2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)最新文献_第2页

VDBWGDL: Vulnerability Detection Based On Weight Graph And Deep Learning 基于权图和深度学习的漏洞检测

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00039

Xin Zhang, Hongyu Sun, Zhipeng He, Mianxue Gu, Jingyu Feng, Yuqing Zhang

{"title":"VDBWGDL: Vulnerability Detection Based On Weight Graph And Deep Learning","authors":"Xin Zhang, Hongyu Sun, Zhipeng He, Mianxue Gu, Jingyu Feng, Yuqing Zhang","doi":"10.1109/dsn-w54100.2022.00039","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00039","url":null,"abstract":"Vulnerability detection has always been an essential part of maintaining information security, and the existing work can significantly improve the performance of vulnerability detection. However, due to the differences in representation forms and deep learning models, various methods still have some limitations. In order to overcome this defect, We propose a vulnerability detection method VDBWGDL, based on weight graphs and deep learning. Firstly, it accurately locates vulnerability-sensitive keywords and generates variant codes that satisfy vulnerability trigger logic and programmer programming style through code variant methods. Then, the control flow graph is sliced for vulnerable code keywords and program critical statements. The code block is converted into a vector containing rich semantic information and input into the weight map through the deep learning model. According to specific rules, different weights are set for each node. Finally, the similarity is obtained through the similarity comparison algorithm, and the suspected vulnerability is output according to different thresholds. VDBWGDL improves the accuracy and F1 value by 3.98% and 4.85% compared with four state-of-the-art models. The experimental results prove the effectiveness of VDBWGDL.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105790","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

A Dataset of Linux Failure Data for Dependability Evaluation and Improvement 用于可靠性评估和改进的Linux故障数据集

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00024

João R. Campos, Ernesto Costa, M. Vieira

{"title":"A Dataset of Linux Failure Data for Dependability Evaluation and Improvement","authors":"João R. Campos, Ernesto Costa, M. Vieira","doi":"10.1109/dsn-w54100.2022.00024","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00024","url":null,"abstract":"Software systems are now used to execute critical tasks on a daily basis. As a result, unhandled or uncontrolled failures at runtime may lead to non-negligible risks or losses. To mitigate this, considerable effort and resources have been dedicated to assessing and improving the dependability of such systems. However, researching novel techniques to develop more dependable systems requires access to rich and detailed data. As data from real systems are not typically available, researchers often look for alternative processes, such as fault injection, to generate realistic synthetic data. As this requires considerable effort and expertise, researchers frequently rely on outdated datasets or develop simplified processes to collect data, eventually compromising the validation and development of their methods. This paper presents, discusses, and makes available a large failure dataset collected from an up-to-date Linux kernel through fault injection. It provides a detailed characterization of the target system by continuously monitoring hundreds of system metrics and various system logs throughout the experiments. Ultimately, the goal is to provide a reliable, well-defined, and properly generated dataset that can be used to research techniques to support the development of more dependable systems.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136497","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

Towards Building Resilient Ensembles against Training Data Faults 针对训练数据错误构建弹性集成

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00020

Abraham Chan, A. Gujarati, K. Pattabiraman, S. Gopalakrishnan

引用次数: 0

A Robust Framework for Adaptive Selection of Filter Ensembles to Detect Adversarial Inputs 一种自适应选择滤波器组合检测对抗输入的鲁棒框架

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00019

Arunava Roy, D. Dasgupta

{"title":"A Robust Framework for Adaptive Selection of Filter Ensembles to Detect Adversarial Inputs","authors":"Arunava Roy, D. Dasgupta","doi":"10.1109/dsn-w54100.2022.00019","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00019","url":null,"abstract":"Existing defense strategies against adversarial attacks (AAs) on AI/ML are primarily focused on examining the input data streams using a wide variety of filtering techniques. For instance, input filters are used to remove noisy, misleading, and out-of-class inputs along with a variety of attacks on learning systems. However, a single filter may not be able to detect all types of AAs. To address this issue, in the current work, we propose a robust, transferable, distribution-independent, and cross-domain supported framework for selecting Adaptive Filter Ensembles (AFEs) to minimize the impact of data poisoning on learning systems. The optimal filter ensembles are determined through a Multi-Objective Bi-Level Programming Problem (MOBLPP) that provides a subset of diverse filter sequences, each exhibiting fair detection accuracy. The proposed framework of AFE is trained to model the pristine data distribution to identify the corrupted inputs and converges to the optimal AFE without vanishing gradients and mode collapses irrespective of input data distributions. We presented preliminary experiments to show the proposed defense outperforms the existing defenses in terms of robustness and accuracy.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131123251","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

Data-Centric Analysis of Compound Threats to Critical Infrastructure Control Systems 关键基础设施控制系统复合威胁的数据中心分析

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00022

Sahiti Bommareddy, Benjamin Gilby, Maher Khan, Imes Chiu, M. Panteli, J. Lindt, Linton Wells, Y. Amir, Amy Babay

引用次数: 0

A Two-Layer Soft-Voting Ensemble Learning Model For Network Intrusion Detection 网络入侵检测的两层软投票集成学习模型

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00034

Wenbin Yao, Longcan Hu, Yingying Hou, Xiaoyong Li

{"title":"A Two-Layer Soft-Voting Ensemble Learning Model For Network Intrusion Detection","authors":"Wenbin Yao, Longcan Hu, Yingying Hou, Xiaoyong Li","doi":"10.1109/dsn-w54100.2022.00034","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00034","url":null,"abstract":"Network intrusion detection is a real-time technology to protect the network from attack, which plays a major role in the server system and network security. However, network intrusion detection still faces multiple challenges, such as inconsistent data distribution between training and testing dataset, imbalanced data categories and low accuracy rate. To solve these problems, a two-layer soft-voting ensemble learning model with RF, lightGBM and XGBoost as base classifiers is proposed in this paper. Firstly, the model uses the adversarial validate algorithm to test the consistency of data distribution in training and testing dataset to determine whether the dataset needs re-splitting. Secondly, the model adopts the Synthetic Minority Oversampling Technique (SMOTE) to synthesize samples of minority classes, which helps improve the accuracy rate of minority classes. Finally, the experimental results show that the soft-voting ensemble learning model has a higher accuracy rate in both binary and multi-classification than other single models, which proves to be both feasible and efficient. In particular, the recall rate of DoS, ShellCode, Worms and Reconnaissance is significantly increased in multi-classification.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126691755","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

Privacy Leakage Analysis for Colluding Smart Apps 串通智能应用的隐私泄露分析

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00025

Junzhe Wang, Lannan Luo

{"title":"Privacy Leakage Analysis for Colluding Smart Apps","authors":"Junzhe Wang, Lannan Luo","doi":"10.1109/dsn-w54100.2022.00025","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00025","url":null,"abstract":"The rapid proliferation of Internet-of-Things (IoT) has advanced the development of smart environments. By installing smart apps on IoT platforms, users can integrate IoT devices for convenient automation. As smart apps are exposed to a myriad of sensitive data from devices, one severe concern is about the privacy of these digitally augmented spaces. The recent work SAINT [1] has been proposed to detect sensitive data flows in individual smart apps using taint analysis. But it has high false positives and false negatives due to inappropriate consideration of taint seeds and taint sinks.One important security issue ignored by existing work is that the IoT platform supports parent-child smart apps. Their ability to communicate, however, has a negative effect on security. We call the parent-child smart apps colluding smart apps. Unfortunately, no tool exists to detect smart app collusion. We propose PDColA, which addresses the limitations of SAINT, and more importantly, can detect privacy leakages by colluding smart apps. The evaluation results show that PDColA achieves higher accuracies than SAINT in detecting privacy leakages by individual smart apps, and is effective to detect privacy leakages by colluding smart apps.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125460203","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

SbrPBert: A BERT-Based Model for Accurate Security Bug Report Prediction 基于bert的安全漏洞报告准确预测模型

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00030

Xudong Cao, Tianwei Liu, Jiayuan Zhang, Mengyue Feng, Xin Zhang, Wanying Cao, Hongyu Sun, Yuqing Zhang

{"title":"SbrPBert: A BERT-Based Model for Accurate Security Bug Report Prediction","authors":"Xudong Cao, Tianwei Liu, Jiayuan Zhang, Mengyue Feng, Xin Zhang, Wanying Cao, Hongyu Sun, Yuqing Zhang","doi":"10.1109/dsn-w54100.2022.00030","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00030","url":null,"abstract":"Bidirectional Encoder Representation from Transformers (Bert) has achieved impressive performance in several Natural Language Processing (NLP) tasks. However, there has been limited investigation on its adaptation guidelines in specialized fields. Here we focus on the software security domain. Early identification of security-related reports in software bug reports is one of the essential means to prevent security accidents. However, the prediction of security bug reports (SBRs) is limited by the scarcity and imbalance of samples in this field and the complex characteristics of SBRs. So motivated, we constructed the largest dataset in this field and proposed a Security Bug Report Prediction Model Based on Bert (SbrPBert). By introducing a layer-based learning rate attenuation strategy and a fine-tuning method for freezing some layers, our model outperforms the baseline model on both our dataset and other small-sample datasets. This means the practical value of the model in BUG tracking systems or projects that lack samples. Moreover, our model has detected 56 hidden vulnerabilities through deployment on the Mozilla and RedHat projects so far.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126500833","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

An Overview of Sybil Attack Detection Mechanisms in VFC VFC中Sybil攻击检测机制综述

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00028

Haonan Yang, Yongchao Zhong, Bo Yang, Yiyu Yang, Zifeng Xu, Longjuan Wang, Yuqing Zhang

引用次数: 6

A Practical Security Evaluation of a Moving Target Defence against Multi-Phase Cyberattacks 针对多阶段网络攻击的移动目标防御的实用安全评估

2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) Pub Date : 2022-06-01 DOI: 10.1109/dsn-w54100.2022.00026

Tina Moghaddam, Minjune Kim, Jin-Hee Cho, Hyuk-Soon Lim, T. Moore, Frederica Free-Nelson, Dan Dongseong Kim

{"title":"A Practical Security Evaluation of a Moving Target Defence against Multi-Phase Cyberattacks","authors":"Tina Moghaddam, Minjune Kim, Jin-Hee Cho, Hyuk-Soon Lim, T. Moore, Frederica Free-Nelson, Dan Dongseong Kim","doi":"10.1109/dsn-w54100.2022.00026","DOIUrl":"https://doi.org/10.1109/dsn-w54100.2022.00026","url":null,"abstract":"Moving Target Defence (MTD) is a state-of-art defence mechanism as it proactively changes attack surfaces against cyberattacks. The theoretical security effectiveness of MTD techniques need to be validated with experimental evidence. Previous work in evaluating the effectiveness of virtual IP-shuffling MTD techniques mostly focused on the reconnaissance phase of cyberattacks, and used theoretical modelling or simulated and emulated networks to conduct the evaluation. These types of evaluations did not account for realistic network conditions or consider the effect on the attacker’s behaviour. In this paper, we present a practical evaluation of a virtual IP-shuffling MTD technique in a software define networking (SDN) testbed, with attacks based on the first three phases defined in the cyber kill chain, and consider a possible response by the attacker. This work considers two types of attackers: Dummy attacker and Adjusting attacker. A dummy attacker performs attacks consecutively with no knowledge or consideration about the MTD on the system, whereas an adjusting attacker is aware of the network using a time based MTD job management strategy and can adjust their approach accordingly. The effectiveness of attacks are analysed overall and across the three phases, and compared to the expectation. The results validate the effectiveness of the MTD technique, show its utility extends beyond just the reconnaissance phase, and demonstrate that the attacker can adjust their approach if they are aware of the MTD technique being used in order to increase their success rate.","PeriodicalId":349937,"journal":{"name":"2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114603982","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