DOCSLIB.ORG

Anomaly Detection in SMTP Conversations

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Anomaly Detection in SMTP Conversations Piotr Bazydło Accepted version of paper published in IEEE Security & Krzysztof Lasota Privacy, vol. 15, issue 6, Nov/Dec 2017, pp. 25-32 (in Adam Kozakiewicz accordance with IEEE policy on electronic information dissemination) Research and Academic Computer Network NASK DOI: 10.1109/MSP.2017.4251116 Kolska 12, 01-045 Warsaw, Poland [email protected] © 2017 IEEE. Personal use of this material is permitted. [email protected] Permission from IEEE must be obtained for all other uses, in any [email protected] current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Botnet fingerprinting method based on anomaly detection in SMTP conversations Abstract: The paper presents the results obtained during research on detection of unsolicited e-mails which are sent by botnets. The distinction from most of the existing solutions is the fact that the presented approach is based on the analysis of network traffic – the sequence and syntax of SMTP commands observed during email delivery process. The paper presents several improvements for detection of unsolicited email sources from different botnets (fingerprinting), which can be used during network forensic investigation. 1. Introduction The system of electronic mail is essential for business communication and most of the organizations cannot imagine working or even existing without e-mail accounts. On the other hand, it is also used for malicious activities. Just sending the advertising emails, besides Fig. 1. Spambot email delivery with brokers (A) and being inconvenient and wasting various types of direct (B) resources, e.g. available bandwidth (more than half of which unwanted messages reach the target e-mail the email messages are classified as spam [1]) or disk servers. In the second case (Fig. 1B), where bots are able space, carries a small risk of a security violation. Spam is to use specific conversation with mail servers, the commonly used during phishing campaigns and can number of spam sources dramatically increases, up to the include links to malicious websites or malicious number of compromised machines controlled by the attachments. botmaster. Mail servers receive messages from a huge Classical approach to e-mail forensic investigation number of sources. Filtration is no longer trivial, source [2] assumes analysis of electronic message headers and e-mail address, domain or IP address of the sender are contents to identify the sender. The direct source of spam not enough. usually turns out to be misconfigured/compromised mail A large number of scientific papers describing servers or specially prepared brokers (proxies), known as methods of spam detection have been published [5, 6]. open relays which allow uncontrolled sending of These are mainly based on the analysis of the received electronic messages. Such sources of threats can be message - its headers, content and attachments. For this trivially filtered using blacklists. purpose statistical methods [7], graph theory [8] and This in turn resulted in the development of even machine learning algorithms [9,] are used. advanced spambots - malicious programs specialized in Noteworthy are the papers of Stringhini et al. ([10] and sending spam using their own implementation or its follow-up [11]), which present quite a different existing libraries containing SMTP (Simple Mail approach. Authors focus on the analysis of network Transport Protocol - standard protocol used to send e- communication observed during the sending of a mail) client functionality. However, the modular message by a spambot, while creating a profile of so- structure of modern bots allows the same infected called SMTP dialects and comparing them with normal machines to perform other malicious activities (DDoS, traffic generated by benign mail sending applications. bitcoin mining etc.) [3, 4]. Their results show mistakes in the implementation of the Figure 1 illustrates the two main modes of spambot SMTP protocol made by the malware authors and operation. In the first mode (Fig. 1A), an infected therefore possibility of its classification. Detection of computer sends spam via the aforementioned brokers. anomalies in SMTP traffic may find application in the Thereby, it is restricting the number of locations from network forensic investigation on malicious activity of spambots [12]. Paper [13] presents in-depth analysis of S: 220 smtp.server.com spam detection through network characteristics, like IP C: EHLO my.example.com TTL derived from TCP SYN packet or completion time S: 250 smtp.server.com of TCP three way handshake. C: MAIL FROM:<[email protected]> In this paper, we focus on the development of the S: 250 2.1.0 Ok algorithm of detection and classification of messages C: RCPT TO:<[email protected]> sent by spambots. The article presents additional features S: 250 2.1.5 Ok (section 3) and results (both spam detection and botnet C: DATA fingerprinting) that we have obtained during our work on S: 354 the analysis of unsolicited emails (section 4). The main C: Test message. part of the article is preceded by a short description of C: . the structure of an SMTP conversation and a definition S: 250 2.0.0 Ok of an SMTP dialect (section 2). The paper finishes with a C: QUIT brief conclusion of our findings (section 5). S: 221 2.0.0 Bye When TCP connection is successfully established, 2. SMTP Dialects the server greets the client (with “220” command). In order to understand the idea of SMTP dialects The client responds using the “EHLO” command analysis, some basics have to be established. (or “HELO” in a deprecated version of SMTP). Then, SMTP is a global standard for email transfer. This the sender of the email is provided using “MAIL text-based protocol was defined in 1982 and updated in FROM” command. If the sender address is accepted by 2008 by RFC 5321. In simple words, it can be described the server, the recipient address is provided using as the rules of conversation between MUA (Mail User “RCPT TO”. Finally, the client signalizes beginning of Agent, e.g. Microsoft Outlook, Mozilla Thunderbird) the message through the “DATA” command and ends it clients and MTA/MDA servers (Mail Transfer Agent, using a special terminator (dot). The transmitted message Mail Delivery Agent, e.g. Postfix or Microsoft Exchange should conform to the IMF (Internet Message Format) Server), which should result in electronic mail delivery. standard. Conversation ends with the “QUIT” command Generalized mail delivery is presented in fig. 2 and and the server response. the procedure is performed in a following way: In this simple scenario, only five from eleven • 1) user provides an input (message and primary SMTP client commands are used. Every SMTP recipients) to the MUA which transfers mail to command must end with <CR><LF> (carriage return + the mail server using the SMTP protocol, line feed) line terminator. • 2a) if server is used as a relay, the MTA Extended SMTP allows the client to use available forwards the message to the appropriate mail SMTP extensions, listed by the server after successful server using SMTP, “EHLO”. For example, the client may receive • 2b) if the current MTA is the final destination information whether encrypted connections initialized by of the message, mail delivery to the local “STARTTLS” command are supported. mailbox is handled by the server's MDA (Mail Like in a spoken language, there are rules that have Delivery Agent), to be obeyed in order for an SMTP conversation to be understood by both sides, but some minor grammatical • 3) mail is downloaded from the proper mailbox differences or errors have no real impact on the by the MUA using POP (Post Office Protocol) conversation outcome. or IMAP (Internet Message Access Protocol) If for example, the MTA recognizes both “MAIL protocols. FROM:<[email protected]>” and “mail from: [email protected]" commands properly and can distinguish the address correctly. SMTP commands are case-insensitive, and some characters like “<>” or some whitespaces are optional. Various equivalent forms of SMTP commands lead to the possibility of distinguishing different SMTP implementations and their sources. As mentioned in the introduction section, first work towards analysis of different SMTP protocol implementations was presented by G. Stringhini et. al. [10]. Authors provided the solution called “B@bel”, in which SMTP dialects derived from incoming TCP sessions are analyzed and compared with the known Fig. 2. Direct and indirect email delivery. (benign) SMTP dialects. In later research [12], G. Stringhini et. al. used primary IMF fields like message SMTP conversation is carried out according to the subject or sender address to connect SMTP dialects with client-server model and looks the same, whether spam campaigns. message delivery is performed between the MUA and the MTA or two different MTA's. The following listing shows a typical SMTP conversation, where an email is sent to one recipient. 3. SMTP Dialect Extension suspicious because of the inconsistency in dialect. The As our solution is based on the concept presented in same reasoning can be applied to MTA dialects. [10, 11], we propose the following improvements to the Another information used in our research included already developed SMTP dialects fingerprinting: the IMF “Received” trace field. It is supposed to inform • analysis of full SMTP conversation (“B@bel" the current MTA about the real route of an email. If the system was limited to the investigation until email is received by an MTA for delivery to another “DATA” command), server, it has to insert a trace in the form of a “Received” • analysis of SMTP extensions syntax, field. In case of direct email delivery, there should not be • analysis of IMF fields strictly linked with any “Received” trace in IMF.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Glossary of Spam Terms
    white paper Glossary of Spam terms The jargon of The spam indusTry table of Contents A Acceptable Use Policy (AUP) . 5 Alias . 5 Autoresponder . 5 B Ban on Spam . 5 Bayesian Filtering . 5 C CAN-SPAM . 5 Catch Rate . 5 CAUSe . 5 Challenge Response Authentication . 6 Checksum Database . 6 Click-through . 6 Content Filtering . 6 Crawler . 6 D Denial of Service (DoS) . 6 Dictionary Attack . 6 DNSBL . 6 e eC Directive . 7 e-mail Bomb . 7 exploits Block List (XBL) (from Spamhaus org). 7 F False Negative . 7 False Positive . 7 Filter Scripting . 7 Fingerprinting . 7 Flood . 7 h hacker . 8 header . 8 heuristic Filtering . 8 honeypot . 8 horizontal Spam . 8 i internet Death Penalty . 8 internet Service Provider (iSP) . 8 J Joe Job . 8 K Keyword Filtering . 9 Landing Page . 9 LDAP . 9 Listwashing . 9 M Machine-learning . 9 Mailing List . 9 Mainsleaze . 9 Malware . 9 Mung . 9 N Nigerian 419 Scam . 10 Nuke . 10 O Open Proxy . 10 Open Relay . 10 Opt-in . 10 Opt-out . 10 P Pagejacking . 10 Phishing . 10 POP3 . 11 Pump and Dump . 11 Q Quarantine . 11 R RBLs . 11 Reverse DNS . 11 ROKSO . 11 S SBL . 11 Scam . 11 Segmentation . 11 SMtP . 12 Spam . 12 Spambot . 12 Spamhaus . 12 Spamming . 12 Spamware . 12 SPewS . 12 Spider . 12 Spim . 12 Spoof . 12 Spyware . 12 t training Set . 13 trojan horse . 13 trusted Senders List . 13 U UCe . 13 w whack-A-Mole . 13 worm . 13 V Vertical Spam . 13 Z Zombie . 13 Glossary of Spam terms A acceptable use policy (AUP) A policy statement, made by an iSP, whereby the company outlines its rules and guidelines for use of the account .
    [Show full text]
  • Anna Enarsson New Blends in the English Language Engelska C
    Estetisk-filosofiska fakulteten Anna Enarsson New Blends in the English Language Engelska C-uppsats Termin: Höstterminen 2006 Handledare: Michael Wherrity Karlstads universitet 651 88 Karlstad Tfn 054-700 10 00 Fax 054-700 14 60 [email protected] www.kau.se Titel: New Blends in the English Language Författare: Anna Enarsson Antal sidor: 29 Abstract: The aim of this essay was to identify new blends that have entered the English language. Firstly six different word-formation processes, including blending, was described. Those were compounding, clipping, backformation, acronyming, derivation and blending. The investigation was done by using a list of blends from Wikipedia. The words were looked up in the Longman dictionary of 2005 and in a dictionary online. A google search and a corpus investigation were also conducted. The investigation suggested that most of the blends were made by clipping and the second most common form was clipping and overlapping. Blends with only overlapping was unusual and accounted for only three percent. The investigation also suggested that the most common way to create blends by clipping was to use the first part of the first word and the last part of the second word. The blends were not only investigated according to their structure but also according to the domains they occur in. This part of the investigation suggested that the blends were most frequent in the technical domain, but also in the domain of society. Table of contents 1. Introduction and aims..........................................................................................................1
    [Show full text]
  • NIST SP 800-44 Version 2
    Special Publication 800-44 Version 2 Guidelines on Securing Public Web Servers Recommendations of the National Institute of Standards and Technology Miles Tracy Wayne Jansen Karen Scarfone Theodore Winograd NIST Special Publication 800-44 Guidelines on Securing Public Web Version 2 Servers Recommendations of the National Institute of Standards and Technology Miles Tracy, Wayne Jansen, Karen Scarfone, and Theodore Winograd C O M P U T E R S E C U R I T Y Computer Security Division Information Technology Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899-8930 September 2007 U.S. Department of Commerce Carlos M. Gutierrez, Secretary National Institute of Standards and Technology James Turner, Acting Director GUIDELINES ON SECURING PUBLIC WEB SERVERS Reports on Computer Systems Technology The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology. ITL’s responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Special Publication 800-series reports on ITL’s research, guidance, and outreach efforts in computer security, and its collaborative activities with industry, government, and academic organizations. National Institute of Standards and Technology Special Publication 800-44 Version 2 Natl. Inst. Stand. Technol. Spec. Publ. 800-44 Ver.
    [Show full text]
  • The Tricks of the Trade: What Makes Spam Campaigns Successful?
    The Tricks of the Trade: What Makes Spam Campaigns Successful? Jane Iedemska, Gianluca Stringhini, Richard Kemmerer, Christopher Kruegel, and Giovanni Vigna University of California, Santa Barbara f7 am, gianluca, kemm, chris, [email protected] Abstract—Spam is a profitable business for cybercriminals, with on which aspects make a spam campaign successful. However, the revenue of a spam campaign that can be in the order of this information is important for security researchers, because millions of dollars. For this reason, a wealth of research has been it allows to develop countermeasures that are able to cripple performed on understanding how spamming botnets operate, as the performance of a botnet. well as what the economic model behind spam looks like. In this paper, we study what distinguishes a successful spam Running a spamming botnet is a complex task: the spammer campaign from a failed one. We analyzed the data contained needs to manage the infected machines, the spam content being sent, and the email addresses to be targeted, among the rest. on 24 command and control server instances from the Cutwail In this paper, we try to understand which factors influence the spamming botnet. These servers contain detailed statistics spam delivery process and what characteristics make a spam about the activity of 17 spammers, for the period between 2007 campaign successful. To this end, we analyzed the data stored on and 2010. This data allowed us to understand what challenges a number of command and control servers of a large spamming spammers are facing and how they overcome them. We studied botnet, together with the guidelines and suggestions that the how the Cutwail botmasters help their customers set up a botnet creators provide to spammers to improve the performance successful campaign, which command and control settings of their botnet.
    [Show full text]
  • Social Fingerprinting: 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.
    [Show full text]