We present an integrated optoelectronic CMOS biochip based photometer for quantitative immunoassay diagnostics. The application specific integrated circuit (ASIC) includes a 6 × 7 CMOS sensor array with 100 μm × 100 μm photodiodes acting as signal transducers. The ASIC was designed in a standard 0.35-μm CMOS technology without expensive post-CMOS processes. The principle of the photometer is based on light absorption measurement. The implemented light control reduces start-up and settling time of the light source to 30 seconds, enables fast starts of measurements and reduces overall measurement times. With this approach, we demonstrate for the first time the quantification of prostate-specific antigen (PSA) using an optoelectronic CMOS biochip. A PSA immunoassay is performed on the top surface of the CMOS sensor array, enzyme kinetics and PSA concentrations are measured within 6minutes with a limit-of-detection (LoD) of 0.5 ng/ml. The LoD meets the requirement for clinical testing, since the cutoff level of PSA is 4 ng/ml. Additionally, we achieve an overall run-to-run coefficient of variations (CV) of 7% which is good compared to other point-of-care (PoC) systems.

Many proteins have the potential to aggregate into amyloid fibrils, protein polymers associated with a wide range of human disorders such as Alzheimer's and Parkinson's disease. The thermodynamic stability of amyloid fibrils, in contrast to that of folded proteins, is not well understood: the balance between entropic and enthalpic terms, including the chain entropy and the hydrophobic effect, are poorly characterised. Using a combination of theory, in vitro experiments, simulations of a coarse-grained protein model and meta-data analysis, we delineate the enthalpic and entropic contributions that dominate amyloid fibril elongation. Our prediction of a characteristic temperature-dependent enthalpic signature is confirmed by the performed calorimetric experiments and a meta-analysis over published data. From these results we are able to define the necessary conditions to observe cold denaturation of amyloid fibrils. Overall, we show that amyloid fibril elongation is associated with a negative heat capacity, the magnitude of which correlates closely with the hydrophobic surface area that is buried upon fibril formation, highlighting the importance of hydrophobicity for fibril stability.

In extensive trials we asked participants with disabilities to play a set of computer games, demanding a variety of cognitive and physical skills. In particular we presented a Tetris variant we developed ourselves, which follows various includification guidelines. Participants were recorded on video in order to identify strengths and weaknesses in coping with the presented game challenges. In the videos we recorded the game screen, a frontal video of the participants showing emotional responses, and eye gaze. Post processing tasks are used to analyze and retrieve data from the videos. This helps to build up a video database for later analysis and answer questions raised after the recordings were taken.

Alert aggregation is an important subtask of intrusion detection. The goal is to identify and to cluster different alerts-produced by low-level intrusion detection systems, firewalls, etc.-belonging to a specific attack instance which has been initiated by an attacker at a certain point in time. Thus, meta-alerts can be generated for the clusters that contain all the relevant information whereas the amount of data (i.e., alerts) can be reduced substantially. Meta-alerts may then be the basis for reporting to security experts or for communication within a distributed intrusion detection system. We propose a novel technique for online alert aggregation which is based on a dynamic, probabilistic model of the current attack situation. Basically, it can be regarded as a data stream version of a maximum likelihood approach for the estimation of the model parameters. With three benchmark data sets, we demonstrate that it is possible to achieve reduction rates of up to 99.96 percent while the number of missing meta-alerts is extremely low. In addition, meta-alerts are generated with a delay of typically only a few seconds after observing the first alert belonging to a new attack instance.

Assessments are an effective technique for software quality assurance. As global software development (GSD) becomes the standard, an assessment framework must be flexible to support different sourcing and shoring models. Although much work exists on inspections and reviews, an assessment framework which addresses these challenges is missing. We present a systematic yet flexible assessment framework. The paper contributes: i) The description of our assessment framework which addresses four challenges: Appropriateness of a software requirements specification (SRS), viability of software architectures and SRS, wholeness of work packages, and compliance of results with predefined quality objectives. ii) A detailed explanation how the assessment framework can be tailored to support offshore and outsourcing scenarios. This paper describes the result of a two years research initiative at Capgemini sd&m and serves the practitioner to implement assessment frameworks according to his needs. We also discuss open research questions of high relevance for the software industry.

Distributed hash tables are popular third generation P2P protocols which are well understood in theory. These protocols usually assume that every node in the overlay is able to exchange messages with any other overlay node. However, this assumption is not always true for real-world networks, including the PlanetLab or the entire Internet. In these networks, the non-transitive connectivity phenomenon is experienced, in which some overlay nodes are able to exchange messages with a certain node and others are not. This turned out to be a serious problem, particularly for structured P2P overlays. Non-transitive connectivity issues were mainly ignored by P2P research for a long time, but have been intensively discussed recently. This paper suggests a new measure for the degree of non-transitive connectivity and presents a comprehensive, source routing based solution, to overcome non-transitive connectivity problems in distributed hash tables.

Distributed intrusion detection and prevention plays an increasingly important role in securing computer networks. In a distributed intrusion detection system, alerts or high-level meta-alerts are exchanged, aggregated, and correlated in a cooperative fashion to overcome the limitations of conventional intrusion detection systems. Substantial progress has been made, but current systems still suffer from various drawbacks: Most of them only distribute the data collection and not the analysis itself or they rely on a hierarchical or even centralized organization and/or communication architecture. Furthermore, the alerts or meta-alerts are usually aggregated at a pre-defined location and there is no reduction of the vast amount of alerts prior to distribution. Consequently, scalability is limited and any central component in the architecture introduces a "single point of failure ". We propose a completely distributed intrusion detection system based on distributed hash tables to efficiently exchange and aggregate alerts and meta-alerts in a cooperative, self-organizing, and load-balanced way. Independent intrusion detection agents publish their alerts based on a new novelty measure for alerts which prohibits the distribution of already known and hence worthless knowledge. The benefits of our approach are evaluated for a well-known probing attack.

Content pollution is a de facto problem in several currently popular Peer-to-Peer file sharing systems. Previously proposed strategies to fight content pollution include a peer reputation system named Scrubber as well as the alternative Credence object reputation system. This paper builds on previous work into three directions. First, it evaluates the cost-effectiveness of Scrubber and Credence in reducing pollution dissemination when polluters make use of collusion and Sybil attacks. Second, it modifies the Scrubber system to increase its effectiveness, in particular under collusion. Finally, it proposes a hybrid peer and object reputation system that combines the benefits of both strategies. We performed an extensive evaluation of all three systems, for various configurations and pollution mechanisms, as well as collusion and Sybil attacks. Our results show that the new hybrid system is much more effective and robust to malicious actions than any individual strategy, even in very uncooperative and unreliable peer communities.

With the ongoing growth of the Internet, intrusion detection systems (IDS) play an increasing role in securing communication networks. Particularly, where sensitive and confidential information is stored or transmitted, there is a vital importance of security. In the past few years, soft-computing techniques (especially neural networks) found their way more and more into the research area of intrusion detection and are now an inherent part of it. Although, feature selection is an important task for almost all neural network applications, only very few investigations dealing with any type of automated feature selection are known in the area of intrusion detection. This article sets out an evolutionary algorithm (EA) that performs the tasks of feature selection and architecture optimization for radial basis function (RBF) networks automatically. With the feature selection process proposed, it is possible to reduce the number of input features significantly, which is very important due to the fact that the neural networks can effectively be prevented from overfitting. Furthermore, reducing the number of input features also reduces the time needed for feature extraction as well as the execution time for the trained RBF networks and allows the extraction of well interpretable rules. The results (depending on the attack type, about 2 up to 5 features are needed for attack detection on an average) are demonstrated for seven attack types using the DARPA 1998 intrusion detection evaluation data.

Feature selection and architecture optimization are two key tasks in most neural network applications. Appropriate input features must be selected from a given (and often large) set of possible features and architecture parameters of the network such as the number of hidden neurons or learning parameters must be adapted with respect to the selected features and a learning data set. This article sets out an evolutionary algorithm (EA) that performs the tasks simultaneously for radial basis function (RBF) networks. The feasibility and the benefits of this approach are demonstrated in an application in the area of computer security: the detection of attacks (intrusive behavior) in computer networks. The EA, however, is independent from the application example given so that the ideas and solutions may easily be transferred to other applications and even other neural network paradigms. In the application example investigated overall classification rates of about 99.4% (average of eight attack types) can be reached for independent validation data.

In many neural network applications, the understanding of the network functionality is an important issue. Trained neural networks are often termed as "black boxes" which do not allow to get a deeper insight into the relationships between the input (feature) and output spaces. In the past years some researchers addressed the problem of rule extraction from trained neural networks. This article investigates the properties and results of different rule extraction techniques in a specific application area: The detection of intrusions in computer networks. This application area is chosen because intrusion detection and the development of appropriate intrusion detection systems (IDS) gains more and more importance with the rapidly increasing impact of the Internet.