Advanced services for critical infrastructures protection

Kozik, Rafał; Choraś, Michał; Flizikowski, Adam; Theocharidou, Marianthi; Rosato, Vittorio; Rome, Erich

doi:10.1007/s12652-015-0283-x

Cited by 22 publications

(8 citation statements)

References 2 publications

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“…The rising numbers of attacks aimed at citizens, societies and even seemingly secure systems built for critical infrastructures are easily observable [2,5]. The inadequacy of signaturebased systems in cyberattack detection is one of the primary causes of this situation.…”

Section: Cybersecurity: a Quick Primer On Challenges And Trendsmentioning

confidence: 99%

Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection

et al. 2019

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Network and information security are regarded as some of the most pressing problems of contemporary economy, affecting both individual citizens and entire societies, making them a highlight for homeland security. Innovative approaches to handle this challenge are undertaken by the scientific community, proposing the utilization of the emerging, advanced machine learning methods. This very paper puts forward a novel approach to the detection of cyberattacks taking inventory of the practical application of information granules. The feasibility of utilizing Granular Computing (GC) as a solution to the most current challenges in cybersecurity is researched. To the best of our knowledge, granular computing has not yet been widely examined or used for cybersecurity application purposes. The major contribution of this work is a method for constructing information granules from network data. We then report promising results on a benchmark dataset.

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Section: Cybersecurity: a Quick Primer On Challenges And Trendsmentioning

confidence: 99%

Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection

et al. 2019

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“…By introducing some modification to the algorithm, it is possible to monitor the behavior of the surrounding cells [7]. Other examples of cellular automata implementations include image processing, generation of textures, simulation of waves, wind, and people evacuation process as well as a simulation program, developed for the purpose of this study [19][20][21][22][23]. The aim of the proposed algorithm is to generate the simulations of patterns of human escape from the building on fire with a given number of exits and fire sources [24][25][26][27].…”

Section: Application Of Cellular Automatamentioning

confidence: 99%

A Cellular Automata-Based Simulation Tool for Real Fire Accident Prevention

Czerniak

Zarzycki

Apiecionek

et al. 2018

Mathematical Problems in Engineering

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Many serious real-life problems could be simulated using cellular automata theory. There were a lot of fires in public places which kill many people. Proposed method, called Cellular Automata Evaluation (CAEva in short), is using cellular automata theory and could be used for checking buildings conditions for fire accident. The tests performed on real accident showed that an appropriately configured program allows obtaining a realistic simulation of human evacuation. The authors analyze some real accidents and proved that CAEva method appears as a very promising solution, especially in the cases of building renovations or temporary unavailability of escape routes.

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“…A number of systems are dedicated to mitigating the cascading impact resulting from the failure of or an attack on critical infrastructure. Those systems, however, can be the subject of a cyber-attack [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

Dutta

Choraś

Pawlicki

et al. 2020

Sensors

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113

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Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection.

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Advanced services for critical infrastructures protection

Cited by 22 publications

References 2 publications

Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection

Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection

A Cellular Automata-Based Simulation Tool for Real Fire Accident Prevention

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

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