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Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery

2021 International Journal of Advanced Trends in Computer Science and Engineering  
Today's age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both techniques play a significant role in measuring vulnerability prediction accuracy.  ...  The meaning of using weakness with the same risk might go to misperception.  ...  Further study appraised that C and C++ open-source code present a large-scale function level for vulnerability detection using ML.  ... 
doi:10.30534/ijatcse/2021/871032021 fatcat:fhx2y72a5fdadmj2yyqqjnj37i

Fuzzing: a survey

Jun Li, Bodong Zhao, Chao Zhang
2018 Cybersecurity  
Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques.  ...  In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery.  ...  Compared with other techniques, fuzzing requires few knowledge of targets and could be easily scaled up to large applications, and thus has become the most popular vulnerability discovery solution, especially  ... 
doi:10.1186/s42400-018-0002-y fatcat:3xvvipq7gfbkxl55h5desnqpiq

Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks [article]

Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
2021 arXiv   pre-print
We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks - functional algorithm classification and vulnerability discovery.  ...  We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task.  ...  Due to the large variability among vulnerabilities, increasingly large sizes of software and increasing costs of testing it, the problem of vulnerability discovery is not solved.  ... 
arXiv:2002.03388v2 fatcat:jgbkymwohrax3nkm5a2biqwub4

Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning [article]

Rock Stevens, Octavian Suciu, Andrew Ruef, Sanghyun Hong, Michael Hicks, Tudor Dumitraş
2017 arXiv   pre-print
As a result of our work, we responsibly disclosed five vulnerabilities, established three new CVE-IDs, and illuminated a common insecure practice across many machine learning systems.  ...  learning techniques.  ...  We use American Fuzzy Lop (AFL) [36] to instrument and fuzz-test machine learning programs.  ... 
arXiv:1701.04739v1 fatcat:nwkbcoh2ozhy7pmtmtxv4jctti

A systematic review of fuzzing based on machine learning techniques

Yan Wang, Peng Jia, Luping Liu, Cheng Huang, Zhonglin Liu, Tao Song
2020 PLoS ONE  
Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance.  ...  Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies five different stages in which machine learning has been used.  ...  Online classifiers allow models to scale well even for large numbers of crashes by incremental learning, while being easy to update for new crashes.  ... 
doi:10.1371/journal.pone.0237749 pmid:32810156 fatcat:j33n55wjg5hmvndnu2payv3zfy

A Review on Latest Technologies in Big Data Analysis

Castro S, Pushpalakshmi R
2018 International Journal of Engineering & Technology  
Some of the digital technologies such as cloud computing and Internet of Things (IoT) are considered as the major sources of such large data.  ...  In this digital world, the modern information systems have produced a large amount of data which needs huge depositary in terms of terabytes for storage.  ...  Towards this direction, several researches and surveys have been done by employing machine learning approaches with minimum storage requirements.  ... 
doi:10.14419/ijet.v7i3.1.16806 fatcat:fvxtqfgysrhtbiakgnvowceyhm

Towards Compliant Data Management Systems for Healthcare ML [article]

Goutham Ramakrishnan, Aditya Nori, Hannah Murfet, Pashmina Cameron
2020 arXiv   pre-print
Together, these represent first efforts towards building a compliant data management system for healthcare machine learning projects.  ...  We review how data flows within machine learning projects in healthcare from source to storage to use in training algorithms and beyond.  ...  However, it may not scale well to large data inputs and its usage-APIs are implemented only in python and R.  ... 
arXiv:2011.07555v1 fatcat:hkygwivw3ncavm3slf6bkv3e2u

Machine Knowledge: Information Studies and Artificial Intelligence in Dialogue

Julia Bullard, Muhammad Abdul-Mageed
2018 Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l'ACSI  
In this piece, we examine the parallels and contrasts between knowledge organization and data science with a focus on the subfields of AI and machine learning in particular.  ...  transparency.Dans cet article, nous examinons les parallèles et les contrastes entre l'organisation des connaissances et la science des données en mettant l'accent sur les sous-domaines de l'IA et de l'apprentissage machine  ...  Computational approaches that exploit large-sized datasets are becoming increasingly mainstream in many fields as they offer new possibilities for analysis and making discoveries.  ... 
doi:10.29173/cais971 fatcat:47mqo2ywbze7jgr7vyahkj7uou

Machine Learning-based Analysis of Program Binaries: A Comprehensive Study

Hongfa Xue, Shaowen Sun, Guru Venkataramani, Tian Lan
2019 IEEE Access  
To meet these challenges, machine learning-based binary code analysis frameworks attract substantial attention due to their automated feature extraction and drastically reduced efforts needed on large-scale  ...  INDEX TERMS Machine learning, program binary analysis, taxonomy.  ...  VULNERABILITY DISCOVERY Machine learning-based BCAs are also applied successfully in vulnerability discovery field.  ... 
doi:10.1109/access.2019.2917668 fatcat:fwjpykkdpjev7pzkhaoily4zci

Guest Editorial Deep Learning Models for Industry Informatics

Dharma Prakash Agrawal, Brij Bhooshan Gupta, Haoxiang Wang, Xiaojun Chang, Shingo Yamaguchi, Gregorio Martinez Perez
2018 IEEE Transactions on Industrial Informatics  
The fourteenth paper entitled, "Cross-Project Transfer Representation Learning for Vulnerable Function Discovery" authored by Lin et al. presents the cold-start problem of machine learning, by learning  ...  Many individuals have contributed toward the success of this issue.  ... 
doi:10.1109/tii.2018.2834547 fatcat:m453tboxz5d6tk3cpipqbknpfi

Intrusion Detection System for Large Scale Data using Machine Learning Algorithms

2019 International Journal of Engineering and Advanced Technology  
The result section will show how proposed system is better than classical machine learning algorithms.  ...  Due to various network attacks it is very hard to detect malicious activities from remote user as well as remote machines.  ...  Intrusion Detection System for Large Scale Data using Machine Learning Algorithms Accuracy and detection rate stated by this method are 85% and 81% respectively.  ... 
doi:10.35940/ijeat.f7971.088619 fatcat:dseiyikuxrgjdl3lg46pxopdr4

Malware: a future framework for device, network and service management

Radu State, Olivier Festor
2007 Journal in Computer Virology  
from them and that network management, which is the activity supposed to prevent them, can actually benefit from their use.  ...  We focus on five lessons learned from current malware that can benefit to the network management community.  ...  One solution towards the identification of such a user category is a large scale distributed overlay honeypot.  ... 
doi:10.1007/s11416-007-0037-1 fatcat:sut33x47hjeajcdz66xo4t7jjy

Machine Learning for Security and the Internet of Things: the Good, the Bad, and the Ugly

Fan Liang, William G. Hatcher, Weixian Liao, Weichao Gao, Wei Yu
2019 IEEE Access  
More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled  ...  In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT.  ...  Thus, as immanently needed research, the discovery of vulnerabilities in existing systems, the development of defensive approaches, and the discovery of vulnerabilities in defensive strategies are critical  ... 
doi:10.1109/access.2019.2948912 fatcat:wxd6imn62fgufdmfh3gtaijeru

Big Data Approach towards Networking, Data Transfer, IOT and Security: A Review

Anju Abraham
2019 Zenodo  
Big Data has emerged as an advanced discovery for large scale data collection. Data generated by large instruments that generates semi structured and unstructured information.  ...  Thus, in this Big data technology, particularly machine learning, has been widely used for intrusion/anomaly detection, little has been done in networking that is developed by software.  ...  Big data instruments and algorithms are widely used to disallow these attacks, especially supervised way of Journal of Web Development and Web Designing Volume 4 Issue 3 mechanisms, machine and deep learning  ... 
doi:10.5281/zenodo.3413933 fatcat:xmg6n4bjazamni6dzwglbjbr3i

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

Yongjun Lee, Hyun Kwon, Sang-Hoon Choi, Seung-Ho Lim, Sung Hoon Baek, Ki-Woong Park
2019 Applied Sciences  
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  ...  Grieco introduced a new approach in "Toward large-scale vulnerability discovery using machine learning" [16] in 2016.  ... 
doi:10.3390/app9194086 fatcat:dpmasa3dkrbzbicqtm3c2c2ptu
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