When Proof of Work Works

Proof of work (POW) is a set of cryptographic mechanisms which increase the cost of initiating a connection. Currently recipients bear as much or more cost per connection as initiators. The design goal of POW is to reverse the economics of connection initiation on the Internet. In the case of spam, the first economic examination of POW argued that POW would not, in fact, work. This result was based on the difference in production cost between legitimate and criminal enterprises. We illustrate that the difference in production costs enabled by zombies does not remove the efficacy of POW when work requirements are weighted. We illustrate that POW will work with a reputation system modeled on the systems currently used by commercial anti-spam companies. We also discuss how the variation on POW changes the nature of corresponding proofs from token currency to a notational currency

Cognitive Spam Recognition Using Hadoop and Multicast-Update

In today's world of exponentially growing technology, spam is a very common issue faced by users on the internet. Spam not only hinders the performance of a network, but it also wastes space and time, and causes general irritation and presents a multitude of dangers - of viruses, malware, spyware and consequent system failure, identity theft, and other cyber criminal activity. In this context, cognition provides us with a method to help improve the performance of the distributed system. It enables the system to learn what it is supposed to do for different input types as different classifications are made over time and this learning helps it increase its accuracy as time passes. Each system on its own can only do so much learning, because of the limited sample set of inputs that it gets to process. However, in a network, we can make sure that every system knows the different kinds of inputs available and learns what it is supposed to do with a better success rate. Thus, distribution and combination of this cognition across different components of the network leads to an overall improvement in the performance of the system. In this paper, we describe a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. We also present two possible methods of implementation - using a MapReduce Framework (hadoop), and also using messages coupled with a multicast-send based network - with their own subtypes, and the pros and cons of each. We finally present a comparative analysis of the two main methods and provide a basic idea about the usefulness of the two in various different scenarios

Resolving FP-TP Conflict in Digest-Based Collaborative Spam Detection by Use of Negative Selection Algorithm

A well-known approach for collaborative spam filtering is to determine which emails belong to the same bulk, e.g. by exploiting their content similarity. This allows, after observing an initial portion of a bulk, for the bulkiness scores to be assigned to the remaining emails from the same bulk. This also allows the individual evidence of spamminess to be joined, if such evidence is generated by collaborating filters or users for some of the emails from an initial portion of the bulk. Usually a database of previously observed emails or email digests is formed and queried upon receiving new emails. Previous evaluations [2,10] of the approach based on the email digests that preserve email content similarity indicate and partially demonstrate that there are ways to make the approach robust to increased obfuscation efforts by spammers. However, for the settings of the parameters that provide good matching between the emails from the same bulk, the unwanted random matching between ham emails and unrelated ham and spam emails stays rather high. This directly translates into a need for use of higher bulkiness thresholds in order to ensure low false positive (FP) detection of ham, which implies that larger initial parts of spam bulks will not be filtered, i.e. true positive (TP) detection will not be very high (FP-TP conflict). In this paper we demonstrate how, by use of the negative selection algorithm, the unwanted random matching between unrelated emails may be decreased at least by an order of magnitude, while preserving the same good matching between the emails from the same bulk. We also show how this translates into an order of magnitude (at least) of less undetected bulky spam emails, under the same ham miss- detection requirements

Security When People Matter: Structuring Incentives For User Behavior

Humans are “smart components” in a system, but cannot be directly programmed to perform; rather, their autonomy must be respected as a design constraint and incentives provided to induce desired behavior. Sometimes these incentives are properly aligned, and the humans don’t represent a vulnerability. But often, a misalignment of incentives causes a weakness in the system that can be exploited by clever attackers. Incentive-centered design tools help us understand these problems, and provide design principles to alleviate them. We describe incentive-centered design and some tools it provides. We provide a number of examples of security problems for which Incentive Centered Design might be helpful. We elaborate with a general screening model that offers strong design principles for a class of security problems.http://deepblue.lib.umich.edu/bitstream/2027.42/55773/1/icec702w-wash.pd

SchedMail: Sender-Assisted Message Delivery Scheduling to Reduce Time-Fragmentation

Although early efforts aimed at dealing with large amounts of emails focused on filtering out spam, there is growing interest in prioritizing non-spam emails, with the objective of reducing information overload and time fragmentation experienced by recipients. However, most existing approaches place the burden of classifying emails exclusively on the recipients' side, either directly or through recipients' email service mechanisms. This disregards the fact that senders typically know more about the nature of the contents of outgoing messages before the messages are read by recipients. This thesis presents mechanisms collectively called SchedMail which can be added to popular email clients, to shift a part of the user efforts and computational resources required for email prioritization to the senders' side. Particularly, senders declare the urgency of their messages, and recipients specify policies about when different types of messages should be delivered. Recipients also judge the accuracy of sender-side urgency, which becomes the basis for learned reputations of senders; these reputations are then used to interpret urgency declarations from the recipients' perspectives. In order to experimentally evaluate the proposed mechanisms, a proof-of-concept prototype was implemented based on a popular open source email client K-9 Mail. By comparing the amount of email interruptions experienced by recipients, with and without SchedMail, the thesis concludes that SchedMail can effectively reduce recipients' time fragmentation, without placing demands on email protocols or adding significant computational overhead

Towards sender accountability on email infrastructure using sender identity and reputation management

Email Infrastructure has grown exponentially, since the early days of ARPANET, to support millions of users. However, the extensive adoption of the original open design has led to security implications. As claimed in recent statistics, about 95% of the emails are unsolicited and place phishing losses at $500 million. Even though, current email-filtering technologies weed out most of the incoming spam, there is a need to hold senders accountable for their email behavior. Without sender accountability, there is no way to hold senders responsible for their online email behavior. Holding senders accountable helps identify senders who propagate spam, and possibly reduce the spam transmitted. Holding a sender accountable for the sender’s online activity requires: first, the sender’s identification; and second, maintenance of its historical email activity. Today, widely deployed sender identity techniques counteract email spoofing by authenticating the sender's email server to the receiver organizations. Unfortunately, these techniques are not as effective as originally intended as: a) the senders create their own identity; b) spam-propagating senders have adopted these technologies. Knowledge of the sender's identity alone does not guarantee its adherence to email best practices. Towards establishing sender accountability, this dissertation proposes RepuScore, a collaborative reputation framework that allows participating receiver organizations to share sender's behavioral patterns. In addition, this dissertation also explores Privilege Messaging (P-Messaging) framework, a fine-granular sender- authorization framework where each sender holds a set of credentials (privileges) to send an email; the receivers verify the attached credentials before accepting the emails. P- Messaging attempts to maintain trust among organizations with the help of a central authority, which periodically verifies the participating organization's adherence to good email practices. To create a long-standing history, participating organizations locally collect information about the senders - from users or existing spam classification mechanisms that are submitted to a central RepuScore authority - to compute a global reputation summary. This dissertation discusses the distributed architecture and the algorithms designed to compute reputation based on the sender's a) spam rate (RepuScore) or b) spam rate and email volume (Volume-Enhanced RepuScore). Additionally, the dissertation shares findings from experiments based on a RepuScore prototype using a) simulation logs; and b) deployed SpamAssassin plug-in since 10/9/2007 at three organizations. Based on the deployment, reputation for about 90,000 sender identities and about 12 million IP addresses as of Feb 2009 have been computed. We note that email classification using RepuScore is 97.8% accurate. Finally, this dissertation discusses future directions for Distributed RepuScore that allows organizations to maintain their personal reputation view to be shared among trusted peers. Distributed RepuScore enables a global reputation view while holding senders accountable at each organization instead of deploying it at a central authority

Measuring effectiveness and value of email advertisements in relationship-oriented email messages

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 63-70).This thesis explores the value of email advertisements in relationship-based communication by measuring click-through rates. Advertisements were embedded within standard electronic mail messages in a template. The results are discussed in aggregate to better understand this medium and value in an advertising context, but the analysis also breaks down how six factors affect click behavior by recipients. The factors include the impact of a privacy statement, type of organizational recipients, click timing, recent advertisements, advertisement frequency, and advertisement relevance. The results are also analyzed using a chi-squared statistical test to determine whether the individual factor is significant in this analysis. This paper also discusses the privacy implications of advertising in a social-medium with comparisons to social ads using Facebook's Beacon as a benchmark. A discussion of how these results apply in an email environment where anti-SPAM infrastructure is a large part of the overall system is evaluated as well. Results show that internal recipients are an effective target market for relationship messaging and that emails often generate clicks days or weeks after the original message was delivered. The research is very relevant to email and targeted advertising, but also applies in a broader context to social advertising where there is a existing relationship between a sender and recipient.by Dinesh Shenoy.S.M

Towards secure message systems

Message systems, which transfer information from sender to recipient via communication networks, are indispensable to our modern society. The enormous user base of message systems and their critical role in information delivery make it the top priority to secure message systems. This dissertation focuses on securing the two most representative and dominant messages systems---e-mail and instant messaging (IM)---from two complementary aspects: defending against unwanted messages and ensuring reliable delivery of wanted messages.;To curtail unwanted messages and protect e-mail and instant messaging users, this dissertation proposes two mechanisms DBSpam and HoneyIM, which can effectively thwart e-mail spam laundering and foil malicious instant message spreading, respectively. DBSpam exploits the distinct characteristics of connection correlation and packet symmetry embedded in the behavior of spam laundering and utilizes a simple statistical method, Sequential Probability Ratio Test, to detect and break spam laundering activities inside a customer network in a timely manner. The experimental results demonstrate that DBSpam is effective in quickly and accurately capturing and suppressing e-mail spam laundering activities and is capable of coping with high speed network traffic. HoneyIM leverages the inherent characteristic of spreading of IM malware and applies the honey-pot technology to the detection of malicious instant messages. More specifically, HoneyIM uses decoy accounts in normal users\u27 contact lists as honey-pots to capture malicious messages sent by IM malware and suppresses the spread of malicious instant messages by performing network-wide blocking. The efficacy of HoneyIM has been validated through both simulations and real experiments.;To improve e-mail reliability, that is, prevent losses of wanted e-mail, this dissertation proposes a collaboration-based autonomous e-mail reputation system called CARE. CARE introduces inter-domain collaboration without central authority or third party and enables each e-mail service provider to independently build its reputation database, including frequently contacted and unacquainted sending domains, based on the local e-mail history and the information exchanged with other collaborating domains. The effectiveness of CARE on improving e-mail reliability has been validated through a number of experiments, including a comparison of two large e-mail log traces from two universities, a real experiment of DNS snooping on more than 36,000 domains, and extensive simulation experiments in a large-scale environment