EAI Endorsed Trans. Security Safety最新文献_第7页

Toward A Network-Assisted Approach for Effective Ransomware Detection 一种有效的勒索软件检测的网络辅助方法

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.28-1-2021.168506

Tianrou Xia, Yuanyi Sun, Sencun Zhu, Z. Rasheed, K. Shafique

引用次数: 2

Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge 基于边缘网络频率估计的视频馈送认证

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.4-2-2021.168648

Deeraj Nagothu, Yu Chen, Alexander J. Aved, E. Blasch

引用次数: 21

A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks 物联网网络中基于入侵检测的机器学习研究综述

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.6-10-2021.171246

Hela Mliki, A. Kaceam, L. Chaari

{"title":"A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks","authors":"Hela Mliki, A. Kaceam, L. Chaari","doi":"10.4108/eai.6-10-2021.171246","DOIUrl":"https://doi.org/10.4108/eai.6-10-2021.171246","url":null,"abstract":"The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols. The IoT technologies are expected to o ﬀ er a new level of connectivity thanks to its smart devices able to enhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitive data and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of research and industrial concern. Indeed, threats against IoT devices and services could cause security breaches and data leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paper studied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approach could provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore, highlighted the related issues to develop secured and e ﬃ cient IoT services. It tried to allow a comprehensive review of IoT features and design. It mainly focused on intrusion detection based on the machine learning schema and built a taxonomy of di ﬀ erent IoT attacks and threats. This paper also compared between the di ﬀ erent intrusion detection techniques and established a taxonomy of machine leaning methods for intrusion detection solutions.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133899472","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: 6

Mapping of the Security Requirements of GDPR and NISD GDPR与NISD安全需求的对应关系

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.30-6-2020.166283

Najm-u Saqib, Vasileios Germanos, W. Zeng, L. Maglaras

引用次数: 2

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.14-9-2021.170956

Zhengping Luo, Tao Hou, Xiangrong Zhou, Hui Zeng, Zhuo Lu

引用次数: 2

Caching Techniques for Security Metadata in Integrity-Protected Fabric-Attached Memories 完整性保护结构附加存储器中安全元数据的缓存技术

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.13-7-2018.165516

Mazen Alwadi, Amro Awad

引用次数: 0

The performance analysis of public key cryptography-based authentication 基于公钥加密的认证性能分析

EAI Endorsed Trans. Security Safety Pub Date : 2018-05-15 DOI: 10.4108/eai.15-5-2018.154770

Kaiqi Xiong

引用次数: 1

NIS directive: The case of Greece 国情院指令:希腊的情况

EAI Endorsed Trans. Security Safety Pub Date : 2018-05-15 DOI: 10.4108/eai.15-5-2018.154769

L. Maglaras, G. Drivas, Kleanthis Noou, Stylianos Rallis

引用次数: 16

Human-centered strategies for cyber-physical systems security 以人为中心的网络物理系统安全策略

EAI Endorsed Trans. Security Safety Pub Date : 2018-05-15 DOI: 10.4108/eai.15-5-2018.154773

Ebrima N. Ceesay, K. Myers, P. Watters

引用次数: 3

Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach 基于非有效负载的攻击无关混淆改进网络入侵检测分类器:一种对抗方法

EAI Endorsed Trans. Security Safety Pub Date : 2018-05-07 DOI: 10.4108/eai.10-1-2019.156245

I. Homoliak, Martin Teknos, Martín Ochoa, Dominik Breitenbacher, S. Hosseini, P. Hanáček

{"title":"Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach","authors":"I. Homoliak, Martin Teknos, Martín Ochoa, Dominik Breitenbacher, S. Hosseini, P. Hanáček","doi":"10.4108/eai.10-1-2019.156245","DOIUrl":"https://doi.org/10.4108/eai.10-1-2019.156245","url":null,"abstract":"Machine-learning based intrusion detection classifiers are able to detect unknown attacks, but at the same time, they may be susceptible to evasion by obfuscation techniques. An adversary intruder which possesses a crucial knowledge about a protection system can easily bypass the detection module. The main objective of our work is to improve the performance capabilities of intrusion detection classifiers against such adversaries. To this end, we firstly propose several obfuscation techniques of remote attacks that are based on the modification of various properties of network connections; then we conduct a set of comprehensive experiments to evaluate the effectiveness of intrusion detection classifiers against obfuscated attacks. We instantiate our approach by means of a tool, based on NetEm and Metasploit, which implements our obfuscation operators on any TCP communication. This allows us to generate modified network traffic for machine learning experiments employing features for assessing network statistics and behavior of TCP connections. We perform the evaluation of five classifiers: Gaussian Naive Bayes, Gaussian Naive Bayes with kernel density estimation, Logistic Regression, Decision Tree, and Support Vector Machines. Our experiments confirm the assumption that it is possible to evade the intrusion detection capability of all classifiers trained without prior knowledge about obfuscated attacks, causing an exacerbation of the TPR ranging from 7.8% to 66.8%. Further, when widening the training knowledge of the classifiers by a subset of obfuscated attacks, we achieve a significant improvement of the TPR by 4.21% - 73.3%, while the FPR is deteriorated only slightly (0.1% - 1.48%). Finally, we test the capability of an obfuscations-aware classifier to detect unknown obfuscated attacks, where we achieve over 90% detection rate on average for most of the obfuscations.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314164","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: 26