By Robinson E. Pino, Alexander Kott, Michael Shevenell
This e-book explores cybersecurity examine and improvement efforts, together with principles that care for the becoming problem of the way computing engineering can merge with neuroscience. The contributing authors, who're popular leaders during this box, completely research new applied sciences that may automate safety techniques and practice self reliant services with determination making services. to maximise reader perception into the variety of professions facing elevated cybersecurity concerns, this ebook offers paintings played through executive, undefined, and educational learn associations operating on the frontier of cybersecurity and community sciences. Cybersecurity platforms for Human Cognition Augmentation is designed as a reference for practitioners or executive staff operating in cybersecurity. Advanced-level scholars or researchers occupied with laptop engineering or neuroscience also will locate this e-book an invaluable resource.
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Extra resources for Cybersecurity Systems for Human Cognition Augmentation
75-92. 30. M. 2013c. Towards a theory of understanding and mental modeling. Recent. Advances. Comp. , 250-255. M. 2003. Transforming data into actionable knowledge in network centric warfare. Battlefield Technology, 6, 1, 1-10. 32. B. 2002. Swiss army knife and Ockham’s razor: Modeling and facilitating operator’s comprehension in complex dynamic tasks. IEEE Systems, Man Cybernetics, 32, 2, 185-199. 1 Introduction The growth of digital content and information through the World Wide Web is increasing rapidly and more of this traffic is generated by smart mobile low size, weight, and power (SWaP) devices that are constantly sending/receiving information to/from the network for up-to-date operation.
From this Figure, we can observe that if only two packets are used for ALIEN to train the neural network, the classification performance is 100 %. It is interesting to note that if only one packet is used, the true-negative, TN, performance 2 Neuromorphic Computing for Cognitive Augmentation in Cyber Defense 41 is 100 % while the true-positive, TP, performance is about 50 %. What this specific result highlights is the fact that only one packet of the MX or A type was used during the training. Therefore, the system learned to identify DNS A packet types and guessed with 50 % accuracy on the DNS MX packet type.
Minimization of the expected total damage F(‖λ‖). For example, an invention (called Importance Map, ) displays the relative impact of different malware features on the classification decision made by the neural net. 7 as “image of man” but count x3 as a part of the image as opposed to a part of the surrounds). 3 Support Attributions of Interaction Strength Subjective strength of interaction between two assets reflects their perceived importance to and the degree of collaboration in the performance of the mission.