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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.  ...  With the development and widely use of machine learning techniques, some research try to use machine learning techniques to assist the generation of testcases.  ... 
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.  ...  In recent years we have seen a big surge in applications of machine learning (ML) to the field of security, where researchers routinely turn to ML algorithms for smarter automated solutions.  ... 
arXiv:2002.03388v2 fatcat:jgbkymwohrax3nkm5a2biqwub4

Internet of Things: Evolution, Concerns and Security Challenges

Parushi Malhotra, Yashwant Singh, Pooja Anand, Deep Kumar Bangotra, Pradeep Kumar Singh, Wei-Chiang Hong
2021 Sensors  
The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS).  ...  The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection  ...  in the smart digitized world limits the automated detection and discovery of the vulnerabilities.  ... 
doi:10.3390/s21051809 pmid:33807724 fatcat:5qznjvr665fjpmh7vy5jzp6chy

Network Infrastructure Vulnerabilities and Its Mitigation

Debalina Basu
2019 International Journal for Research in Applied Science and Engineering Technology  
It is smarter to discover these vulnerabilities ahead of time before assailant do. The intensity of Vulnerability appraisal is generally thought little of.  ...  This prompts an ever increasing number of vulnerabilities in Systems. Assailants utilize these vulnerabilities to abuse the injured individual's framework.  ...  The routing data will likewise provide some insight with respect to whether the specific target is ensured by firewall. b) Target Discovery: It is the way toward finding machines on the target network.  ... 
doi:10.22214/ijraset.2019.5314 fatcat:lkfrycupwnbx7kvb73xhcarp2e

Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

Abdellah Chehri, Issouf Fofana, Xiaomin Yang
2021 Sustainability  
This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence.  ...  Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks.  ...  On the other hand, machine learning (ML) helps to recognize patterns in data so that machines can learn from experience [49] .  ... 
doi:10.3390/su13063196 fatcat:oacg77qgyreczgy4altsgnllha

The Need for Holistic Network Design

Alberto Leon-Garcia, Martha Steenstrup
2021 IEEE Communications Magazine  
The emerging multitier network will play a central role in the creation of intelligent applications that leverage AI and machine learning to provide continuous situational awareness, event detection and  ...  The evolution of the network toward an application platform has the potential to address some of these requirements.  ...  The emerging multitier network will play a central role in the creation of intelligent applications that leverage AI and machine learning to provide continuous situational awareness, event detection and  ... 
doi:10.1109/mcom.2021.9530485 fatcat:2alxqckc5zcmzmbxbpgp5llnhu

NeuFuzz: Efficient Fuzzing with Deep Neural Network

Yunchao Wang, Zehui Wu, Qiang Wei, Qingxian Wang
2019 IEEE Access  
In particular, the deep neural network is used to learn the hidden vulnerability pattern from a large number of vulnerable and clean program paths to train a prediction model to classify whether paths  ...  In this paper, we present a solution, NeuFuzz, using the deep neural network to guide intelligent seed selection during graybox fuzzing to alleviate the aforementioned limitation.  ...  trains them using traditional machine learning algorithms.  ... 
doi:10.1109/access.2019.2903291 fatcat:cmbyaxwohba6zm746p5xfxmdzy

Ontological and Machine Learning Approaches for Managing Driving Context in Intelligent Transportation

Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif
2017 Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
A smart vehicle is equipped with machine learning functionalities that are capable of classifying any driving situation, and accord assistance to the driver or the vehicle or both to avoid accident, when  ...  To manage this rich information, knowledge representation using ontology is used and through it, such information becomes a source of knowledge.  ...  Machine Learning Machine Learning algorithms (Mitchell 1997 ) use data to discover pattern and can be used to predict an output from a formatted input after training the algorithm on a sufficiently big  ... 
doi:10.5220/0006580803020309 dblp:conf/ic3k/HinaTSR17 fatcat:nr3oaruyxfgdbj2m3v6fhubmlm

Smart Health and Cybersecurity in the Era of Artificial Intelligence [chapter]

A.K.M. Jahangir Alam Majumder, Charles B. Veilleux
2021 Computer-Mediated Communication [Working Title]  
AI could be used as an objective method in recognizing mental health crisis.  ...  Research into Smart Health using Artificial Intelligence (AI) could help identify the mental health of individuals by analyzing physiological data.  ...  AI and machine learning make smarter cybersecurity possible and these emerging technologies have vast potential applications in healthcare, finance, retail, etc.  ... 
doi:10.5772/intechopen.97196 fatcat:yn4wipshwjfgfgiud3yp6k64py

STAKEHOLDER INPUT TO RESEARCH AGENDA

Paul Malone, James Clarke, Sidhant Hasija, Ehsan Elahi, Sara Pittonet Gaiarin, Stephanie Parker
2019 Zenodo  
The advanced search mechanism allows users to search for knowledge using these elements as filters. A summary search result for each of the identified human values is presented in this document.  ...  No surprise that most security breaches take place using known vulnerabilities for which patches exist. The hackers can keep on using the same resources against many potential victims.  ...  The next generation internet will have to empower users, including the most vulnerable or challenged, to have access to the same digital learning opportunities, in forms that are accessible, perceivable  ... 
doi:10.5281/zenodo.3364010 fatcat:3u3guqxjzbgc7aw5g4hf324hj4

SGPFuzzer: A State-Driven Smart Graybox Protocol Fuzzer for Network Protocol Implementations

Yingchao Yu, Zuoning Chen, Shuitao Gan, Xiaofeng Wang
2020 IEEE Access  
As one of the most widely used technologies in software testing, fuzzing technology has been applied to network protocol vulnerability detection, and various network protocol fuzzers have been proposed  ...  Finally, we evaluate SGPFuzzer on two widely used protocol implementations (LightFTP and tinyDTLS).The results show that SGPFuzzer outperforms Boofuzz and AFL in path coverage, unique crashes and the first  ...  In terms of state inference and state machine learning, Pulsar [10] and Autofuzz [3] learn a protocol state machine from a corpus of message sequences, and TLS-fuzzer [11] leverages a related algorithm  ... 
doi:10.1109/access.2020.3025037 fatcat:77anseyuv5dthc46aytphitqtu

Smarter City: Smart Energy Grid based on Blockchain Technology

Alessandra Pieroni, Noemi Scarpato, Luca Di Nunzio, Francesca Fallucchi, Mario Raso
2018 International Journal on Advanced Science, Engineering and Information Technology  
It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources  ...  and private citizens), using the Blockchain granting ledger.  ...  Furthermore, the machines can be used to develop the proofof-work for a mining process.  ... 
doi:10.18517/ijaseit.8.1.4954 fatcat:kde5fsiy5zfqro2mvmgkc42g6i

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Tan Yigitcanlar, Kevin C. Desouza, Luke Butler, Farnoosh Roozkhosh
2020 Energies  
Interest in the use of AI for urban innovation continues to grow.  ...  Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.  ...  Describes the use of machine learning in improving fall detection devices.  ... 
doi:10.3390/en13061473 fatcat:ynlbnwsqjvcqnpuvmhwns7bgwu

The Human Digitalisation Journey: Technology First at the Expense of Humans?

Hossein Hassani, Xu Huang, Emmanuel Silva
2021 Information  
review and discussion, aims to investigate the journey of human digitalisation, explore the reality of domination between technology and humans, provide a better understanding of the human value and human vulnerability  ...  to machine communications are possible to provide smarter services.  ...  This will protect humans from vulnerabilities and contribute towards a sustainable cycle of advancements that can establish and create symbiosis between AI and humans [58] such that they share similar  ... 
doi:10.3390/info12070267 fatcat:rlocs2tsvbgj7movlurxmazdha

Necessity of data science for enhanced Cybersecurity

Shiv Hari Tewari
2021 International Journal of Data Science and Big Data Analytics  
To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science.  ...  After that I have described the various upcoming challenges that can emerge after the frequent applications of CSDS, how machine learning and deep learning are applicable in it and types of algorithms  ...  Manjeet and Raymond (2018) discussed in their paper how the machine learning can be used to create an impenetrable cyber defense system by using the methods of machine learning like supervise, unsupervised  ... 
doi:10.51483/ijdsbda.1.1.2021.63-79 fatcat:ycciwntsd5aodjp4tjzmhn4odu
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