GOF-SLFN- An Intelligent Attack Detection System against Denial of Service (DoS) attacks based on Glow Worm Swarm optimized Single Layer Feed Forward Networks for vehicular Cyber Physical Systems (VCPS)

Robust Self-Triggered Control for Nonlinear Cyber-Physical Systems with State Constraints under DoS Attacks

10.21203/rs.3.rs-1098276/v1 ◽

2021 ◽

Author(s):

Zhaoyang Cuan ◽

Dawei Ding ◽

Heng Wang

Keyword(s):

Control Strategy ◽

State Constraints ◽

Denial Of Service ◽

Cyber Physical Systems ◽

Dos Attacks ◽

System Safety ◽

Physical Systems ◽

Security Control ◽

Event Based ◽

Safety And Stability

Abstract This paper is concerned with the event-based control problem for nonlinear cyber-physical systems (CPSs) with state constraints. A novel security control strategy consisting of a self-triggered mechanism is developed to decrease the network communication loads to the most extent on the basis of ensuring system safety and stability. The maximum capability of the designed self-triggered mechanism to resist denial-of-service (DoS) attacks occurring in controller-actuator (C-A) and sensor-controller (S-C) channels synchronously is also analyzed. In particular, we prove that the security control strategy guarantees the system safety and stability without resulting in Zeno behavior. Finally, a numerical example is provided to demonstrate the prominent effectiveness and the advantages over the existing results.

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Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

Security and Communication Networks ◽

10.1155/2019/1574749 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 6

Author(s):

Francisco Sales de Lima Filho ◽

Frederico A. F. Silveira ◽

Agostinho de Medeiros Brito Junior ◽

Genoveva Vargas-Solar ◽

Luiz F. Silveira

Keyword(s):

Machine Learning ◽

Network Traffic ◽

Service Providers ◽

Detection System ◽

Denial Of Service ◽

Sampling Rate ◽

Attack Detection ◽

Dos Attacks ◽

Internet Service ◽

Benchmark Datasets

Users and Internet service providers (ISPs) are constantly affected by denial-of-service (DoS) attacks. This cyber threat continues to grow even with the development of new protection technologies. Developing mechanisms to detect this threat is a current challenge in network security. This article presents a machine learning- (ML-) based DoS detection system. The proposed approach makes inferences based on signatures previously extracted from samples of network traffic. The experiments were performed using four modern benchmark datasets. The results show an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate (SR) of 20% of network traffic.

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Generative Adversarial Networks Based Cognitive Feedback Analytics System for Integrated Cyber-Physical System and Industrial Iot Networks

10.21203/rs.3.rs-924288/v1 ◽

2021 ◽

Author(s):

Kamal Upreti ◽

Mohammad Haider Syed ◽

Mohammad Shabbir Alam ◽

Adi Alhudhaif ◽

Mohammed Shuaib ◽

...

Keyword(s):

Denial Of Service ◽

Cyber Physical Systems ◽

Generative Adversarial Networks ◽

Dos Attacks ◽

Physical Systems ◽

Adversarial Networks ◽

Industrial Iot ◽

Cognitive Feedback ◽

Feedback Data ◽

Response Feedback

Abstract In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (S-C) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies.

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Deep Reinforcement Learning for Building Honeypots against Runtime DOS Attack

10.21203/rs.3.rs-207770/v1 ◽

2021 ◽

Author(s):

Selvakumar Veluchamy ◽

RubaSoundar Kathavarayan

Keyword(s):

Reinforcement Learning ◽

Detection System ◽

Denial Of Service ◽

Attack Detection ◽

Dos Attack ◽

Dos Attacks ◽

Server Side ◽

Intelligent Decision Making ◽

Deep Recurrent Neural Network ◽

And Performance

Abstract Honeypot is a network environment used to protect the legitimate network resources from attacks. Honeypot creates an environment that impresses attackers to inject their activities to steal resources. This is a way to detect the attacks by doing attack detection procedures. In this work, Denial of Service (DoS) attacks are effectively detected by proposed honeypot system. Machine Learning (ML) and Deep Learning (DL) methods evolve in many areas to build intelligent decision making systems. This work uses DL approaches and secures event validation procedures for finding predicting DoS attacks. The proposed system called Deep Adaptive Reinforcement Learning for Honeypots (DARLH) is implemented to monitor internal and external DoS attacks. In the honeypot environment, the proposed DARLH system implements DARL based IDS (Intrusion Detection System) agents and Deep Recurrent Neural Network (DRNN) based IDS agents for monitoring multiple runtime DoS attacks. These techniques support for dynamic IDS against DoS attack. In addition, the DARLH creates protected poison distribution and server side supervision system for keeping the monitoring events legitimate. This work is implemented and performance is evaluated. The results are compared with existing systems like GNBH, BCH and RNSG. In this comparison, the proposed system provides 5–10% better results than other systems.

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