Metaheuristic Techniques in Attack and Defense Strategies for Cybersecurity: A Systematic Review

2021 ◽  
pp. 449-467
Author(s):  
Agustín Salas-Fernández ◽  
Broderick Crawford ◽  
Ricardo Soto ◽  
Sanjay Misra
Keyword(s):  
Systematic Review ◽  
Defense Strategies ◽  
Metaheuristic Techniques ◽  
Attack And Defense

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

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.

scholarly journals Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 168994-169009
Author(s):  
Hamid Al-Hamadi ◽  
Ing-Ray Chen ◽  
Ding-Chau Wang ◽  
Meshal Almashan
Keyword(s):  
Intrusion Detection ◽  
Defense Strategies ◽  
Attack And Defense

scholarly journals Stability and Topology of Scale-Free Networks under Attack and Defense Strategies

2005 ◽  
Vol 94 (18) ◽  
Author(s):  
Lazaros K. Gallos ◽  
Reuven Cohen ◽  
Panos Argyrakis ◽  
Armin Bunde ◽  
Shlomo Havlin
Keyword(s):  
Defense Strategies ◽  
Scale Free ◽  
And Topology ◽  
Scale Free Networks ◽  
Attack And Defense

scholarly journals Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security

2020 ◽  
Vol 3 ◽  
Author(s):  
Adnan Qayyum ◽  
Aneeqa Ijaz ◽  
Muhammad Usama ◽  
Waleed Iqbal ◽  
Junaid Qadir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Graphics Processing Units ◽  
High Performance ◽  
Cloud Services ◽  
Third Party ◽  
Systematic Evaluation ◽  
Research Issues ◽  
Wide Range ◽  
Attack And Defense

With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions—attacks and defenses—related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation.

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