scholarly journals Instruction2vec: Efficient Preprocessor of Assembly Code to Detect Software Weakness with CNN

2019 ◽  
Vol 9 (19) ◽  
pp. 4086 ◽  
Author(s):  
Yongjun Lee ◽  
Hyun Kwon ◽  
Sang-Hoon Choi ◽  
Seung-Ho Lim ◽  
Sung Hoon Baek ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Experimental Results ◽  
New Method ◽  
Binary Analysis ◽  
Vulnerability Detection ◽  
Security Vulnerabilities ◽  
Assembly Code ◽  
Detection Techniques ◽  
Software Vulnerabilities ◽  
Analysis Technique

Potential software weakness, which can lead to exploitable security vulnerabilities, continues to pose a risk to computer systems. According to Common Vulnerability and Exposures, 14,714 vulnerabilities were reported in 2017, more than twice the number reported in 2016. Automated vulnerability detection was recommended to efficiently detect vulnerabilities. Among detection techniques, static binary analysis detects software weakness based on existing patterns. In addition, it is based on existing patterns or rules, making it difficult to add and patch new rules whenever an unknown vulnerability is encountered. To overcome this limitation, we propose a new method—Instruction2vec—an improved static binary analysis technique using machine. Our framework consists of two steps: (1) it models assembly code efficiently using Instruction2vec, based on Word2vec; and (2) it learns the features of software weakness code using the feature extraction of Text-CNN without creating patterns or rules and detects new software weakness. We compared the preprocessing performance of three frameworks—Instruction2vec, Word2vec, and Binary2img—to assess the efficiency of Instruction2vec. We used the Juliet Test Suite, particularly the part related to Common Weakness Enumeration(CWE)-121, for training and Securely Taking On New Executable Software of Uncertain Provenance (STONESOUP) for testing. Experimental results show that the proposed scheme can detect software vulnerabilities with an accuracy of 91% of the assembly code.

Discriminant DCT Feature Extraction

2006 ◽  
pp. 205-221
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Discrete Cosine Transform ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Experimental Results ◽  
Cosine Transform ◽  
Analysis Technique

This chapter provides a feature extraction approach that combines the discrete cosine transform (DCT) with LDA. The DCT-based frequency-domain analysis technique is introduced first. Then, we describe the presented discriminant DCT approach and analyze its theoretical properties. Finally, we offer detailed experimental results and a chapter summary.

An E-nose and Convolution Neural Network based Recognition Method for Processed Products of Crataegi Fructus

2020 ◽  
Vol 23 ◽  
Author(s):  
Tianshu Wang ◽  
Yanpin Chao ◽  
Fangzhou Yin ◽  
Xichen Yang ◽  
Chenjun Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Electronic Nose ◽  
Experimental Results ◽  
Convolution Neural Network ◽  
New Method ◽  
Recognition Method ◽  
Processed Products

Background: The identification of Fructus Crataegi processed products manually is inefficient and unreliable. Therefore, how to identify the Fructus Crataegis processed products efficiently is important. Objective: In order to efficiently identify Fructus Grataegis processed products with different odor characteristics, a new method based on an electronic nose and convolutional neural network is proposed. Methods: First, the original smell of Fructus Grataegis processed products is obtained by using the electronic nose and then preprocessed. Next, feature extraction is carried out on the preprocessed data through convolution pooling layer Results: The experimental results show that the proposed method has higher accuracy for the identification of Fructus Grataegis processed products, and is competitive with other machine learning based methods. Conclusion: The method proposed in this paper is effective for the identification of Fructus Grataegi processed products.

Direct Orthogonal Neighborhood Preserving Discriminant Analysis

2011 ◽  
Vol 179-180 ◽  
pp. 1254-1259
Author(s):  
Yu Rong Lin ◽  
Qiang Wang
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Recognition Rate ◽  
Feature Space ◽  
Experimental Results ◽  
New Method ◽  
High Dimensional ◽  
Singularity Problem

Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods don’t address the singularity problem in the high dimensional feature space,which means that the eigen-equation of orthogonal feature extraction algorithms cannot be solved directly. In this paper, we present a new method called Direct Orthogonal Neighborhood Preserving Discriminant Analysis (DONPDA), which is able to extract all the orthogonal discriminant vectors simultaneously in the high-dimensional feature space and does not suffer the singularity problem. Experimental results on ORL database indicate that the proposed DONPDA method achieves higher recognition rate than the ONPDA method and other some existing orthogonal feature extraction algorithms.

scholarly journals Histogram of Oriented Gradient Based Gist Feature for Building Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Li ◽  
Kaili Cheng ◽  
Zhezhou Yu
Keyword(s):  
Feature Extraction ◽  
Traditional Approach ◽  
Gabor Filters ◽  
Experimental Results ◽  
New Method ◽  
Histogram Of Oriented Gradient ◽  
Feature Vectors ◽  
Building Recognition ◽  
Gradient Based

We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.

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.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

A new method for monitoring the redox potential of fuel salt based on the deposition of 95Nb on Hastelloy C276

2021 ◽  
Vol 109 (5) ◽  
pp. 357-365
Author(s):  
Zhiqiang Cheng ◽  
Zhongqi Zhao ◽  
Junxia Geng ◽  
Xiaohe Wang ◽  
Jifeng Hu ◽  
...  
Keyword(s):  
Redox Potential ◽  
Molten Salt ◽  
Specific Activity ◽  
Chemical Reduction ◽  
Quantitative Approach ◽  
Experimental Results ◽  
New Method ◽  
Molten Salt Reactor ◽  
Fuel Salt ◽  
Well Correlation

Abstract To develop the application of 95Nb as an indicator of redox potential for fuel salt in molten salt reactor (MSR), the specific activity of 95Nb in FLiBe salt and its deposition of 95Nb on Hastelloy C276 have been studied. Experimental results indicated that the amount of 95Nb deposited on Hastelloy C276 resulted from its chemical reduction exhibited a positive correlation with the decrease of 95Nb activity in FLiBe salt and the relative deposition coefficient of 95Nb to 103Ru appeared a well correlation with 95Nb activity in FLiBe salt. Both correlations implied that the measurement of 95Nb activity deposited on Hastelloy C276 specimen might provide a quantitative approach for monitoring the redox potential of fuel salt in MSR.

scholarly journals A new method of inferring the size, number density, and charge of mesospheric dust from its in situ collection by the DUSTY probe

2019 ◽  
Vol 12 (3) ◽  
pp. 1673-1683 ◽  
Author(s):  
Ove Havnes ◽  
Tarjei Antonsen ◽  
Gerd Baumgarten ◽  
Thomas W. Hartquist ◽  
Alexander Biebricher ◽  
...  
Keyword(s):  
New Method ◽  
Production Model ◽  
Dust Particles ◽  
Bottom Plate ◽  
Charged Dust ◽  
Fundamental Parameters ◽  
Dust Charge ◽  
Analysis Technique ◽  
June July ◽  
The Impact

Abstract. We present a new method of analyzing measurements of mesospheric dust made with DUSTY rocket-borne Faraday cup probes. It can yield the variation in fundamental dust parameters through a mesospheric cloud with an altitude resolution down to 10 cm or less if plasma probes give the plasma density variations with similar height resolution. A DUSTY probe was the first probe that unambiguously detected charged dust and aerosol particles in the Earth's mesosphere. DUSTY excluded the ambient plasma by various biased grids, which however allowed dust particles with radii above a few nanometers to enter, and it measured the flux of charged dust particles. The flux measurements directly yielded the total ambient dust charge density. We extend the analysis of DUSTY data by using the impact currents on its main grid and the bottom plate as before, together with a dust charging model and a secondary charge production model, to allow the determination of fundamental parameters, such as dust radius, charge number, and total dust density. We demonstrate the utility of the new analysis technique by considering observations made with the DUSTY probes during the MAXIDUSTY rocket campaign in June–July 2016 and comparing the results with those of other instruments (lidar and photometer) also used in the campaign. In the present version we have used monodisperse dust size distributions.

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

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