Optimization Method of Web Fuzzy Test Cases Based on Genetic Algorithm

In order to solve the problems of low code coverage, few vulnerabilities found, and poor fuzzing effect caused by the small number of test cases and single types in Web fuzzing, on the basis of studying the current Web fuzzing methods, the existing fuzzing Web applications are tested Program research. A genetic algorithm-based method for optimizing fuzzing test cases for Web applications is proposed. It analyzes and counts the traffic of public network website business with Web service attack characteristics, and uses genetic algorithms to generate a large number of test cases with various types to explore the Web service vulnerability that exists. Based on the creation of a Web attack signature database with weights, this method uses genetic algorithms to randomly pre-generate the test cases of the fuzzing test, and uses the response of the Web service to repeatedly iterate the weights of different attack signatures in the Web attack signature database. So as to generate the best test cases. Experimental analysis shows that this method effectively finds security vulnerabilities in Web applications.

Offline signature recognition system using oriented FAST and rotated BRIEF

Despite recent developments in offline signature recognition systems, there is however limited focus on the recognition problem facet of using an inadequate sample size for training that could deliver reliable and easy to use authentication systems. Signature recognition systems are one of the most popular biometric authentication systems. They are regarded as non-invasive, socially accepted, and adequately precise. Research on offline signature recognition systems still has not shown competent results when a limited number of signatures are used. This paper describes our proposed practical offline signature recognition system using the oriented FAST and rotated BRIEF (ORB) feature extraction algorithm. We focus on the practicality of the proposed system, which requires only the minimum number of signatures per user to achieve a high level of fidelity. We manifest the practicality of our approach with a signature database of 300 signatures from 100 different individuals, implying that only two signatures are needed per person to train the proposed system. Our proposed solution achieves a 91% recognition rate with a median matching time of only 7 ms.

Immune Signature Linked to COVID-19 Severity: A SARS-Score for Personalized Medicine

SARS-CoV-2 infection leads to a highly variable clinical evolution, ranging from asymptomatic to severe disease with acute respiratory distress syndrome, requiring intensive care units (ICU) admission. The optimal management of hospitalized patients has become a worldwide concern and identification of immune biomarkers predictive of the clinical outcome for hospitalized patients remains a major challenge. Immunophenotyping and transcriptomic analysis of hospitalized COVID-19 patients at admission allow identifying the two categories of patients. Inflammation, high neutrophil activation, dysfunctional monocytic response and a strongly impaired adaptive immune response was observed in patients who will experience the more severe form of the disease. This observation was validated in an independent cohort of patients. Using in silico analysis on drug signature database, we identify differential therapeutics that specifically correspond to each group of patients. From this signature, we propose a score—the SARS-Score—composed of easily quantifiable biomarkers, to classify hospitalized patients upon arrival to adapt treatment according to their immune profile.

SPEED2: inferring upstream pathway activity from differential gene expression

Extracting signalling pathway activities from transcriptome data is important to infer mechanistic origins of transcriptomic dysregulation, for example in disease. A popular method to do so is by enrichment analysis of signature genes in e.g. differentially regulated genes. Previously, we derived signatures for signalling pathways by integrating public perturbation transcriptome data and generated a signature database called SPEED (Signalling Pathway Enrichment using Experimental Datasets), for which we here present a substantial upgrade as SPEED2. This web server hosts consensus signatures for 16 signalling pathways that are derived from a large number of transcriptomic signalling perturbation experiments. When providing a gene list of e.g. differentially expressed genes, the web server allows to infer signalling pathways that likely caused these genes to be deregulated. In addition to signature lists, we derive ‘continuous’ gene signatures, in a transparent and automated fashion without any fine-tuning, and describe a new algorithm to score these signatures.

Evaluation of Kernel Based Atanassov's Intuitionistic Fuzzy Clustering for Network Forensics and Intrusion Detection

Malware or virus is one of the most significant security threats in Internet. There are mainly two types of successful (partially) solutions available. One is anti-virus and other is backlisting. This kind of detection generally depends on the existing malware or virus signature database. Cyber-criminals bypass defenses by generating variants of their malware program. Traditional approach has limitations such as unable to detect zero day threats or generate so many false alerts et al. To overcome these difficulties, a system is built based on Atanassov's intuitionistic fuzzy set (AIFS) theory based clustering method that takes care of these problems in a robust way. It not only raises an alert for new kind of malware but also decreases the number of false alerts. This is done by giving it decision-making intelligence. There is not much work done in the field of network forensics using AIFS theory. Some clustering techniques are used in these fields but those have limitations like accuracy, performance or difficulty to cluster noisy data. This method clusters the malwares/viruses with high accuracy on the basis of severity. Experiments are performed on several pcap files with malware traffic to assess the performance and accuracy of the method and results are compared with different clustering algorithms.

Computational analysis of transcriptome signature repurposes low dose trifluoperazine for the treatment of fragile X syndrome in mouse model

ABSTRACTFragile X syndrome (FXS), caused by mutations in fragile X mental retardation 1 gene (FMR1), is a prevailing genetic disorder of intellectual disability and autism. Currently, there is no efficacious medication for FXS. Here, we use transcriptome landscape as a holistic molecular phenotype/endpoint to identify potential therapeutic intervention. Through in silico screening with public gene signature database, computational analysis of transcriptome profile in Fmr1 knockout (KO) neurons predicts therapeutic value of an FDA-approved drug trifluoperazine. Through experimental validation, we find that systemic administration of low dose trifluoperazine at 0.05 mg/kg attenuates multiple FXS- and autism-related behavioral symptoms. Moreover, computational analysis of transcriptome alteration caused by trifluoperazine suggests a new mechanism of action against PI3K (Phosphatidylinositol-4,5-bisphosphate 3-kinase) activity. Consistently, trifluoperazine suppresses PI3K activity and its down-stream targets Akt (protein kinase B) and S6K1 (S6 kinase 1) in neurons. Further, trifluoperazine normalizes the aberrantly elevated activity of Akt and S6K1 and enhanced protein synthesis in FXS mouse. In conclusion, our data demonstrate promising value of gene signature-based computation in identification of therapeutic strategy and repurposing drugs for neurological disorders, and suggest trifluoperazine as a potential practical treatment for FXS.