Dos and Don'ts of Machine Learning in Computer Security

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This development has influenced computer security, spawning a series of work on learning-based security systems, such as for malware detection, vulnerability discovery, and binary code analysis. Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance and render learning-based systems potentially unsuitable for security tasks and practical deployment. In this paper, we look at this problem with critical eyes. First, we identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We conduct a study of 30 papers from top-tier security conferences within the past 10 years, confirming that these pitfalls are widespread in the current security literature. In an empirical analysis, we further demonstrate how individual pitfalls can lead to unrealistic performance and interpretations, obstructing the understanding of the security problem at hand. As a remedy, we propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible. Furthermore, we identify open problems when applying machine learning in security and provide directions for further research.Comment: to appear at USENIX Security Symposium 202

Lessons Learned on Machine Learning for Computer Security

We identify 10 generic pitfalls that can affect the experimental outcome of AI driven solutions in computer security. We find that they are prevalent in the literature and provide recommendations for overcoming them in the future

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Fusion and Binary-Decay Mechanisms in the 35^{35}Cl+24^{24}Mg System at E/A ≈\approx 8 MeV/Nucleon

Compound-nucleus fusion and binary-reaction mechanisms have been investigated for the $^{35}$Cl+$^{24}$Mg system at an incident beam energy of E$_{Lab}$= 282 MeV. Charge distributions, inclusive energy spectra, and angular distributions have been obtained for the evaporation residues and the binary fragments. Angle-integrated cross sections have been determined for evaporation residues from both the complete and incomplete fusion mechanisms. Energy spectra for binary fragment channels near to the entrance-channel mass partition are characterized by an inelastic contribution that is in addition to a fully energy damped component. The fully damped component which is observed in all the binary mass channels can be associated with decay times that are comparable to, or longer than the rotation period. The observed mass-dependent cross sections for the fully damped component are well reproduced by the fission transition-state model, suggesting a fusion followed by fission origin. The present data cannot, however, rule out the possibility that a long-lived orbiting mechanism accounts for part or all of this yield.Comment: 41 pages standard REVTeX file, 14 Figures available upon request -

Detection of North American orthopoxviruses by real time-PCR

The prevalence of North American orthopoxviruses in nature is unknown and may be more difficult to ascertain due to wide spread use of vaccinia virus recombinant vaccines in the wild. A real time PCR assay was developed to allow for highly sensitive and specific detection of North American orthopoxvirus DNA in animal tissues and bodily fluids. This method is based on the amplification of a 156 bp sequence within a myristylated protein, highly conserved within the North American orthopoxviruses but distinct from orthologous genes present in other orthopoxviruses. The analytical sensitivity was 1.1 fg for Volepox virus DNA, 1.99 fg for Skunkpox virus DNA, and 6.4 fg for Raccoonpox virus DNA with a 95% confidence interval. Our assay did not cross-react with other orthopoxviruses or ten diverse representatives of the Chordopoxvirinae subfamily. This new assay showed more sensitivity than tissue culture tests, and was capable of differentiating North American orthopoxviruses from other members of Orthopoxvirus. Thus, our assay is a promising tool for highly sensitive and specific detection of North American orthopoxviruses in the United States and abroad

An expression signature of syndecan-1 (CD138), E-cadherin and c-met is associated with factors of angiogenesis and lymphangiogenesis in ductal breast carcinoma in situ

INTRODUCTION: Heparan sulphate proteoglycan syndecan-1 modulates cell proliferation, adhesion, migration and angiogenesis. It is a coreceptor for the hepatocyte growth factor receptor c-met, and its coexpression with E-cadherin is synchronously regulated during epithelial-mesenchymal transition. In breast cancer, changes in the expression of syndecan-1, E-cadherin and c-met correlate with poor prognosis. In this study we evaluated whether coexpression of these functionally linked prognostic markers constitutes an expression signature in ductal carcinoma in situ (DCIS) of the breast that may promote cell proliferation and (lymph)angiogenesis. METHODS: Expression of syndecan-1, E-cadherin and c-met was detected immunohistochemically using a tissue microarray in tumour specimens from 200 DCIS patients. Results were correlated with the expression patterns of angiogenic and lymphangiogenic markers. Coexpression of the three prognostic markers was evaluated in human breast cancer cells by confocal immunofluorescence microscopy and RT-PCR. RESULTS: Coexpression and membrane colocalization of the three markers was confirmed in MCF-7 cells. E-cadherin expression decreased, and c-met expression increased progressively in more aggressive cell lines. Tissue microarray analysis revealed strong positive staining of tumour cells for syndecan-1 in 72%, E-cadherin in 67.8% and c-met in 48.6% of DCIS. E-cadherin expression was significantly associated with c-met and syndecan-1. Expression of c-met and syndecan-1 was significantly more frequent in the subgroup of patients with pure DCIS than in those with DCIS and a coexisting invasive carcinoma. Levels of c-met and syndecan-1 expression were associated with HER2 expression. Expression of c-met significantly correlated with expression of endothelin A and B receptors, vascular endothelial growth factor (VEGF)-A and fibroblast growth factor receptor-1, whereas E-cadherin expression correlated significantly with endothelin A receptor, VEGF-A and VEGF-C staining. CONCLUSION: Syndecan-1, E-cadherin and c-met constitute a marker signature associated with angiogenic and lymphangiogenic factors in DCIS. This coexpression may reflect a state of parallel activation of different signal transduction pathways, promoting tumour cell proliferation and angiogenesis. Our findings have implications for future therapeutic approaches in terms of a multiple target approach, which may be useful early in breast cancer progression

Proagonaler Euergetismus. Privates "Sport-Sponsoring" nach römischen Rechtsquellen

Darstellung lebzeitiger Zweckzuwendungen privater Stifter mittels pollicitatio zur Veranstaltung von Wettkämpfen anhand von Dig. 50,12,10