Data-Driven Cybersecurity Incident Prediction: A Survey

Sun, Nan; Zhang, Jun; Rimba, Paul; Gao, Shang; Zhang, Leo Yu; Xiang, Yang

doi:10.1109/comst.2018.2885561

Cited by 264 publications

(136 citation statements)

References 63 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…Statistical methods have been widely used in the context of data-driven cyber security research, such as intrusion detection [15][16][17][18]. However, deep learning has not received the due amount of attention in the context of cyber security [13,14]. This is true despite the fact that deep learning has been tremendous successful in other application domains [19][20][21] and has started to be employed in the cyber security domains, including adversarial malware detection [22,23] and vulnerability detection [24,25].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, researchers have studied how to use a Bayesian method to predict the increase or decrease of cyber attacks [6], how to use a hidden Markov model to predict the increase or decrease of Bot agents [7], how to use a seasonal ARIMA *Correspondence: xfang13@ilstu.edu 1 School of Information Technology, Illinois State University, Normal 61761, IL, USA Full list of author information is available at the end of the article model to predict cyber attacks [8], how to use a FARIMA model to predict cyber attack rates when the time series data exhibits long-range dependence [1], how to use a FARIMA+GARCH model to achieve even more accurate predictions by further accommodating the extreme values exhibited by the time series data [9], how to use a marked point process to model extreme cyber attack rates while considering both magnitudes and inter-arrival times of time series [10], how to use a vine copula model to quantify the effectiveness of cyber defense early-warning mechanisms [11], and how to use a vine copula model to predict multivariate time series of cybersecurity attacks while accommodating the high-dimensional dependence between the time series [12]. We refer to two recent surveys on the use of statistical methods in cyber incident and attack detection and prediction [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning framework for predicting cyber attacks rates

Fang

et al. 2019

EURASIP J. on Info. Security

View full text Add to dashboard Cite

Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized in the literature, in this paper we develop a deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM. Empirical study shows that BRNN-LSTM achieves a significantly higher prediction accuracy when compared with the statistical approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning framework for predicting cyber attacks rates

Fang

et al. 2019

EURASIP J. on Info. Security

View full text Add to dashboard Cite

show abstract

“…[30][31][32] In this category, there are also many studies trying to solve specific problems like "Twitter Spam Drift" [33][34][35] for data-driven cybersecurity prediction tasks. 36 On the other hand, content-based approaches make use of the properties of a posted tweet that includes the count of mentions, count of hashtags, links, trending topics, and duplicate tweets. [37][38][39] Hybrid methods uses the features of both user-based and content-based methods.…”

Section: Related Workmentioning

confidence: 99%

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Kılınç

2019

Softw Pract Exp

View full text Add to dashboard Cite

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

show abstract

“…Cybersecurity has become a very hot topic in recent years. Many communities, groups, and governments start to realize the importance and urgency to deal with the ever‐changing cyberattacks . Experts in the industry and scholars in the academia strive to innovate the next‐generation solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Experts in the industry and scholars in the academia strive to innovate the next‐generation solutions. Among the technical solutions, machine learning–based methods receive an increasingly popular favor due to its superior efficiency comparing with manual analysis . In addition, the instantaneous protection brought by the machine learning–based solutions surpasses most reactive technologies including the automated protection systems in terms of reaction time.…”

Section: Introductionmentioning

confidence: 99%