Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

Authenicated action and the decision to stop smoking.

In this rational reconstruction, two rival research programmes are identified as dominating the Social Psychology of decision making. Behavioral Decision Theory and the Theory of Reasoned Action embody the Rationalist programme. Social Judgment Theory and Attributional Theory exemplify the Empiricist programme. As predicted by the Methodology of Scientific Research Programmes (MSRP), the negative heuristics are shown to condense as hard cores which remain protected from refutation. The historical reconstruction of Social Judgment Theory illustrates uneven development in algorithmic and propositional heuristics. Behavioral Decision Theory shows a progressive problem shift to Multi Attribute Utility Theory (MAUT). In a revision of MSRP to include practice shifts, the Theory of Reasoned Action illustrates progressive practice despite empirical anomalies. Attributional theory shows a progressive problem shift by predicting personal-efficacy to influence choice. Practice, however, is restrained through reliance on the ANOVA paradigm. The experimental study partitioned locus and stability attributes for subjects' choice of therapy programmes in an anti-smoking clinic. A significant main effect was found for stability expectancy, though this did not influence choice. The Lens Model algorithm was demonstrated to transpose successfully onto the Self-efficacy model with the intra-system capturing decisions combining the two forms of efficacy expectation. The Theory of Reasoned Action was augmented by transfer of MAUT techniques giving relative weighting to salience. Though Rationalist and Empiricist paradigms illuminate some aspects of stopping smoking, neither adequately addresses the decision-action gap perceived by smokers who disown their original intentions when the the correspondence is seen as inauthentic. An alternative model is proposed with a basis in Objectivist epistemology. Authenticated action is explained as a means of arriving at decisions through consideration of problem and practice shifts at the individual level

Specification and use of component failure patterns

Safety-critical systems are typically assessed for their adherence to specified safety properties. They are studied down to the component-level to identify root causes of any hazardous failures. Most recent work with model-based safety analysis has focused on improving system modelling techniques and the algorithms used for automatic analyses of failure models. However, few developments have been made to improve the scope of reusable analysis elements within these techniques. The failure behaviour of components in these techniques is typically specified in such a way that limits the applicability of such specifications across applications. The thesis argues that allowing more general expressions of failure behaviour, identifiable patterns of failure behaviour for use within safety analyses could be specified and reused across systems and applications where the conditions that allow such reuse are present.This thesis presents a novel Generalised Failure Language (GFL) for the specification and use of component failure patterns. Current model-based safety analysis methods are investigated to examine the scope and the limits of achievable reuse within their analyses. One method, HiP-HOPS, is extended to demonstrate the application of GFL and the use of component failure patterns in the context of automated safety analysis. A managed approach to performing reuse is developed alongside the GFL to create a method for more concise and efficient safety analysis. The method is then applied to a simplified fuel supply and a vehicle braking system, as well as on a set of legacy models that have previously been analysed using classical HiP-HOPS. The proposed GFL method is finally compared against the classical HiP-HOPS, and in the light of this study the benefits and limitations of this approach are discussed in the conclusions

Methods for Quality Monitoring in Ultrasonic Welding of Carbon Fiber Reinforced Polymer Composites

Carbon fiber reinforced composites have been increasingly used in various industrial sectors, especially in the automotive industry. Ultrasonic welding is considered as an effective approach to joining such composites. Reliable weld quality classification and prediction methods are needed to ensure quality and reduce manufacturing costs. However, existing methods have two weaknesses. The first one is that the majority of the existing methods are based on signal feature data extracted from the original experimental time-series data. Feature-based models may not take full advantage of the information contained in the large amounts of time-series data available, even though the models are simple and easy to program. On the other hand, when using experimental time-series data to conduct weld quality monitoring, the data size may be insufficient for training neural network-based methods for quality monitoring or classification. Therefore, a method is needed to augment experimental data while preserving the statistical characteristics of the experimental data. To find reliable quality monitoring models in various situations, this dissertation proposes two neural network models that are respectively applied to feature-based data and full time-series-based data and compares their performances. The dissertation first investigates the relationship between weld energy and joint performance in ultrasonic welding of carbon fiber reinforced polymer (CFRP) sheets through weld experiments. The weld quality classes for training quality monitoring algorithms are determined from welded joint lap-shear strength and the microstructure of the weld zone. These pre-defined weld quality classes are the output criteria for weld quality monitoring on feature-based models and time-series-based models. For feature- based weld quality monitoring, a simple and efficient feature selection method is first developed to screen the most significant features for classification from multiple weld quality classes. A Bayesian regularized neural network (BRNN) is then demonstrated to be more accurate and robust when classifying weld quality classes in ultrasonic composite welding when using feature-based data as the input than the previously proposed methods of support vector machine (SVM), k-nearest neighbors (kNN), and linear discriminant analysis (LDA). To address the limited size of experimental data, a Multivariate Monte Carlo (MMC) simulation with copulas approach is proposed to reasonably generate large amounts of time-series process signals for ultrasonic composite welding. With both experimental data and a large quantity of simulated data, a deep convolutional neural network (CNN) is applied to weld quality classification. The CNN model is found to be more accurate and robust, not only under small training data set sizes, but also under large training data set sizes when compared with previously researched classification methods applied in ultrasonic welding. In conclusion, neural network-based models could achieve high accuracy using feature signals and the full time-series process signals.Ph.D.Manufacturing EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/168232/1/Dissertation_Lei Sun.pd

Earthquakes, Tsunamis and Nuclear Risks

Environmental Managemen