scholarly journals Analysis of Network Attack and Defense Strategies Based on Pareto Optimum

Electronics ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 36 ◽  
Author(s):  
Yang Sun ◽  
Wei Xiong ◽  
Zhonghua Yao ◽  
Krishna Moniz ◽  
Ahmed Zahir
Keyword(s):  
Pareto Optimum ◽  
Network Attack ◽  
Defense Strategies ◽  
Attack And Defense

scholarly journals Defending Application Layer DDoS Attacks via Multidimensional Parallelotope

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaolin Zhao ◽  
Hui Peng ◽  
Xiang Li ◽  
Yue Li ◽  
Jingfeng Xue ◽  
...  
Keyword(s):  
Specific Property ◽  
Complex Form ◽  
Risk Level ◽  
Network System ◽  
Application Layer ◽  
Network Attack ◽  
Defense Strategies ◽  
Ddos Attack ◽  
Attack And Defense ◽  
Network Risk

The Internet is more and more integrated into people’s life; because of the complexity and fragility of the network environment, network attack presents a more and more serious trend. Application Layer DDoS (AL-DDoS) attack is the most complex form of DDoS attack, which is hindering the availability for the legitimate users by taking up a large number of requests of web server. The paper introduced the concept of behavior utility to portray the network. The concept of attack and defense utility was defined by a specific property which was the manifestation of the network risk after the offset of attack and defense. In the utility model, traffic metrics were mapped to the multidimensional parallelotope in the Euclidean space to express as a diagonal matrix. To determine the threshold status, the defense strategies of load balancing and limiting the maximum number of connections were used with different attack scales. Finally, the attack and defense utility value was calculated to evaluate the network risk level. The proposed method can master the capacity of network system against each attack means and the defense capability of network system. Its availability and accuracy are verified by comparing with the relevant works.

scholarly journals Active Defense Strategy Selection Method Based on Two-Way Signaling Game

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaohu Liu ◽  
Hengwei Zhang ◽  
Yuchen Zhang ◽  
Lulu Shao ◽  
Jihong Han
Keyword(s):  
Signal Transmission ◽  
Signaling Game ◽  
Strategy Selection ◽  
Network Attack ◽  
Defense Strategies ◽  
Defense Strategy ◽  
Active Defense ◽  
Bayesian Equilibrium ◽  
Security Research ◽  
Attack And Defense

Most network security research studies based on signaling games assume that either the attacker or the defender is the sender of the signal and the other party is the receiver of the signal. The attack and defense process is commonly modeled and analyzed from the perspective of one-way signal transmission. Aiming at the reality of two-way signal transmission in network attack and defense confrontation, we propose a method of active defense strategy selection based on a two-way signaling game. In this paper, a two-way signaling game model is constructed to analyze the network attack and defense processes. Based on the solution of a perfect Bayesian equilibrium, a defense strategy selection algorithm is presented. The feasibility and effectiveness of the method are verified using examples from real-world applications. In addition, the mechanism of the deception signal is analyzed, and conclusions for guiding the selection of active defense strategies are provided.

Tracing the Ghosts in Our Ethical Shells

2020 ◽  
Author(s):  
Tam ngoc Nguyen
Keyword(s):  
Language Processing ◽  
Behavioral Science ◽  
Model Development ◽  
Continuous Model ◽  
Research Approach ◽  
Information Warfare ◽  
Scientific Model ◽  
Defense Strategies ◽  
Ethical Behaviors ◽  
Attack And Defense

We proposes a new scientific model that enables the ability to collect evidence, and explain the motivations behind people's cyber malicious/ethical behaviors. Existing models mainly focus on detecting already-committed actions and associated response strategies, which is not proactive. That is the reason why little has been done in order to prevent malicious behaviors early, despite the fact that issues like insider threats cost corporations billions of dollars annually, and its time to detection often lasts for more than a year.We address those problems by our main contributions of:+ A better model for ethical/malicious behavioral analysis with a strong focus on understanding people's motivations. + Research results regarding ethical behaviors of more than 200 participants, during the historic Covid-19 pandemic. + Novel attack and defense strategies based on validated model and survey results. + Strategies for continuous model development and integration, utilizing latest technologies such as natural language processing, and machine learning. We employed mixed-mode research approach of: integrating and combining proven behavioral science models, case studying of hackers, survey research, quantitative analysis, and qualitative analysis. For practical deployments, corporations may utilize our model in: improving HR processes and research, prioritizing plans based on the model's informed human behavioral metrics, better analysis in existing or potential cyber insider threat cases, generating more defense tactics in information warfare and so on.

Anatomy of SIP registration removal attack and defense strategies

2014 ◽  
Author(s):  
Ismail Melih Tas ◽  
Onur Ozbirecikli ◽  
Ugur Cagai ◽  
Erhan Taskin ◽  
Huseyin Tas
Keyword(s):  
Defense Strategies ◽  
Attack And Defense

Generation Method of Power Network Security Defense Strategy Based on Markov Decision Process

2021 ◽  
Author(s):  
Wang Yang ◽  
Liu Dong ◽  
Wang Dong ◽  
Xu Chun
Keyword(s):  
Network Security ◽  
Response Time ◽  
Markov Decision Process ◽  
Decision Process ◽  
Evolutionary Game ◽  
Power Network ◽  
Network Attack ◽  
Defense Strategy ◽  
Markov Decision ◽  
Attack And Defense

Aiming at the problem that the current generation method of power network security defense strategy ignores the dependency relationship between nodes, resulting in closed-loop attack graph, which makes the defense strategy not generate attack path, resulting in poor defense effect and long generation response time of power network security defense strategy, a generation method of power network security defense strategy based on Markov decision process is proposed. Based on the generation of network attack and defense diagram, the paper describes the state change of attack network by using Markov decision-making process correlation principle, introduces discount factor, calculates the income value of attack and defense game process, constructs the evolutionary game model of attack and defense, solves the objective function according to the dynamic programming theory, obtains the optimal strategy set and outputs the final results, and generates the power network security defense strategy. The experimental results show that the proposed method has good defense effect and can effectively shorten the generation response time of power network security defense strategy.

scholarly journals Design Method for Virtual Network Attack and Defense Platform

2012 ◽  
Vol 3 ◽  
pp. 335-340
Author(s):  
Wang Fangnian ◽  
Peng Gang ◽  
Che Wanfang ◽  
Niu Cong ◽  
Bai Yun ◽  
...  
Keyword(s):  
Design Method ◽  
Virtual Network ◽  
Network Attack ◽  
Attack And Defense

scholarly journals Cycle-Consistent Adversarial GAN: The Integration of Adversarial Attack and Defense

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Ruoxi Qin ◽  
Linyuan Wang ◽  
Wanting Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Small Magnitude ◽  
Defense Strategies ◽  
Adversarial Examples ◽  
Adversarial Attack ◽  
Public Datasets ◽  
Attack And Defense

In image classification of deep learning, adversarial examples where input is intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them. Different attack and defense strategies have been proposed to better research the mechanism of deep learning. However, those researches in these networks are only for one aspect, either an attack or a defense. There is in the improvement of offensive and defensive performance, and it is difficult to promote each other in the same framework. In this paper, we propose Cycle-Consistent Adversarial GAN (CycleAdvGAN) to generate adversarial examples, which can learn and approximate the distribution of the original instances and adversarial examples, especially promoting attackers and defenders to confront each other and improve their ability. For CycleAdvGAN, once the GeneratorA and D are trained, GA can generate adversarial perturbations efficiently for any instance, improving the performance of the existing attack methods, and GD can generate recovery adversarial examples to clean instances, defending against existing attack methods. We apply CycleAdvGAN under semiwhite-box and black-box settings on two public datasets MNIST and CIFAR10. Using the extensive experiments, we show that our method has achieved the state-of-the-art adversarial attack method and also has efficiently improved the defense ability, which made the integration of adversarial attack and defense come true. In addition, it has improved the attack effect only trained on the adversarial dataset generated by any kind of adversarial attack.

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