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Technical and Legal Approaches to Unsolicited Electronic Mail, 35 USFL Rev
UIC School of Law UIC Law Open Access Repository UIC Law Open Access Faculty Scholarship 1-1-2001 Technical and Legal Approaches to Unsolicited Electronic Mail, 35 U.S.F. L. Rev. 325 (2001) David E. Sorkin John Marshall Law School, [email protected] Follow this and additional works at: https://repository.law.uic.edu/facpubs Part of the Computer Law Commons, Internet Law Commons, Marketing Law Commons, and the Privacy Law Commons Recommended Citation David E. Sorkin, Technical and Legal Approaches to Unsolicited Electronic Mail, 35 U.S.F. L. Rev. 325 (2001). https://repository.law.uic.edu/facpubs/160 This Article is brought to you for free and open access by UIC Law Open Access Repository. It has been accepted for inclusion in UIC Law Open Access Faculty Scholarship by an authorized administrator of UIC Law Open Access Repository. For more information, please contact [email protected]. Technical and Legal Approaches to Unsolicited Electronic Mailt By DAVID E. SORKIN* "Spamming" is truly the scourge of the Information Age. This problem has become so widespread that it has begun to burden our information infrastructure. Entire new networks have had to be constructed to deal with it, when resources would be far better spent on educational or commercial needs. United States Senator Conrad Burns (R-MT)1 UNSOLICITED ELECTRONIC MAIL, also called "spain," 2 causes or contributes to a wide variety of problems for network administrators, t Copyright © 2000 David E. Sorkin. * Assistant Professor of Law, Center for Information Technology and Privacy Law, The John Marshall Law School; Visiting Scholar (1999-2000), Center for Education and Research in Information Assurance and Security (CERIAS), Purdue University. -
Ipv6 DMZ Web Service Technology Design Guide
IPv6 DMZ Web Service Technology Design Guide August 2014 Series Table of Contents Preface ........................................................................................................................................1 CVD Navigator .............................................................................................................................2 Use Cases .................................................................................................................................. 2 Scope ......................................................................................................................................... 2 Proficiency .................................................................................................................................. 2 Introduction .................................................................................................................................3 Technology Use Cases ............................................................................................................... 3 Use Case: Enable Native IPv6 Access for Network Traffic Between the Internet and a Web Server DMZ Network.............................................................................................................. 3 Use Case: Enable IPv6 Access for Network Traffic Between the Internet and an IPv4-only Web Server DMZ Network ..................................................................................................... 3 Design Overview ........................................................................................................................ -
Iptables with Shorewall!
Iptables with shorewall! Table of Contents 1. Install swarmlab-sec (Home PC) . 1 2. shorewall . 1 2.1. Installation . 2 3. Basic Two-Interface Firewall. 2 4. Shorewall Concepts . 3 4.1. zones — Shorewall zone declaration file . 3 4.2. interfaces — Shorewall interfaces file. 4 4.3. policy — Shorewall policy file . 4 4.4. rules — Shorewall rules file . 4 4.5. Compile then Execute . 4 5. Three-Interface Firewall. 5 5.1. zones . 6 5.2. interfaces . 6 5.3. policy . 7 5.4. rules . 7 5.5. masq - Shorewall Masquerade/SNAT definition file . 7 5.6. snat — Shorewall SNAT/Masquerade definition file . 8 5.7. Compile and Execute . 8 1. Install swarmlab-sec (Home PC) HowTo: See http://docs.swarmlab.io/lab/sec/sec.adoc.html NOTE Assuming you’re already logged in 2. shorewall Shorewall is an open source firewall tool for Linux that builds upon the Netfilter (iptables/ipchains) system built into the Linux kernel, making it easier to manage more complex configuration schemes by providing a higher level of abstraction for describing rules using text files. More: wikipedia 1 NOTE Our docker instances have only one nic to add more nic’s: create netowrk frist docker network create --driver=bridge --subnet=192.168.0.0/16 net1 docker network create --driver=bridge --subnet=192.168.0.0/16 net2 docker network create --driver=bridge --subnet=192.168.0.0/16 net3 then connect network to container connect network created to container docker network connect net1 master docker network connect net1 worker1 docker network connect net2 master docker network connect net2 worker2 now let’s look at the following image 2.1. -
Locating Spambots on the Internet
BOTMAGNIFIER: Locating Spambots on the Internet Gianluca Stringhinix, Thorsten Holzz, Brett Stone-Grossx, Christopher Kruegelx, and Giovanni Vignax xUniversity of California, Santa Barbara z Ruhr-University Bochum fgianluca,bstone,chris,[email protected] [email protected] Abstract the world-wide email traffic [20], and a lucrative busi- Unsolicited bulk email (spam) is used by cyber- ness has emerged around them [12]. The content of spam criminals to lure users into scams and to spread mal- emails lures users into scams, promises to sell cheap ware infections. Most of these unwanted messages are goods and pharmaceutical products, and spreads mali- sent by spam botnets, which are networks of compro- cious software by distributing links to websites that per- mised machines under the control of a single (malicious) form drive-by download attacks [24]. entity. Often, these botnets are rented out to particular Recent studies indicate that, nowadays, about 85% of groups to carry out spam campaigns, in which similar the overall spam traffic on the Internet is sent with the mail messages are sent to a large group of Internet users help of spamming botnets [20,36]. Botnets are networks in a short amount of time. Tracking the bot-infected hosts of compromised machines under the direction of a sin- that participate in spam campaigns, and attributing these gle entity, the so-called botmaster. While different bot- hosts to spam botnets that are active on the Internet, are nets serve different, nefarious goals, one important pur- challenging but important tasks. In particular, this infor- pose of botnets is the distribution of spam emails. -
Spambots: Creative Deception
PATTERNS 2020 : The Twelfth International Conference on Pervasive Patterns and Applications Spambots: Creative Deception Hayam Alamro Costas S. Iliopoulos Department of Informatics Department of Informatics King’s College London, UK King’s College London, UK Department of Information Systems email: costas.iliopoulos Princess Nourah bint Abdulrahman University @kcl.ac.uk Riyadh, KSA email: [email protected] Abstract—In this paper, we present our spambot overview on the intent of building mailing lists to send unsolicited or phishing creativity of the spammers, and the most important techniques emails. Furthermore, spammers can create fake accounts to that the spammer might use to deceive current spambot detection target specific websites or domain specific users and start send- tools to bypass detection. These techniques include performing a ing predefined designed actions which are known as scripts. series of malicious actions with variable time delays, repeating the Moreover, spammers can work on spreading malwares to steal same series of malicious actions multiple times, and interleaving other accounts or scan the web to obtain customers contact legitimate and malicious actions. In response, we define our problems to detect the aforementioned techniques in addition information to carry out credential stuffing attacks, which is to disguised and "don’t cares" actions. Our proposed algorithms mainly used to login to another unrelated service. In addition, to solve those problems are based on advanced data structures spambots can be designed to participate in deceiving users on that are able to detect malicious actions efficiently in linear time, online social networks through the spread of fake news, for and in an innovative way. -
Address Munging: the Practice of Disguising, Or Munging, an E-Mail Address to Prevent It Being Automatically Collected and Used
Address Munging: the practice of disguising, or munging, an e-mail address to prevent it being automatically collected and used as a target for people and organizations that send unsolicited bulk e-mail address. Adware: or advertising-supported software is any software package which automatically plays, displays, or downloads advertising material to a computer after the software is installed on it or while the application is being used. Some types of adware are also spyware and can be classified as privacy-invasive software. Adware is software designed to force pre-chosen ads to display on your system. Some adware is designed to be malicious and will pop up ads with such speed and frequency that they seem to be taking over everything, slowing down your system and tying up all of your system resources. When adware is coupled with spyware, it can be a frustrating ride, to say the least. Backdoor: in a computer system (or cryptosystem or algorithm) is a method of bypassing normal authentication, securing remote access to a computer, obtaining access to plaintext, and so on, while attempting to remain undetected. The backdoor may take the form of an installed program (e.g., Back Orifice), or could be a modification to an existing program or hardware device. A back door is a point of entry that circumvents normal security and can be used by a cracker to access a network or computer system. Usually back doors are created by system developers as shortcuts to speed access through security during the development stage and then are overlooked and never properly removed during final implementation. -
A Visual Analytics Toolkit for Social Spambot Labeling
© 2019 IEEE. This is the author’s version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: 10.1109/TVCG.2019.2934266 VASSL: A Visual Analytics Toolkit for Social Spambot Labeling Mosab Khayat, Morteza Karimzadeh, Jieqiong Zhao, David S. Ebert, Fellow, IEEE G A H B C E I D F Fig. 1. The default layout of the front-end of VASSL: A) the timeline view, B) the dimensionality reduction view, C) the user/tweet detail views, D) & E) the topics view (clustering / words), F) the feature explorer view, G) the general control panel, H) the labeling panel, and I) the control panels for all the views (the opened control panel in the figure is for topics clustering view). Abstract—Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool. -
Detection of Spambot Groups Through DNA-Inspired Behavioral Modeling
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 1 Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi Abstract—Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics. Index Terms—Spambot detection, social bots, online social networks, Twitter, behavioral modeling, digital DNA. -
Technical and Legal Approaches to Unsolicited Electronic Mail†
35 U.S.F. L. REV. 325 (2001) Technical and Legal Approaches to Unsolicited Electronic Mail† By DAVID E. SORKIN* “Spamming” is truly the scourge of the Information Age. This problem has become so widespread that it has begun to burden our information infrastructure. Entire new networks have had to be constructed to deal with it, when resources would be far better spent on educational or commercial needs. United States Senator Conrad Burns (R-MT)1 UNSOLICITED ELECTRONIC MAIL, also called “spam,”2 causes or contributes to a wide variety of problems for network administrators, † Copyright © 2000 David E. Sorkin. * Assistant Professor of Law, Center for Information Technology and Privacy Law, The John Marshall Law School; Visiting Scholar (1999–2000), Center for Education and Research in Information Assurance and Security (CERIAS), Purdue University. The author is grateful for research support furnished by The John Marshall Law School and by sponsors of the Center for Education and Research in Information Assurance and Security. Paul Hoffman, Director of the Internet Mail Consortium, provided helpful comments on technical matters based upon an early draft of this Article. Additional information related to the subject of this Article is available at the author’s web site Spam Laws, at http://www.spamlaws.com/. 1. Spamming: Hearing Before the Subcomm. on Communications of the Senate Comm. on Commerce, Sci. & Transp., 105th Cong. 2 (1998) (prepared statement of Sen. Burns), available at 1998 WL 12761267 [hereinafter 1998 Senate Hearing]. 2. The term “spam” reportedly came to be used in connection with online activities following a mid-1980s episode in which a participant in a MUSH created and used a macro that repeatedly typed the word “SPAM,” interfering with others’ ability to participate. -
How to Configure Some Basic Firewall and VPN Scenarios
AlliedWareTM OS How To | Configure Some Basic Firewall and VPN Scenarios Introduction This document provides examples that illustrate common configurations for security routers. You may want to make changes or enhancements to these configurations to customize them to your particular requirements. However, with the configurations provided here, you can be quickly operational with a reliable and secure Internet connection. What information will you find in this document? The first section provides the basic configuration for two likely methods that will be used for an Internet connection from the security router: z "Script A: basic Ethernet connection" on page 3 z "Script B: basic PPPoE configuration" on page 7 The second section provides three extra configurations to enable the router to support three popular forms of Virtual Private Network (VPN) connection, followed by a configuration for a Mail server on a DMZ. One or more of these additional scripts can be added to either of the basic configuration scripts: z "Script C: internal L2TP Network Server (LNS)" on page 11 z "Script D: IPsec tunnel" on page 13 z "Script E: PPTP server on LAN behind router" on page 16 Then the second section ends with an example in which private IP addresses are used on the DMZ LAN: z "Script F: DMZ using private addresses" on page 17 C613-16069-00 REV B www.alliedtelesis.com Introduction > Related How To Notes These six configuration examples are as general as possible, and no actual IP addresses have been specified. IP addresses are represented by placeholder names in angled brackets, for example, <dmz-ip-address>. -
Understanding Local Health Department Twitter Followership
JOURNAL OF MEDICAL INTERNET RESEARCH Harris et al Original Paper Are Public Health Organizations Tweeting to the Choir? Understanding Local Health Department Twitter Followership Jenine K Harris1, PhD; Bechara Choucair2,3, MD; Ryan C Maier4, BA; Nina Jolani5, BA; Jay M Bernhardt6, MPH, PhD 1Brown School, Washington University in St Louis, St Louis, MO, United States 2Chicago Department of Public Health, Chicago, IL, United States 3Feinberg School of Medicine, Northwestern University, Chicago, IL, United States 4Center for Public Health Systems Science, Washington University in St Louis, St Louis, MO, United States 5National Association of County and City Health Officials, Washington DC, DC, United States 6Center for Digital Health and Wellness, College of Health and Human Performance, University of Florida, Gainesville, FL, United States Corresponding Author: Jenine K Harris, PhD Brown School Washington University in St Louis One Brookings Drive Campus Box 1196 St Louis, MO, 63130 United States Phone: 1 3149353522 Fax: 1 3149353756 Email: [email protected] Abstract Background: One of the essential services provided by the US local health departments is informing and educating constituents about health. Communication with constituents about public health issues and health risks is among the standards required of local health departments for accreditation. Past research found that only 61% of local health departments met standards for informing and educating constituents, suggesting a considerable gap between current practices and best practice. Objective: Social media platforms, such as Twitter, may aid local health departments in informing and educating their constituents by reaching large numbers of people with real-time messages at relatively low cost. Little is known about the followers of local health departments on Twitter. -
Supervised Machine Learning Bot Detection Techniques to Identify
SMU Data Science Review Volume 1 | Number 2 Article 5 2018 Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Phillip George Efthimion Southern Methodist University, [email protected] Scott aP yne Southern Methodist University, [email protected] Nicholas Proferes University of Kentucky, [email protected] Follow this and additional works at: https://scholar.smu.edu/datasciencereview Part of the Theory and Algorithms Commons Recommended Citation Efthimion, hiP llip George; Payne, Scott; and Proferes, Nicholas (2018) "Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots," SMU Data Science Review: Vol. 1 : No. 2 , Article 5. Available at: https://scholar.smu.edu/datasciencereview/vol1/iss2/5 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Efthimion et al.: Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Phillip G. Efthimion1, Scott Payne1, Nick Proferes2 1Master of Science in Data Science, Southern Methodist University 6425 Boaz Lane, Dallas, TX 75205 {pefthimion, mspayne}@smu.edu [email protected] Abstract. In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns.