A deep learning framework for predicting cyber attacks rates

Fang, Xing; Xu, Maochao; Xu, Shouhuai; Zhao, Peng

doi:10.1186/s13635-019-0090-6

Cited by 52 publications

(20 citation statements)

References 38 publications

(53 reference statements)

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“…But still the most vulnerable piece in that cyber scheme is a man. Now it sounds like fantasy when it comes to predicting cyber attacks as it was a weather forecast (Fang et al, 2019). Situation in this country can't be called optimistic at all.…”

Section: Resultsmentioning

confidence: 99%

Methodology For Studying The Influence Of Social Engineering

Karpova¹

2021

European Proceedings of Social and Behavioural Sciences

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The author continues investigating the world problem of cybercrime and social risks as constant companions of that social phenomenon. In the circumstances of a new COVID world order the problem of cybercrime as a challenge to all mankind has only been intensified. Lately the author focused on the problem of understanding the nature of online risks known as risks of continuous online-communication. On the basis of theoretical analysis and empirical research it was systematized probable manifestations of risks taking into account their differential impact on some categories of the population. Now the author makes next step in understanding such a phenomenon like social engineering as a tool for committing a cybercrime. It is stated in the text the relevance of studying that social and psychological case and discussed the need for methodology of its analysis. It is discussed in the article the latest results of a survey conducted by the Russian Opinion Research Center (WCIOM) and proposed the classification of tools used by social engineers while committing such type of crime. According to many experts, the greatest threat to information security, both for large companies and ordinary users, in the next decades will be in roving methods of social engineering used to crack existing security tools. Our biology is inextricably linked with our social essence.

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Section: Resultsmentioning

confidence: 99%

Methodology For Studying The Influence Of Social Engineering

Karpova¹

2021

European Proceedings of Social and Behavioural Sciences

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“…This is important because, often, mathematical tools can only characterize asymptotic (e.g., equilibrium) behaviors. While important for sure, it is imperative to understand the transient behaviors, which often have to be dealt with using data-driven approaches, highlighting the importance of cybersecurity data analytics (e.g., Zhan et al (2013), Zhan et al (2015), Chen et al (2015), Xu et al (2017), Peng et al (2017), Xu et al (2018), Fang et al (2019), and Fang et al (2021).…”

Section: Future Researchmentioning

confidence: 99%

Seeking Foundations for the Science of Cyber Security

Yung

Wang

2021

Inf Syst Front

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Cyber security (or cybersecurity) has become a fundamental issue which deeply affects citizen's lives (including their privacy), the public's economic prosperity, and national security. The high frequency of media reports on highprofile cyber attacks, which cause substantial and, at times, catastrophic damages, highlights that cyberspace is a very fragile and vulnerable ecosystem, and that our understanding of cybersecurity and our capability of defending cyberspace are far from adequate. This is true despite the numerous advancements and breakthroughs in some fields of cybersecurity. One outstanding example is cryptography, which has been built on a firm foundation in Computational Complexity Theory, starting with a number of breakthroughs, e.g. Diffie and Hellman (1976), Rivest et al. (1978), Goldwasser and Micali (1982), and Yao (1982. However, there are many cyber attacks that go beyond the standard cryptographic threats models, such as attacks which can compromise cryptographic private keys by directly stealing memory pages (e.g., Harrison and Xu (2007)), or indirectly exploiting side-channel attacks (e.g., Kocher (1996)). The state-of-the-art is that we are far from being capable of adequately dealing with such attacks in

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“…After the experimental results, the proposed algorithm showed a better classification performance. The author in [16] deployed a bi-directional recurrent neural network for prediction of cyber-attack. Real world cyber-attack dataset was used to validate the proposed model.…”

Section: Literature Reviewmentioning

confidence: 99%

Cyber Situation Awareness Perception Model for Computer Network

Samuel¹

2021

IJACSA

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With the increase in cyber threats, computer network security has raised a lot of issues among various companies. In order to guide against all these threats, a formidable Intrusion Detection System (IDS) is needed. Various Machine Learning (ML) algorithms such as Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes, etc. has been used for threat detection. In light of the novel threats, there is a need to use a combination of tools to accurately enhance intrusion detection in computer networks, this is because intruders are gaining ground in the cyber world and the side effects on organizations cannot be quantified. The aim of this work is to provide an enhanced model for the detection of threats on the computer network. The combination of DT and ANN is proposed to accurately predict threats. With this model, a network administrator will be rest assured to some extent based on the prediction of the model. Two different supervised machine algorithms were hybridized in this research. NSL-KDD dataset was deployed for the simulation process in WEKA environment. The proposed model gave 0.984 precision, 0.982 sensitivity and 0.987 accuracy.

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A deep learning framework for predicting cyber attacks rates

Cited by 52 publications

References 38 publications

Methodology For Studying The Influence Of Social Engineering

Methodology For Studying The Influence Of Social Engineering

Seeking Foundations for the Science of Cyber Security

Cyber Situation Awareness Perception Model for Computer Network

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