The reliance on Network Function Virtualization (NFV) and Software-Defined Network (SDN) introduces a wide variety of security risks in Service Function Chain (SFC), necessitating the implementation of automated security measures to safeguard ongoing service delivery. To address the security risks faced by online SFCs and the shortcomings of traditional manual configuration, we introduce Intent-Based Networking (IBN) for the first time to propose an automatic security enhancement method through embedding Network Security Functions (NSFs). However, the diverse security requirements and performance requirements of SFCs pose significant challenges to the translation from intents to NSF embedding schemes, which manifest in two main aspects. In the logical orchestration stage, NSF composition consisting of NSF sets and their logical embedding locations will significantly impact the security effect. So security intent language model, a formalized method, is proposed to express the security intents. Additionally, NSF Embedding Model Generation Algorithm (EMGA) is designed to determine NSF composition by utilizing NSF capability label model and NSF collaboration model, where NSF composition can be further formulated as NSF embedding model. In the physical embedding stage, the differentiated service requirements among SFCs result in NSF embedded model obtained by EMGA being a multi-objective optimization problem with variable objectives. Therefore, Adaptive Security-aware Embedding Algorithm (ASEA) featuring adaptive link weight mapping mechanism is proposed to solve the optimal NSF embedding schemes. This enables the automatic translation of security intents into NSF embedding schemes, ensuring that both security requirements are met and service performance is guaranteed. We develop the system instance to verify the feasibility of intent translation solution, and massive evaluations demonstrate that ASEA algorithm has better performance compared with the existing works in the diverse requirement scenarios.

Enhancement of service function chain (SFC) security ability by composing virtual network functions (VNFs) and allocating resources considering their security attributes can address the vulnerability threats in cloud environments, which is an important means of attempting to secure SFCs at the deployment stage. However, existing works do not consider the vulnerability correlation of the multi-step attack chains when completing SFC deployment based on trustworthiness. This results in existing security orchestration methods ignoring the differences in trustworthiness among network entities and focusing only on local trust optimization; these steps effectively disrupt the attack chains to secure SFCs. In this article, an innovative hierarchical trust model is proposed to assess the differentiated trustworthiness among network entities caused by vulnerability correlation. On the basis of trustworthiness assessment, both virtual trust of VNF combinations at the SFC composition stage and physical trust of physical node (PN) selections at the SFC placement stage are globally considered to disrupt the attack chains in SFCs as much as possible. To this end, the security-aware and cost-efficient SFC composition and placement (SCSCP) problem is formulated as an integer linear programming (ILP) problem, which is NP-hard. To tackle the SCSCP problem, the joint trust and cost global optimization (JTCGO) algorithm is proposed to dynamically update the trustworthiness and globally find the SFC deployment solutions including the VNF combination schemes and PN selection schemes. Simulation results demonstrate that our proposed algorithm can provide the optimal SFC deployment solutions for requests and can guarantee the SFC trustworthiness at a controllable cost, thereby protecting SFCs from network attacks in complex security environments.

Abstract In network function virtualization, resource allocation is one of the key techniques to ensure the QoS of service requests in face of the uncertain traffic demand and traffic fluctuation of user services. The previous related works usually assume that the traffic demand is a deterministic value and then reallocate substrate resources to deal with the demand uncertainty and traffic fluctuation during operation. In response to performance degradation caused by demand uncertainty and traffic fluctuation, the paper models the service function chain orchestration under demand uncertainty as a robust optimization problem, where the parameter Γ is introduced to control the conservativeness of the solution. On this basis, the strong duality theory is used to transform the original problem into a mixed‐integer linear programming, and then devise an exact Robust Service Provisioning (RSP) algorithm. The simulation evaluation demonstrates that the proposed algorithm could achieve different levels of robustness and make a trade‐off between robustness and cost. The impact of Γ value on the realized robustness and price of robustness is also analysed. Thus, the algorithm proposed could get an effective service function chain orchestration scheme under uncertain traffic demands and provide a reference of the total cost.

BACKGROUND: Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data. RESULT: We developed MutScan to address problems of sensitive detection and efficient validation for target mutations. MutScan involves highly optimized string-searching algorithms, which can scan input FASTQ files to grab all reads that support target mutations. The collected supporting reads for each target mutation will be piled up and visualized using web technologies such as HTML and JavaScript. Algorithms such as rolling hash and bloom filter are applied to accelerate scanning and make MutScan applicable to detect or visualize target mutations in a very fast way. CONCLUSION: MutScan is a tool for the detection and visualization of target mutations by only scanning FASTQ raw data directly. Compared to conventional pipelines, this offers a very high performance, executing about 20 times faster, and offering maximal sensitivity since it can grab mutations with even one single supporting read. MutScan visualizes detected mutations by generating interactive pile-ups using web technologies. These can serve to validate target mutations, thus avoiding false positives. Furthermore, MutScan can visualize all mutation records in a VCF file to HTML pages for cloud-friendly VCF validation. MutScan is an open source tool available at GitHub: https://github.com/OpenGene/MutScan.

Plasma cell-free DNA (cfDNA) has certain fragmentation patterns, which can bring non-random base content curves of the sequencing data's beginning cycles. We studied the patterns and found that we could determine whether a sample is cfDNA or not by just looking into the first 10 cycles of its base content curves. We analysed 3189 FastQ files, including 1442 cfDNA, 1234 genomic DNA, 507 FFPE tumour DNA and 6 urinary cfDNA. By deep analysing these data, we find the patterns are stable enough to distinguish cfDNA from other kinds of DNA samples. Based on this finding, we build classification models to recognise cfDNA samples by their sequencing data. Pattern recognition models are then trained with different classification algorithms like k-nearest neighbours (KNN), random forest and support vector machine (SVM). The result of 1000 iteration .632+ bootstrapping shows that all these classifiers can give an average accuracy higher than 98%, indicating that the cfDNA patterns are unique and can make the dataset highly separable. The best result is obtained using random forest classifier with a 99.89% average accuracy (σ = 0.00068). A tool called CfdnaPattern (http://github.com/OpenGene/CfdnaPattern) has been developed to train the model and to predict whether a sample is cfDNA or not.

BACKGROUND: Some applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling. RESULTS: For each single or pair of FastQ files, AfterQC filters out bad reads, detects and eliminates sequencer's bubble effects, trims reads at front and tail, detects the sequencing errors and corrects part of them, and finally outputs clean data and generates HTML reports with interactive figures. AfterQC can run in batch mode with multiprocess support, it can run with a single FastQ file, a single pair of FastQ files (for pair-end sequencing), or a folder for all included FastQ files to be processed automatically. Based on overlapping analysis, AfterQC can estimate the sequencing error rate and profile the error transform distribution. The results of our error profiling tests show that the error distribution is highly platform dependent. CONCLUSION: Much more than just another new quality control (QC) tool, AfterQC is able to perform quality control, data filtering, error profiling and base correction automatically. Experimental results show that AfterQC can help to eliminate the sequencing errors for pair-end sequencing data to provide much cleaner outputs, and consequently help to reduce the false-positive variants, especially for the low-frequency somatic mutations. While providing rich configurable options, AfterQC can detect and set all the options automatically and require no argument in most cases.