Nicole Lang Beebe

Bio: Nicole Lang Beebe is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: Digital forensics & Insider threat. The author has an hindex of 15, co-authored 45 publications receiving 1178 citations.

Digital forensic research: the good, the bad and the unaddressed

Abstract: Digital forensics is a relatively new scientific discipline, but one that has matured greatly over the past decade. In any field of human endeavor, it is important to periodically pause and review the state of the discipline. This paper examines where the discipline of digital forensics is at this point in time and what has been accomplished in order to critically analyze what has been done well and what ought to be done better. The paper also takes stock of what is known, what is not known and what needs to be known. It is a compilation of the author’s opinion and the viewpoints of twenty-one other practitioners and researchers, many of whom are leaders in the field. In synthesizing these professional opinions, several consensus views emerge that provide valuable insights into the “state of the discipline.”

Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results

Abstract: Current digital forensic text string search tools use match and/or indexing algorithms to search digital evidence at the physical level to locate specific text strings. They are designed to achieve 100% query recall (i.e. find all instances of the text strings). Given the nature of the data set, this leads to an extremely high incidence of hits that are not relevant to investigative objectives. Although Internet search engines suffer similarly, they employ ranking algorithms to present the search results in a more effective and efficient manner from the user's perspective. Current digital forensic text string search tools fail to group and/or order search hits in a manner that appreciably improves the investigator's ability to get to the relevant hits first (or at least more quickly). This research proposes and empirically tests the feasibility and utility of post-retrieval clustering of digital forensic text string search results - specifically by using Kohonen Self-Organizing Maps, a self-organizing neural network approach. This paper is presented as a work-in-progress. A working tool has been developed and experimentation has begun. Findings regarding the feasibility and utility of the proposed approach will be presented at DFRWS 2007, as well as suggestions for follow-on research.

Dealing with Terabyte Data Sets in Digital Investigations

Abstract: Investigators and analysts are increasingly experiencing large, even terabyte sized data sets when conducting digital investigations. State-of-the-art digital investigation tools and processes are efficiency constrained from both system and human perspectives, due to their continued reliance on overly simplistic data reduction and mining algorithms. The extension of data mining research to the digital forensic science discipline will have some or all of the following benefits: (i) reduced system and human processing time associated with data analysis; (ii) improved information quality associated with data analysis; and (iii) reduced monetary costs associated with digital investigations. This paper introduces data mining and reviews the limited extant literature pertaining to the application of data mining to digital investigations and forensics. Finally, it provides suggestions for applying data mining research to digital forensics.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Test Driven Development: By Example

Abstract: From the Book: “Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons: Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). In Test-Driven Development, you: Write new code only if you first have a failing automated test.Eliminate duplication. Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy The two rules imply an order to the tasks ofprogramming: 1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first 2. Green—make the test work quickly, committing whatever sins necessary in the process 3. Refactor—eliminate all the duplication created in just getting the test to work Red/green/refactor. The TDD’s mantra. Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications: If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage. Courage Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects: Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and: Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet. The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test. Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately. That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress. There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code. Once you are finished reading this book, you should be ready to: Start simplyWrite automated testsRefactor to add design decisions one at a time This book is organized into three sections. An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples. I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference. Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read. A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Bad Apples, Bad Cases, and Bad Barrels: Meta-Analytic Evidence About Sources of Unethical Decisions at Work

Abstract: As corporate scandals proliferate, practitioners and researchers alike need a cumulative, quantitative understanding of the antecedents associated with unethical decisions in organizations. In this meta-analysis, the authors draw from over 30 years of research and multiple literatures to examine individual ("bad apple"), moral issue ("bad case"), and organizational environment ("bad barrel") antecedents of unethical choice. Findings provide empirical support for several foundational theories and paint a clearer picture of relationships characterized by mixed results. Structural equation modeling revealed the complexity (multidetermined nature) of unethical choice, as well as a need for research that simultaneously examines different sets of antecedents. Moderator analyses unexpectedly uncovered better prediction of unethical behavior than of intention for several variables. This suggests a need to more strongly consider a new "ethical impulse" perspective in addition to the traditional "ethical calculus" perspective. Results serve as a data-based foundation and guide for future theoretical and empirical development in the domain of behavioral ethics.

Privacy in the digital age: a review of information privacy research in information systems

Abstract: Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.