The rapid decline in size and cost of networked sensors combined with increased incentives for use including monitoring physical fitness, improving public safety, increasing security, and adding convenience is causing the physical and online worlds to become heavily instrumented. Some welcome such developments, but others seek to retain privacy, often by focusing on countering the sensors themselves. Scholars have begun to consider surveillance countermeasures as a stand-alone area of research. However, a scholarly taxonomy useful for critical analysis and systematic countermeasure development is lacking. In this paper we provide such a taxonomy illustrated with example countermeasures that have been successfully employed.

In an ideal world, interface design is the art and science of helping users accomplish tasks in a timely, efficient, and pleasurable manner. This paper studies the inverse situation, the vast emergence of deliberately constructed malicious interfaces that violate design best practices in order to accomplish goals counter to those of the user. This has become a commonplace occurrence both on and off the desktop, particularly on the web. A primary objective of this paper is to formally define this problem, including construction of a taxonomy of malicious interface techniques and a preliminary analysis of their impact on users. Findings are presented that gauge the self-reported tolerance and expectation levels of users with regard to malicious interfaces as well as the effectiveness and ease of use of existing countermeasures. A second objective of this paper is to increase awareness, dialogue, and research in a domain that we consider largely unexplored but critical to future usability of the WWW. Our results were accomplished through significant compilation of malicious interface techniques based on review of thousands of web sites and by conducting three surveys. Ultimately, this paper concludes that malicious interfaces are a ubiquitous problem that demands intervention by the security and human computer interaction communities in order to reduce the negative impact on the global user population.

Free online tools such as search, email and mapping come with a hidden cost. Web users obtain such services by making micropayments of personal and organizational information to the Web service providers. Web companies use this information to create customized advertising and tailored user experiences. Individually, each transaction appears innocuous, but when aggregated, the result is often highly sensitive. The impact of AOL's inadvertent disclosure of 20 million nominally anonymized search queries underscores the pressing need for increasing Web privacy and raising user awareness of the problem. Rather than advocate extreme legal and policy measures to address the dilemma, this paper proposes an equitable self- monitoring solution. Self-monitoring allows individual users and large enterprises to regulate their Web-based interactions intelligently and still allow online companies to innovate and flourish. The primary contributions of our work include exploration of visualization techniques that support self-monitoring based on a user requirements survey, a human-centric evaluation, and a Firefox extension based on one of the visual monitoring solutions developed.

In today's era of the global ubiquitous use of free online tools and business models that depend on data retention and customized advertising, we face a growing tension between the privacy concerns of individuals and the financial motivations of organizations. As a critical foundation step to address this problem, we must first understand the attitudes, beliefs, behaviors, and expectations of web users in order to create an environment where user privacy needs are met while still allowing online companies to innovate and provide functionality that users desire. As security and usability professionals we must identify areas where misperceptions exist and seek solutions, either by raising awareness, changing policy, or through technical means. In this paper, we explore these issues and report the results from a survey of 352 college undergraduates and a comparison group of 25 middle aged adults The results were at times surprising and even contradictory to the views held by security professionals. To summarize our findings, the students we surveyed believe that "an honest man has nothing to fear."

Information visualization is an effective way to easily comprehend large amounts of data. For such systems to be truly effective, the information visualization designer must be aware of the ways in which their system may be manipulated and protect their users from attack. In addition, users should be aware of potential attacks in order to minimize or negate their effect. These attacks target the information visualization system as well as the perceptual, cognitive and motor capabilities of human end users. To identify and help counter these attacks we present a framework for information visualization system security analysis, a taxonomy of visualization attacks and technology independent principles for countering malicious visualizations. These themes are illustrated with case studies and working examples from the network security visualization domain, but are widely applicable to virtually any information visualization system.

The massive amount of alarm data generated from intrusion detection systems is cumbersome for network system administrators to analyze. Often, important details are overlooked and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview where system administrators can get a general sense of network activity and easily detect anomalies. They then have the option of zooming and drilling down for details. The information is presented with local network IP (Internet Protocol) addresses plotted over multiple yaxes to represent the location of alarms. Time on the x-axis is used to show the pattern of the alarms and variations in color encode the severity and amount of alarms. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. The motivation and background of our design is presented along with examples that illustrate its usefulness.

This paper explores the application of visualization techniques to aid in the analysis of malicious and non-malicious binary objects. These objects may include any logically distinct chunks of binary data such as image files, word processing documents and network packets. To facilitate this analysis, we present a novel visualization technique for comparing and navigating among 600-1000+ such objects at one time. While the visualization technique alone has powerful application for both directed and undirected exploration of many classes of binary objects, we chose to study network packets. To increase effectiveness, we strengthened the visualization technique with novel, domain-specific semantic zooming, interactive encoding and dynamic querying capabilities. We present results and lessons learned from implementing these techniques and from studying both malicious and non-malicious network packets. Our results indicate that the information visualization system we present is an efficient and effective way to compare large numbers of network packets, visually examine their payloads and navigate to areas of interest within large network datasets.

The widely accepted method to repair a compromised system is to wipe the system clean and reinstall. We think that there may be alternative methods. Specifically, we envision systems that are capable of automatically recovering from system compromises. Our proposed approach is a repair agent that resides in an isolated area on the system. We use a virtual machine approach to isolate the repair agent. The repair agent should roll back any undesirable changes, determine the point of entry, and prevent further compromise.

This paper examines the dramatic visual fingerprints left by a wide variety of popular network attack tools in order to better understand the specific methodologies used by attackers as well as the identifiable characteristics of the tools themselves. The techniques used are entirely passive in nature and virtually undetectable by the attackers. While much work has been done on active and passive operating systems detection, little has been done on fingerprinting the specific tools used by attackers. This research explores the application of several visualization techniques and their usefulness toward identification of attack tools, without the typical automated intrusion detection system's signatures and statistical anomalies. These visualizations were tested using a wide range of popular network security tools and the results show that in many cases, the specific tool can be identified and provides intuition that many classes of zero-day attacks can be rapidly detected and analyzed using similar techniques.