Machine Learning Techniques for Characterizing IEEE 802.11b Encrypted Data Streams

As wireless networks become an increasingly common part of the infrastructure in industrialized nations, the vulnerabilities of this technology need to be evaluated. Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic. These characteristics include packet size, signal strength, channel utilization and others. Using these characteristics, windows of size 11, 31, and 51 packets are collected and machine learning (ML) techniques are trained to classify applications accessing the 802.11b wireless channel. The four applications used for this study included E-Mail, FTP, HTTP, and Print. Using neural networks and decision trees, the overall success (correct identification of applications) of the ML systems ranged from a low average of 65.8% for neural networks to a high of 85.9% for decision trees. These averages are a result of all classification attempts including the case where only one application is accessing the medium and also the unique combinations of two and three different applications

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

Collaboration in Opportunistic Networks

Motivation. With the increasing integration of wireless short-range communication technologies (Bluetooth, 802.11b WiFi) into mobile devices, novel applications for spontaneous communication, interaction and collaboration are possible. We distinguish between active and passive collaboration. The devices help users become aware of each other and stimulate face-to-face conversation (active collaboration). Also, autonomous device communication for sharing information without user interaction is possible, i.e., devices pass information to other devices in their vicinity (passive collaboration). Both, active and passive collaboration requires a user to specify what kind of information he offers and what kind of information he is interested in. Object of Research: Opportunistic Networks. Spontaneous communication of mobile devices leads to so-called opportunistic networks, a new and promising evolution in mobile ad-hoc networking. They are formed by mobile devices which communicate with each other while users are in close proximity. There are two prominent characteristics present in opportunistic networks: 1) A user provides his personal device as a network node. 2) Users are a priori unknown to each other. Objectives. Due to the fact that a user dedicates his personal device as a node to the opportunistic network and interacts with other users unknown to him, collaboration raises questions concerning two important human aspects: user privacy and incentives. The users’ privacy is at risk, since passive collaboration applications may expose personal information about a user. Furthermore, some form of incentive is needed to encourage a user to share his personal device resources with others. Both issues, user privacy and incentives, need to be taken into account in order to increase the user acceptability of opportunistic network applications. These aspects have not been addressed together with the technical tasks in prior opportunistic network research. Scientific Contribution and Evaluation. This thesis investigates opportunistic networks in their entirety, i.e., our technical design decisions are appropriate for user privacy preservation and incentive schemes. In summary, the proposed concepts comprise system components, a node architecture, a system model and a simple one-hop communication paradigm for opportunistic network applications. One focus of this work is a profile-based data dissemination mechanism. A formal model for this mechanism will be presented. On top of that, we show how to preserve the privacy of a user by avoiding static and thus linkable data and an incentive scheme that is suitable for opportunistic network applications. The evaluation of this work is twofold. We implemented two prototypes on off-the-shelf hardware to show the technical feasibility of our opportunistic network concepts. Also, the prototypes were used to carry out a number of runtime measurements. Then, we developed a novel two-step simulation method for opportunistic data dissemination. The simulation combines real world user traces with artificial user mobility models, in order to model user movements more realistically. We investigate our opportunistic data dissemination process under various settings, including different communication ranges and user behavior patterns. Our results depict, within the limits of our model and assumptions, a good performance of the data dissemination process

The sources and characteristics of electronic evidence and artificial intelligence

In this updated edition of the well-established practitioner text, Stephen Mason and Daniel Seng have brought together a team of experts in the field to provide an exhaustive treatment of electronic evidence and electronic signatures. This fifth edition continues to follow the tradition in English evidence text books by basing the text on the law of England and Wales, with appropriate citations of relevant case law and legislation from other jurisdictions