5,090 research outputs found

    Controlling Spam by Secure Internet Content Selection

    Get PDF
    Unsolicited and undesirable e-mail (spam) is a growing problem for Internet users and service providers. We present the Secure Internet Content Selection (SICS) protocol, an efficient cryptographic mechanism for spam-control, based on allocation of responsibility (liability). With SICS, e-mail is sent with a content label, and a cryptographic protocol ensures labels are authentic and penalizes falsely labeled e-mail (spam). The protocol supports trusted senders (penalized by loss of trust) and unknown senders (penalized financially). The recipient can determine the compensation amount for falsely labeled e-mail (spam)). SICS is practical, with negligible overhead, gradual adoption path, and use of existing relationships; it is also flexible and appropriate for most scenarios, including deployment by end users and/or ISPs and support for privacy and legitimate, properly labeled commercial e-mail. SICS improves on other crypto-based proposals for spam controls, and complements non-cryptographic spam control

    Internet Governance: the State of Play

    Get PDF
    The Global Forum on Internet Governance held by the UNICT Task Force in New York on 25-26 March concluded that Internet governance issues were many and complex. The Secretary-General's Working Group on Internet Governance will have to map out and navigate this complex terrain as it makes recommendations to the World Summit on an Information Society in 2005. To assist in this process, the Forum recommended, in the words of the Deputy Secretary-General of the United Nations at the closing session, that a matrix be developed "of all issues of Internet governance addressed by multilateral institutions, including gaps and concerns, to assist the Secretary-General in moving forward the agenda on these issues." This paper takes up the Deputy Secretary-General's challenge. It is an analysis of the state of play in Internet governance in different forums, with a view to showing: (1) what issues are being addressed (2) by whom, (3) what are the types of consideration that these issues receive and (4) what issues are not adequately addressed

    The Use of Firewalls in an Academic Environment

    No full text

    Security techniques for intelligent spam sensing and anomaly detection in online social platforms

    Get PDF
    Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen

    Security techniques for intelligent spam sensing and anomaly detection in online social platforms

    Get PDF
    Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen

    Evaluation of Email Spam Detection Techniques

    Get PDF
    Email has become a vital form of communication among individuals and organizations in today’s world. However, simultaneously it became a threat to many users in the form of spam emails which are also referred as junk/unsolicited emails. Most of the spam emails received by the users are in the form of commercial advertising, which usually carry computer viruses without any notifications. Today, 95% of the email messages across the world are believed to be spam, therefore it is essential to develop spam detection techniques. There are different techniques to detect and filter the spam emails, but off recently all the developed techniques are being implemented successfully to minimize the threats. This paper describes how the current spam email detection approaches are determining and evaluating the problems. There are different types of techniques developed based on Reputation, Origin, Words, Multimedia, Textual, Community, Rules, Hybrid, Machine learning, Fingerprint, Social networks, Protocols, Traffic analysis, OCR techniques, Low-level features, and many other techniques. All these filtering techniques are developed to detect and evaluate spam emails. Along with classification of the email messages into spam or ham, this paper also demonstrates the effectiveness and accuracy of the spam detection techniques
    • …
    corecore