EMaP: Explainable AI with Manifold-based Perturbations

In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently-developed attack against perturbation-based methods.Comment: 29 page

A Deep Learning Approach to Network Intrusion Detection

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments

Comparative Analysis of Swine Antibody Responses Following Vaccination with Live-Attenuated and Killed African Swine Fever Virus Vaccines

African swine fever virus (ASFV) is circulating in many swine-producing countries, causing significant economic losses. It is observed that pigs experimentally vaccinated with a live-attenuated virus (LAV) but not a killed virus (KV) vaccine develop solid homologous protective immunity. The objective of this study was to comparatively analyze antibody profiles between pigs vaccinated with an LAV vaccine and those vaccinated with a KV vaccine to identify potential markers of vaccineinduced protection. Thirty ASFV seronegative pigs were divided into three groups: Group 1 received a single dose of an experimental LAV, Group 2 received two doses of an experimental KV vaccine, and Group 3 was kept as a non-vaccinated (NV) control. At 42 days post-vaccination, all pigs were challenged with the parental virulent ASFV strain and monitored for 21 days. All pigs vaccinated with the LAV vaccine survived the challenge. In contrast, eight pigs from the KV group and seven pigs from the NV group died within 14 days post-challenge. Serum samples collected on 41 days post-vaccination were analyzed for their reactivity against a panel of 29 viral structural proteins. The sera of pigs from the LAV group exhibited a strong antibody reactivity against various viral structural proteins, while the sera of pigs in the KV group only displayed weak antibody reactivity against the inner envelope (p32, p54, p12). There was a negative correlation between the intensity of antibody reactivity against five ASFV antigens, namely p12, p14, p15, p32, and pD205R, and the viral DNA titers in the blood of animals after the challenge infection. Thus, antibody reactivities against these five antigens warrant further evaluation as potential indicators of vaccine-induced protection

Deep Learning Combined with De - noising Data for Network Intrusion Detection

Anomaly-based Network Intrusion Detection Systems (NIDSs) are a common security defense for modern networks. The success of their operation depends upon vast quantities of training data. However, one major limitation is the inability of NIDS to be reliably trained using imbalanced datasets. Network observations are naturally imbalanced, yet without substantial data pre-processing, NIDS accuracy can be significantly reduced. With the diversity and dynamicity of modern network traffic, there are concerns that the current reliance upon un-natural balanced datasets cannot remain feasible in modern networks. This paper details our de-noising method, which when combined with deep learning techniques can address these concerns and offer accuracy improvements of between 1.5% and 4.5%. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs

Theory of the optical absorption of light carrying orbital angular momentum by semiconductors

We develop a free-carrier theory of the optical absorption of light carrying orbital angular momentum (twisted light) by bulk semiconductors. We obtain the optical transition matrix elements for Bessel-mode twisted light and use them to calculate the wave function of photo-excited electrons to first-order in the vector potential of the laser. The associated net electric currents of first and second-order on the field are obtained. It is shown that the magnetic field produced at the center of the beam for the $\ell=1$ mode is of the order of a millitesla, and could therefore be detected experimentally using, for example, the technique of time-resolved Faraday rotation.Comment: Submitted to Phys. Rev. Lett. (23 Jan 2008

Role of Esrrg in the Fibrate-Mediated Regulation of Lipid Metabolism Genes in Human ApoA-I Transgenic Mice

We have used a new ApoA-I transgenic mouse model to identify by global gene expression profiling, candidate genes that affect lipid and lipoprotein metabolism in response to fenofibrate treatment. Multilevel bioinformatical analysis and stringent selection criteria (2-fold change, 0% false discovery rate) identified 267 significantly changed genes involved in several molecular pathways. The fenofibrate-treated group did not have significantly altered levels of hepatic human APOA-I mRNA and plasma ApoA-I compared with the control group. However, the treatment increased cholesterol levels to 1.95-fold mainly due to the increase in high-density lipoprotein (HDL) cholesterol. The observed changes in HDL are associated with the upregulation of genes involved in phospholipid biosynthesis and lipid hydrolysis, as well as phospholipid transfer protein. Significant upregulation was observed in genes involved in fatty acid transport and β-oxidation, but not in those of fatty acid and cholesterol biosynthesis, Krebs cycle and gluconeogenesis. Fenofibrate changed significantly the expression of seven transcription factors. The estrogen receptor-related gamma gene was upregulated 2.36-fold and had a significant positive correlation with genes of lipid and lipoprotein metabolism and mitochondrial functions, indicating an important role of this orphan receptor in mediating the fenofibrate-induced activation of a specific subset of its target genes.National Institutes of Health (HL48739 and HL68216); European Union (LSHM-CT-2006-0376331, LSHG-CT-2006-037277); the Biomedical Research Foundation of the Academy of Athens; the Hellenic Cardiological Society; the John F Kostopoulos Foundatio

Behaviour-aware Malware Classification: Dynamic Feature Selection

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. The results achieved thus far have demonstrated promising improvements over existing approaches