Cyber Attack Detection Method Based on NLP and Ensemble Learning Approach

2020 ◽  
Author(s):  
Maheli Ahmed ◽  
Mohammed Nasir Uddin
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Attack Detection ◽  
Learning Approach ◽  
Cyber Attack

scholarly journals OUTLIER DETECTION METHOD USE FOR THE NETWORK FLOW ANOMALY DETECTION / IŠSKIRČIŲ RADIMO METODŲ TAIKYMAS ANOMALIJOMS KOMPIUTERIŲ TINKLO PAKETŲ SRAUTUOSE APTIKTI

2016 ◽  
Vol 8 (3) ◽  
pp. 327-333 ◽  
Author(s):  
Rimas Ciplinskas ◽  
Nerijus Paulauskas
Keyword(s):  
Anomaly Detection ◽  
Network Flow ◽  
Detection Method ◽  
Attack Detection ◽  
Detection Methods ◽  
Cyber Attack ◽  
Normal Network ◽  
New Type ◽  
Flow Detection ◽  
F Measure

New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure. Kibernetinių atakų gausa ir įvairovė bei siekis nuo jų apsisaugoti verčia nuolat kurti naujus ir tobulinti jau esamus atakų aptikimo metodus. Kaip rodo praktika, dabartiniai atakų atpažinimo metodai iš esmės veikia pagal antivirusinių programų principą, t.y. sudaromi žinomų atakų šablonai, kuriais remiantis yra aptinkamos atakos, tačiau pagrindinis tokių metodų trūkumas – negalėjimas aptikti naujų, dar nežinomų atakų. Šiai problemai spręsti yra pasitelkiami anomalijų aptikimo metodai, kurie leidžia aptikti nukrypimus nuo normalios tinklo būsenos. Straipsnyje yra pateiktas naujas metodas, leidžiantis aptikti kompiuterių tinklo paketų srauto anomalijas taikant lokalių išskirčių faktorių algoritmą. Atliktas tyrimas leido surasti požymių grupes, kurias taikant anomalūs tinklo srautai yra atpažįstami geriausiai, t. y. pasiekiamos didžiausios tikslumo, atkuriamumo ir F-mato reikšmės.

RESILIENT FLOCKING CONTROL FOR CONNECTED AND AUTOMATED VEHICLES WITH CYBER-ATTACK THREATS

2021 ◽  
pp. 1-7
Author(s):  
Fengchen Wang ◽  
Yan Chen
Keyword(s):  
Detection Method ◽  
Tracking Error ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Vehicle Tracking ◽  
Cyber Attack ◽  
Dynamics Model ◽  
Automated Vehicles ◽  
Tracking Errors ◽  
Error Dynamics

Abstract To improve the cybersecurity of flocking control for connected and automated vehicles (CAVs), this paper proposes a novel resilient flocking control by specifically considering cyber-attack threats on vehicle tracking errors. Using the vehicle tracking error dynamics model, a dual extended Kalman filter (DEKF) is applied to detect cyber-attacks as an unknown constant on vehicle tracking information with noise rejections. To handle the coupling effects between tracking errors and cyber-attacks, the proposed DEKF consists of a tracking error filter and a cyber-attack filter, which are utilized to conduct the prediction and correction of tracking errors alternatively. Whenever an abnormal tracking error is detected, an observer-based resilient flocking control is enabled. Demonstrated by simulation results, the proposed cyber-attack detection method and resilient flocking control design can successfully achieve and maintain the flocking control of multi-CAV systems by rejecting certain cyber-attack threats.

scholarly journals A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Jia ◽  
Xiaohong Huang ◽  
Rujun Liu ◽  
Yan Ma
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Attack Detection ◽  
True Negative ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Accuracy And Precision ◽  
Ddos Attack Detection

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

Optimized cyber-attack detection method of power systems using sliding mode observer

2022 ◽  
Vol 205 ◽  
pp. 107745
Author(s):  
Mahdieh Adeli ◽  
Majid Hajatipour ◽  
Mohammad Javad Yazdanpanah ◽  
Hamed Hashemi-Dezaki ◽  
Mohsen Shafieirad
Keyword(s):  
Power Systems ◽  
Detection Method ◽  
Sliding Mode ◽  
Attack Detection ◽  
Sliding Mode Observer ◽  
Cyber Attack

scholarly journals A Novel Cyber Attack Detection Method in Networked Control Systems

2018 ◽  
Vol 48 (11) ◽  
pp. 3254-3264 ◽  
Author(s):  
Eman Mousavinejad ◽  
Fuwen Yang ◽  
Qing-Long Han ◽  
Ljubo Vlacic
Keyword(s):  
Control Systems ◽  
Detection Method ◽  
Networked Control Systems ◽  
Attack Detection ◽  
Networked Control ◽  
Cyber Attack

scholarly journals Optimization of network traffic anomaly detection using machine learning

2021 ◽  
Vol 11 (3) ◽  
pp. 2360
Author(s):  
ChoXuan Do ◽  
Nguyen Quang Dam ◽  
Nguyen Tung Lam
Keyword(s):  
Random Forest ◽  
Detection Method ◽  
Information Gain ◽  
Principal Component ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Abnormal Behavior ◽  
Cyber Attack ◽  
Detection Algorithms ◽  
Abnormal Behavior Detection

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.

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