2021 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第3页

Adversarial Deep Reinforcement Learning Enabled Threat Analytics Framework for Constrained Spatio-Temporal Movement Intelligence Data 基于对抗性深度强化学习的受限时空运动智能数据威胁分析框架

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624731

Jalal Ghadermazi, Soumyadeep Hore, Dinesh Sharma, Ankit Shah

引用次数: 1

Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach 针对恶意软件检测器的单发黑盒对抗性攻击:一种因果语言模型方法

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624787

J. Hu, Mohammadreza Ebrahimi, Hsinchun Chen

{"title":"Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach","authors":"J. Hu, Mohammadreza Ebrahimi, Hsinchun Chen","doi":"10.1109/ISI53945.2021.9624787","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624787","url":null,"abstract":"Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security concerns. Generating such adversarial variants by the defender is crucial to improving the resistance of DL-based malware detectors against them. This necessity has given rise to an emerging stream of machine learning research, Adversarial Malware example Generation (AMG), which aims to generate evasive adversarial malware variants that preserve the malicious functionality of a given malware. Within AMG research, black-box method has gained more attention than white-box methods. However, most black-box AMG methods require numerous interactions with the malware detectors to generate adversarial malware examples. Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth. In this study, we show that a novel DL-based causal language model enables single-shot evasion (i.e., with only one query to malware detector) by treating the content of the malware executable as a byte sequence and training a Generative Pre-Trained Transformer (GPT). Our proposed method, MalGPT, significantly outperformed the leading benchmark methods on a real-world malware dataset obtained from VirusTotal, achieving over 24.51% evasion rate. MalGPT enables cybersecurity researchers to develop advanced defense capabilities by emulating large-scale realistic AMG.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114929922","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

A Central Opinion Extraction Framework for Boosting Performance on Sentiment Analysis 一种提高情感分析性能的中心意见抽取框架

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624682

Yuan Tian, Nan Xu, W. Mao, Yin Luo

{"title":"A Central Opinion Extraction Framework for Boosting Performance on Sentiment Analysis","authors":"Yuan Tian, Nan Xu, W. Mao, Yin Luo","doi":"10.1109/ISI53945.2021.9624682","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624682","url":null,"abstract":"With the rapid development of the Internet, mining opinions and emotions from the explosive growth of user-generated content is a key field of social media analysis. However, the expression forms of the central opinion which strongly expresses the essential points and converges the main sentiments of the overall document are diverse in practice, such as sequential sentences, a sentence fragment, or an individual sentence. Previous research studies on sentiment analysis based on document level and sentence level fail to deal with this actual situation uniformly. To address this issue, we propose a Central Opinion Extraction (COE) framework to boost performance on sentiment analysis with social media texts. Our framework first extracts a span-level central opinion text, which expresses the essential opinion related to sentiment representation among the whole text, and then uses extracted textual span to boost the performance of sentiment classifiers. The experimental results on a public dataset show the effectiveness of our framework for boosting the performance on document-level sentiment analysis task.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310406","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

Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection 信用卡欺诈检测的超级应用和电信替代数据源的特征级融合

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624796

Jaime D. Acevedo-Viloria, Sebastián Soriano Pérez, Jesus Solano, David Zarruk-Valencia, Fernando G. Paulin, Alejandro Correa Bahnsen

引用次数: 1

Distilling Contextual Embeddings Into A Static Word Embedding For Improving Hacker Forum Analytics 将上下文嵌入提取为静态词嵌入以改进黑客论坛分析

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624848

Benjamin Ampel, Hsinchun Chen

引用次数: 2

Boosting Hidden Graph Node Classification for Large Social Networks 大型社交网络中隐图节点分类的增强

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624788

Hanxuan Yang, Qingchao Kong, W. Mao, Lei Wang

{"title":"Boosting Hidden Graph Node Classification for Large Social Networks","authors":"Hanxuan Yang, Qingchao Kong, W. Mao, Lei Wang","doi":"10.1109/ISI53945.2021.9624788","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624788","url":null,"abstract":"Identifying hidden nodes in social networks is a critical issue in security-related applications. In contrast to the conventional node classification on graphs with all nodes being observable, it is more challenging to classify the hidden nodes that are unobservable during the training process, also known as the “inductive learning” in previous research. Existing approaches for inductive node classification mainly adopt graph neural network models to learn node representations. Although these methods are advantageous to modeling the topology of graph-structured data, they rely heavily on node features which may vary significantly in different specific application scenarios. In addition, the inherently changeable graph structure induced by hidden nodes may cause the over-fitting problem. To address the above issues and boost the performances of hidden node classification, we propose a deep generative model based on variational auto-encoders. Specifically, we design a novel graph neural network to aggregate the multi-hop neighbor information of each node. Meanwhile, to better utilize the graph structure information as a supplement to node features, we consider the heterogeneous node influences and introduce a gated attention mechanism using node degrees. Moreover, our proposed model can be trained by minibatches and thus is applicable to large social networks. We conduct experiments on four real-world datasets, and verify the effectiveness of our method for hidden graph node classification.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125665461","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

Intrusion Detection for Industrial Control Systems by Machine Learning using Privileged Information 基于特权信息的机器学习工业控制系统入侵检测

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624757

Moojan Pordelkhaki, S. Fouad, Mark Josephs

{"title":"Intrusion Detection for Industrial Control Systems by Machine Learning using Privileged Information","authors":"Moojan Pordelkhaki, S. Fouad, Mark Josephs","doi":"10.1109/ISI53945.2021.9624757","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624757","url":null,"abstract":"The continuous operation of an industrial process, such as water treatment or power generation, is governed by an Industrial Control System (ICS). Cyber-attacks on industrial networks are of growing concern because of the disruption they can cause, leading to loss of revenue, and the possibility of harm to workers, plant and surroundings. Operators therefore need a Network Intrusion Detection System (NIDS) to analyse industrial network traffic in real time for adversarial behaviour. Machine Learning (ML) is applicable to the problem of network intrusion detection. This paper investigates the possibility of training an ML-based NIDS for an ICS (specifically, the well-known Secure Water Treatment testbed) by combining network traffic data and physical process data. In the supplied dataset, data had already been labelled “according to normal and abnormal behaviours”; the labelling of data collected around the start and end of each attack was scrutinized and, where found to be problematic, labelled data were excluded in order to improve the effectiveness of supervised learning. The ML technique of “Learning using Privileged Information” was evaluated and found to be superior to six baseline ML algorithms trained on network traffic data alone.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115789433","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

Characterization of Domestic Violence through Self-disclosure in Social Media: A Case Study of the Time of COVID-19 社交媒体自我披露对家庭暴力的表征——以COVID-19时期为例

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624676

A. Aldkheel, Lina Zhou, Kanlun Wang

{"title":"Characterization of Domestic Violence through Self-disclosure in Social Media: A Case Study of the Time of COVID-19","authors":"A. Aldkheel, Lina Zhou, Kanlun Wang","doi":"10.1109/ISI53945.2021.9624676","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624676","url":null,"abstract":"Domestic violence (DV) can lead to physical, psychological, and/or emotional consequences for its victims. Social media provides a new platform for DV victims to share their personal experiences and seek needed support. The anonymity of social media can potentially provide comfort and safety for victims to disclose their victimization experience. Despite a few efforts in detecting DV from social media, they have focused on differentiating DV-from non-DV-related content, or classifying DV-related content into a few general categories. By conducting an in-depth analysis of the content of DV self-disclosure in social media, this study characterizes DV in multiple aspects for the first time, including victim, perpetrator, relationship, and abuse. Moreover, it identifies the attributes to describe each aspect in detail. Furthermore, we use the social media data generated during the COVID-19 pandemic as a case study to understand the patterns of DV. The research findings of this study have implications for increasing the awareness of DV and designing support for DV victims.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121626159","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

Extracting Impacts of Non-pharmacological Interventions for COVID-19 From Modelling Study 从模型研究中提取非药物干预对COVID-19的影响

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624840

Yunrong Yang, Zhidong Cao, Pengfei Zhao, D. Zeng, Qingpeng Zhang, Yin Luo

{"title":"Extracting Impacts of Non-pharmacological Interventions for COVID-19 From Modelling Study","authors":"Yunrong Yang, Zhidong Cao, Pengfei Zhao, D. Zeng, Qingpeng Zhang, Yin Luo","doi":"10.1109/ISI53945.2021.9624840","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624840","url":null,"abstract":"COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity, non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 abstracts of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116112105","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

Towards a Data-Driven Recommender System for Handling Ransomware and Similar Incidents 基于数据驱动的推荐系统处理勒索软件及类似事件

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624774

M. Husák

引用次数: 5