Morikawa, Takeshi Takahashi, D. Inoue
{"title":"Color-coded Attribute Graph: Visual Exploration of Distinctive Traits of IoT-Malware Families","authors":"Jiaxing Zhou, Tao Ban, Tomohiro Morikawa, Takeshi Takahashi, D. Inoue","doi":"10.1109/ISCC58397.2023.10217974","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10217974","url":null,"abstract":"This study investigates the use of explainable artificial intelligence (XAI) to identify the unique features distinguishing malware families and subspecies. The proposed method, called the color-coded attribute graph (CAG), employs XAI and visualization techniques to create a visual representation of malware samples. The CAG utilizes the feature importance scores (ISs) obtained from a pre-trained classifier model and a scale function to normalize the scores for visualization. The approach assigns each family a representative color. The features are color-coded according to their relevance to the malware family. This work evaluates the proposed method on a dataset of 13,823 Internet of Things malware samples and compares two approaches for feature IS extraction using Linear Support Vector Machine and Local Interpretable Model-Agnostic Explanations. The experimental results demonstrate the effectiveness of the CAG in interpreting machine learning-based methods for malware detection and classification, leading to more accurate analyses.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126074709","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
A Transmission Power Distribution Method Based on Lyapunov for Scraper Chain Tension Monitoring Network 基于Lyapunov的刮板链张力监测网络传输功率分配方法
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218169
Xiaodong Yan, Gongbo Zhou, Ping Zhou, Wen Wang, Lianfeng Han, Zhenzhi He
{"title":"A Transmission Power Distribution Method Based on Lyapunov for Scraper Chain Tension Monitoring Network","authors":"Xiaodong Yan, Gongbo Zhou, Ping Zhou, Wen Wang, Lianfeng Han, Zhenzhi He","doi":"10.1109/ISCC58397.2023.10218169","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218169","url":null,"abstract":"Using WSN technology to monitor the tension of the scraper chain can determine whether there is chain jam, chain broken and other failures, but the scraper and scraper chain are constantly moving, making continuous monitoring of the tension of the scraper chain through the monitoring node with limited energy has become a challenge. To reduce node transmission power consumption and extend the life of the monitoring network, this paper first establishes a tension monitoring network model for scraper chains. Then, based on the motion characteristics of the scraper conveyor, a monitoring node transmission power allocation method based on Lyapunov optimization theory is proposed. The simulation results indicate that the proposed method can ensure the stability of the monitoring network data queue and minimize the transmission power of the monitoring nodes. Compared with the full power allocation method, the proposed TPAL method reduces transmission power consumption by more than 15.6%.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032250","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
Variational Auto-Encoder Model and Federated Approach for Non-Intrusive Load Monitoring in Smart Homes 智能家居非侵入式负荷监测的变分自编码器模型与联邦方法
2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10217998
Shamisa Kaspour, A. Yassine
{"title":"Variational Auto-Encoder Model and Federated Approach for Non-Intrusive Load Monitoring in Smart Homes","authors":"Shamisa Kaspour, A. Yassine","doi":"10.1109/ISCC58397.2023.10217998","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10217998","url":null,"abstract":"Non-Intrusive Load Monitoring (NILM) is a technique used for identifying individual appliances' energy consumption from a household's total power usage. This study examines a novel energy disaggregation model called Variational Auto-Encoder (VAE) with Federated Learning (FL). Specifically, VAE has a complex structure that resolves the issues in Short Sequence-to-Point (Short S2P) with fewer samples as input windows for each appliance. Short S2P cannot be generalized and might confront some challenges while disaggregating multi-state appliances. To this end, we examine a series of experiments using a real-life dataset of appliance-level power from the UK: UK-DALE. We also investigate additional protection of model parameters using Differential Privacy (DP). The findings show that FL with the VAE model achieves comparable performance to its centralized counterpart and improves all the metrics significantly compared to the Short S2P model.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116574283","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
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