IEEE Organizations related to Blacklisting

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Conferences related to Blacklisting

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2020 IEEE Symposium on Security and Privacy (SP)

Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2023 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2022 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2021 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2019 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2018 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2017 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for the presentation of developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.Papers offer novel research contributions in any aspect of computer security or electronic privacy. Papers may represent advances in the theory, design, implementation, analysis, or empirical evaluation of secure systems, either for general use or for specific application domains.

  • 2016 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for the presentation of developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.Papers offer novel research contributions in any aspect of computer security or electronic privacy. Papers may represent advances in the theory, design, implementation, analysis, or empirical evaluation of secure systems, either for general use or for specific application domains.

  • 2015 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for the presentation of developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.Papers offer novel research contributions in any aspect of computer security or electronic privacy. Papers may represent advances in the theory, design, implementation, analysis, or empirical evaluation of secure systems, either for general use or for specific application domains.

  • 2014 IEEE Symposium on Security and Privacy (SP)

    IEEE Symposium on Security and Privacy has been the premier forum for computer security research, presenting the latest developments and bringing together researchers and practitioners.

  • 2013 IEEE Symposium on Security and Privacy (SP) Conference dates subject to change

    IEEE Symposium on Security and Privacy has been the premier forum for computer security research, presenting the latest developments and bringing together researchers and practitioners.

  • 2012 IEEE Symposium on Security and Privacy (SP) Conference dates subject to change

    IEEE Symposium on Security and Privacy has been the premier forum for computer security research, presenting the latest developments and bringing together researchers and practitioners.

  • 2011 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2010 IEEE Symposium on Security and Privacy (SP)

    S&P is interested in all aspects of computer security and privacy.

  • 2009 IEEE Symposium on Security and Privacy (SP)

    The IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2008 IEEE Symposium on Security and Privacy (SP)

    Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

  • 2007 IEEE Symposium on Security and Privacy (SP)

    Research contributions in any aspect of computer security and electronic privacy including advances in the theory, design, implementation, analysis of empirical evaluation of secure systems.

  • 2006 IEEE Symposium on Security and Privacy (SP)

  • 2005 IEEE Symposium on Security and Privacy (SRSP)


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

IEEE INFOCOM solicits research papers describing significant and innovative researchcontributions to the field of computer and data communication networks. We invite submissionson a wide range of research topics, spanning both theoretical and systems research.


2019 15th IEEE International Workshop on Factory Communication Systems (WFCS)

WFCS is the largest IEEE conference especially dedicated to industrial communication systems and technologies. The aim of the WFCS series is to provide a forum for researchers, developers and practitioners to review and discuss most recent trends in the area and share innovative research directions


2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

Conference focused on wireless networked sensing systems.


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Periodicals related to Blacklisting

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Xplore Articles related to Blacklisting

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RRPhish: Anti-phishing via mining brand resources request

2018 IEEE International Conference on Consumer Electronics (ICCE), 2018

Although in recent years a variety of anti-phishing studies have been carried out, phishing fraud has become increasingly rampant. Especially with the popularity of electronic banking and mobile payment, phishing attacks have become more profitable. In this context, exploring efficient and practical anti-phishing technology is particularly necessary and urgent. In this paper, by analyzing the resources (CSS, JS, and image ...


Detecting Phishing Attacks Using Natural Language Processing and Machine Learning

2018 IEEE 12th International Conference on Semantic Computing (ICSC), 2018

Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis ...


Web Application Firewall: Network Security Models and Configuration

2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018

Web Application Firewalls (WAFs) are deployed to protect web applications and they offer in depth security as long as they are configured correctly. A problem arises when there is over-reliance on these tools. A false sense of security can be obtained with the implementation of a WAF. In this paper, we provide an overview of traffic filtering models and some ...


Comprehensible Categorization and Visualization of Orchestrated Malicious Domain Names using Linkage Analysis

2018 16th Annual Conference on Privacy, Security and Trust (PST), 2018

Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names upto-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won't be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper ...


Effectively Protect Your Privacy: Enabling Flexible Privacy Control on Web Tracking

2017 Fifth International Symposium on Computing and Networking (CANDAR), 2017

Third-party tracking, which can collect the users' privacy when users are surfing the Internet, has garnered much attention. Nowadays tracker-blocking tools often use a ruleset based on the domains and elements that need to be blocked. This results in blocking all access tracking, even though the website shows no sign about tracking users' privacy. And what's more, although the tracker-blocking ...


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Educational Resources on Blacklisting

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IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Blacklisting"

IEEE-USA E-Books

  • RRPhish: Anti-phishing via mining brand resources request

    Although in recent years a variety of anti-phishing studies have been carried out, phishing fraud has become increasingly rampant. Especially with the popularity of electronic banking and mobile payment, phishing attacks have become more profitable. In this context, exploring efficient and practical anti-phishing technology is particularly necessary and urgent. In this paper, by analyzing the resources (CSS, JS, and image files) request characteristics of phishing sites, we propose a novel anti-phishing method - RRPhish. RRPhish as an enhanced blacklist technology, can detect not only phishes in blacklist, but also emerging phishes. The experiments demonstrate the effectiveness of RRPhish.

  • Detecting Phishing Attacks Using Natural Language Processing and Machine Learning

    Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.

  • Web Application Firewall: Network Security Models and Configuration

    Web Application Firewalls (WAFs) are deployed to protect web applications and they offer in depth security as long as they are configured correctly. A problem arises when there is over-reliance on these tools. A false sense of security can be obtained with the implementation of a WAF. In this paper, we provide an overview of traffic filtering models and some suggestions to avail the benefit of web app firewall.

  • Comprehensible Categorization and Visualization of Orchestrated Malicious Domain Names using Linkage Analysis

    Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names upto-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won't be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper demonstrates how to use linkage analysis and open-source threat intelligence to visualize the relationship of malicious domain names whilst verifying their categories, i.e., drive-by download, unwanted software etc. Featured by a graph-based model that could present the inter-connectivity of malicious domain names in a dynamic fashion, the proposed approach proved to be helpful for revealing the group patterns of different kinds of malicious domain names. When applied to analyze a blacklisted set of URLs in a real enterprise network, it showed better effectiveness than traditional methods and yielded a clearer view of the common patterns in the data.

  • Effectively Protect Your Privacy: Enabling Flexible Privacy Control on Web Tracking

    Third-party tracking, which can collect the users' privacy when users are surfing the Internet, has garnered much attention. Nowadays tracker-blocking tools often use a ruleset based on the domains and elements that need to be blocked. This results in blocking all access tracking, even though the website shows no sign about tracking users' privacy. And what's more, although the tracker-blocking tools try their best to block all the third-party tracking, not all the users dislike the advertisement. Some of them think if their privacy is fine, it's all right to accept advertisements. In this paper, we present a novel framework by using Word2Vec to block third-party tracking. Our goal is to create more flexible and well-developed ruleset that can help users to protect their privacy according to their needs. Instead of blocking all access tracking, we decide to pay more attention to the websites that have a strong probability to collect the users' privacy. We use Word2Vec to classify the websites, and our results show that after using our framework, the error rate drops from 71% to 24%. We believe it brings the new blood into the field of web privacy by providing not only the new third-party tracking tool but also a novel way of thinking about how to block the third-party tracking.

  • Detection and Prevention of Phishing Websites Using Machine Learning Approach

    Phishing costs Internet user's lots of dollars per year. It refers to exploiting weakness on the user side, which is vulnerable to such attacks. The phishing problem is huge and there does not exist only one solution to minimize all vulnerabilities effectively, thus multiple techniques are implemented. In this paper, we discuss three approaches for detecting phishing websites. First is by analyzing various features of URL, second is by checking legitimacy of website by knowing where the website is being hosted and who are managing it, the third approach uses visual appearance based analysis for checking genuineness of website. We make use of Machine Learning techniques and algorithms for evaluation of these different features of URL and websites. In this paper, an overview about these approaches is presented.

  • An Efficient Approach of Spam Detection in Twitter

    Twitter spam has turned into a basic issue these days. Late work concentrate on applying machine learning systems for twitter spam discovery which make utilisation of factual components of tweets. In our marked tweets informational collection, we watch that the measurable properties of spam tweets fluctuate after some time and along these lines, the execution of existing machine learning based classifier diminishes. This issue is called Twitter spam drift. With a specific end goal to handle this issue, a scheme called Lfun scheme is used which can find changed spam tweets from unlabelled tweets and fuse them into classifiers training process. The new training dataset is used to trained another dataset containing unlabelled tweets which will result in finding of spam tweets. Our proposed scheme will adjust training data such as dropping too old samples after certain time which will eliminate unuseful information saving space.

  • Optimization of Bayesian Classifier Based on Flower Pollination Algorithm

    Naive Bayesian classifier is a commonly used classification algorithm, which has the advantages of high classification efficiency and low cost. However, in practical application, the assumption of class conditional independence reduces the accuracy of algorithm classification. In order to solve the problem, the flower pollination algorithm (FPA) is adopted to optimize Naive Bayes classifier, and the Naive Bayesian classifier algorithm based on improved flower pollination algorithm (NBC-IFPA) is proposed. Firstly, the blacklist mechanism is introduced to make the FPA jump out of the local optimal solution. Secondly, the random perturbation term is introduced to increase the diversity of the population and improve the searching ability of FPA. Finally, the improved FPA is used to search for the global optimal attribute weights and use them into the weighted naive Bayesian model for classification. The simulation results show that the NBC-IFPA algorithm has higher classification accuracy.

  • Method of Quantification of Cyber Threat Based on Indicator of Compromise

    As a large quantity of new and varied attacks occur in Korea, it is difficult to analyze and respond to them with limited security experts and existing equipment. This paper proposes a method of analyzing the threat of Indicator of Compromise (IoC) used for cyber incidents and calculating it as a quantitative value in order to check the analysis priority of cyber incidents that occur in large quantities. Using this method, a large quantity of cyber incidents can be efficiently responded to by checking the quantification of cyber threat objectively to quickly determine the response level of the cyber incident and actively analyze cyber incidents with high threat levels.

  • Spam detection in online social networks by deep learning

    Twitter spam is one of the most important problems that professionals have to deal with in social networks on the internet. For this problem, the researchers presented some solutions, mostly based on a number of different methods considering learning. The means and techniques used at the current time has achieved a good ratio of the accuracy based on the so-called methods of blacklisting in order to determine the undesirable activities in relation to send and receive an e-mail on social networks that based on the conclusions obtained from previous experiments and studies. However, methods that rely on automated learning are not capable of detecting spam activities in proportion to the real scenarios. We see that methods called blacklist methods are not able to meet the disparities we see in activities related to the transmission of such a message, because manually checking Unique Resources Locaters (URLs) is more time-consuming task. In this study, we present a deep learning method for spam detection in witter. For this purpose, the Word2Vec based on representation is first trained. Then we use binary classification methods to distinguish the spam and the nonspam tweets. The empirical results conducted on tweets prove that the selected methods outperform the classical approaches.



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