scholarly journals D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis

2021 ◽  
Author(s):  
Yunhui Zheng ◽  
Saurabh Pujar ◽  
Burn Lewis ◽  
Luca Buratti ◽  
Edward Epstein ◽  
...  
Keyword(s):  
Detection Methods ◽  
Differential Analysis ◽  
Vulnerability Detection

scholarly journals Differential Analysis of RNA Structure Probing Experiments at Nucleotide Resolution: Uncovering Regulatory Functions of RNA Structure

2021 ◽  
Author(s):  
Bo Yu ◽  
Pan Li ◽  
Qiangfeng Cliff Zhang ◽  
Lin Hou
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Detection Methods ◽  
Systematic Bias ◽  
Differential Analysis ◽  
Variable Regions ◽  
Next Generation Sequencing Technology ◽  
Structure Probing ◽  
Nucleotide Resolution

AbstractRNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome identified multiple regions that form different structures in nucleus and cytoplasm, linking RNA structural variation to regulation of mRNAs encoding mitochondria-associated proteins. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

scholarly journals Possibilities of conducting XSS-attacks and the development of countermeasures

2020 ◽  
Vol 224 ◽  
pp. 01040
Author(s):  
Yelena Revyakina ◽  
Larissa Cherckesova ◽  
Olga Safaryan ◽  
Denis Korochentsev ◽  
Nikolay Boldyrikhin ◽  
...  
Keyword(s):  
Program Implementation ◽  
Web Sites ◽  
Software Implementation ◽  
Detection Methods ◽  
Vulnerability Detection ◽  
Detection Program ◽  
Investigation Process ◽  
The Web

The article describes the investigation process of the possibilities of XSS–attacks, and the development of counteraction means to these attacks. Researches were determined whether XSS–attack can be fulfilled successfully, and vulnerability detection methods can be applied; were developed the logical and structural diagrams of XSS–vulnerability detection program; were realized program implementation (software) of algorithms for detecting XSS–vulnerabilities on the Web – sites. The software implementation is Web extension for the Google Chrome browser. Main purpose of implementing this software is to confirm or deny the presence of XSS–vulnerabilities on the site, and to counteract the possible attack.

scholarly journals A Vulnerability Detection System Based on Fusion of Assembly Code and Source Code

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xingzheng Li ◽  
Bingwen Feng ◽  
Guofeng Li ◽  
Tong Li ◽  
Mingjin He
Keyword(s):  
Detection System ◽  
Source Code ◽  
Detection Methods ◽  
Alignment Algorithm ◽  
Vulnerability Detection ◽  
Timely Manner ◽  
Assembly Code ◽  
Software Vulnerabilities ◽  
Network Intrusion ◽  
Deep Learning Model

Software vulnerabilities are one of the important reasons for network intrusion. It is vital to detect and fix vulnerabilities in a timely manner. Existing vulnerability detection methods usually rely on single code models, which may miss some vulnerabilities. This paper implements a vulnerability detection system by combining source code and assembly code models. First, code slices are extracted from the source code and assembly code. Second, these slices are aligned by the proposed code alignment algorithm. Third, aligned code slices are converted into vector and input into a hyper fusion-based deep learning model. Experiments are carried out to verify the system. The results show that the system presents a stable and convergent detection performance.

scholarly journals Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning

2020 ◽  
Vol 10 (5) ◽  
pp. 1692 ◽  
Author(s):  
Xin Li ◽  
Lu Wang ◽  
Yang Xin ◽  
Yixian Yang ◽  
Yuling Chen
Keyword(s):  
Detection Method ◽  
False Positive Rate ◽  
Source Code ◽  
Representation Learning ◽  
Detection Methods ◽  
Intermediate Representation ◽  
Vulnerability Detection ◽  
Network Intrusion ◽  
Intelligent Methods ◽  
High Level

Vulnerability is one of the root causes of network intrusion. An effective way to mitigate security threats is to discover and patch vulnerabilities before an attack. Traditional vulnerability detection methods rely on manual participation and incur a high false positive rate. The intelligent vulnerability detection methods suffer from the problems of long-term dependence, out of vocabulary, coarse detection granularity and lack of vulnerable samples. This paper proposes an automated and intelligent vulnerability detection method in source code based on the minimum intermediate representation learning. First, the sample in the form of source code is transformed into a minimum intermediate representation to exclude the irrelevant items and reduce the length of the dependency. Next, the intermediate representation is transformed into a real value vector through pre-training on an extended corpus, and the structure and semantic information are retained. Then, the vector is fed to three concatenated convolutional neural networks to obtain high-level features of vulnerability. Last, a classifier is trained using the learned features. To validate this vulnerability detection method, an experiment was performed. The empirical results confirmed that compared with the traditional methods and the state-of-the-art intelligent methods, our method has a better performance with fine granularity.

scholarly journals Automated Software Vulnerability Detection Based on Hybrid Neural Network

2021 ◽  
Vol 11 (7) ◽  
pp. 3201
Author(s):  
Xin Li ◽  
Lu Wang ◽  
Yang Xin ◽  
Yixian Yang ◽  
Qifeng Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Detection Methods ◽  
Buffer Size ◽  
Intermediate Representation ◽  
Vulnerability Detection ◽  
Global Features ◽  
Hybrid Neural Network ◽  
Structure Information ◽  
High Level

Vulnerabilities threaten the security of information systems. It is crucial to detect and patch vulnerabilities before attacks happen. However, existing vulnerability detection methods suffer from long-term dependency, out of vocabulary, bias towards global features or local features, and coarse detection granularity. This paper proposes an automatic vulnerability detection framework in source code based on a hybrid neural network. First, the inputs are transformed into an intermediate representation with explicit structure information using lower level virtual machine intermediate representation (LLVM IR) and backward program slicing. After the transformation, the size of samples and the size of vocabulary are significantly reduced. A hybrid neural network model is then applied to extract high-level features of vulnerability, which learns features both from convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The former is applied to learn local vulnerability features, such as buffer size. Furthermore, the latter is utilized to learn global features, such as data dependency. The extracted features are made up of concatenated outputs of CNN and RNN. Experiments are performed to validate our vulnerability detection method. The results show that our proposed method achieves excellent results with F1-scores of 98.6% and accuracy of 99.0% on the SARD dataset. It outperforms state-of-the-art methods.

DNA nick end labeling: a reliable marker for apoptosis?

1995 ◽  
Vol 53 ◽  
pp. 962-963
Author(s):  
Anne F. Bushnell ◽  
Sarah Webster ◽  
Lynn S. Perlmutter
Keyword(s):  
Cell Death ◽  
Cultured Cells ◽  
Apoptotic Cell ◽  
Morphological Characteristics ◽  
Detection Methods ◽  
Tissue Preparation ◽  
Preparation Methods ◽  
Dna Laddering ◽  
Disease States ◽  
Reliable Marker

Apoptosis, or programmed cell death, is an important mechanism in development and in diverse disease states. The morphological characteristics of apoptosis were first identified using the electron microscope. Since then, DNA laddering on agarose gels was found to correlate well with apoptotic cell death in cultured cells of dissimilar origins. Recently numerous DNA nick end labeling methods have been developed in an attempt to visualize, at the light microscopic level, the apoptotic cells responsible for DNA laddering.The present studies were designed to compare various tissue processing techniques and staining methods to assess the occurrence of apoptosis in post mortem tissue from Alzheimer's diseased (AD) and control human brains by DNA nick end labeling methods. Three tissue preparation methods and two commercial DNA nick end labeling kits were evaluated: the Apoptag kit from Oncor and the Biotin-21 dUTP 3' end labeling kit from Clontech. The detection methods of the two kits differed in that the Oncor kit used digoxigenin dUTP and anti-digoxigenin-peroxidase and the Clontech used biotinylated dUTP and avidinperoxidase. Both used 3-3' diaminobenzidine (DAB) for final color development.

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