Convolutional Neural Networks (CNN) — Machine Learning for Encryption & Countermeasures

Joe Alongi
8 min readDec 2, 2017


Through machine intelligence, we have ventured upon applications of a plethora of frameworks to solve for imagery, motion picture, sound, and natural language. We have started to outline how they can create high-level interpreted data as independent frameworks, and working collaboratively to process additive layers for accuracy.

Among the understanding of these data sets and the attributions of how increasing applications of data are developed in the connected world, we can begin to parlay the understanding of how they can connect to work to identify each other. As we look at the abilities of convolution to interpret data, with LTSMs to remember/gate/filter them, we can understand how generative networks can create outputs through the filtered data to create instruments in which have a scaling presence.

The understanding of these factors has driven the pursuit in cryptology in the recent past to work towards security applications such as blockchain. The matters of encrypting and passing the data through secure layers is a managed proponent in meeting the developing matters at hand with a sense of viable measure. In this chapter of our progress, we start to understand the contrasting capabilities of these networks the solve against each other, in the means of deciphering, understanding and valuing threats in the networks while applying these technologies.

We mentioned blockchain in the spirit of contributory development across a community of users, its foundation in decentralization to invigorate record processing. The sense of connecting data is similar to how these algorithms and frameworks will be developed collectively as both of the technologies shift and develop moving forward. In a very connective means, these communities have grown out of unification, the process in connecting the blockchain, through means of records, is similar to how neurons unite to create a network in a respective sense.

Decentralized Data Structures in Machine Intelligent Blockchains

If you have ever lost a file, deleted a document, or found missing links in a project because of the aforementioned, you have experienced something of the nature of centralized data, one source/input/storage, all of these in which may have affected you experientially. This is an example of where blockchains and machine intelligence begins, creating multiple valid functions, collaborating with shared data, and factoring them together for key outputs, from the structured inputs. A blockchain is a generative structure, in which collaborates across an array of individuals to write valued data together, the unified output, a link, in the extending chain. As a neuron is trained to learn, the vector is set, and then factors inputs in the next, the blockchain does so for security.

The efforts to initially solve this came from the cloud, the idea of grasping data and sucking it up into a stored place. These functions helped precipitate an order of which we could all store collective data for applications, programs, and keeping — extending the means in which we can align our devices, without filling them completely to the brim… leave room. Initially, this was a major leap, as it not only solved some of the hardware form factor concerns but also provided another means of uniting the user with an extended range of connectivity and experiences. The challenges with these networks are that they essentially become/became/are landfills for data, in a way, such as the intent, outside that of the computing measure. What we see often is the risk of having all of this stored data in a central location, from saying consumer devices like phones, to business operations on networks. Having everything in one place is certainly helpful, organized, and unified, but it also leaves a lot of room for vulnerability both internally and externally.

We begin to understand a need for this newly organized fashion, much as packets are divided and reconnected to serve the information, these technologies both divide and conquer in a similar sense. In blockchains, people across the chain become those that are collaboratively responsible for the definitions inside of the records, in this case, the cloud is divided, not split, united, but divided, through the means of cryptology, connecting the network through stored and generated blocks in which each contain a set hash that unites them. These records contain elements in each that establish continuity as each user holds a similar record in their chain, and can be referenced together to contrast and provide insight on what changed, when, and identified uniquely. The example is often written in the cryptocurrency sense, in which when we count or exchange these currencies, the records, need to clearly defend and visible to everyone, in a sense the trust and perception of ICOs is the value derivative unit, hence the developed form of connecting.

When we look at neural networks and combining their individual use for processing data inputs we consider similar elements. In creating the layers for analysis, let us say, natural language, for example, we look to create vectors for each fragment of speech to uniquely identify patterns across a longer form. This is an interesting way of considering it for understanding a top-down view of how the individual data components are processed through trained layers and then connected for a means of machine intelligent computation/understanding/generative iteration.

In a similar sense that blockchain stratifies the ability for users to edit records in a malicious fashion, so as well, the machine intelligence components of neural networks, strengthen our impact of computation and awareness in an inverse awareness manner. Forming individual ‘records’ in the machine intelligent sense, for sake of syncing terminology, we can guide machines to develop around having an acute awareness for dialects, reactionary predetermination, and advance generative techniques in imagery and motion, along with language from learned examples. These awarenesses are important, as the developing wares of attacks are impending machine networks, layered with machine intelligence. Much as we have seen botnet DDOS attacks before, similarly and subtly, the intelligence of machines is accomplishing the means through that of one with human-like generation. It is important in this sense to understand how these contributions align so that we can deconstruct the patterns and algorithms in which contribute to them, developing our own awareness toolsets for refactoring security.

Systematic Complexities of Neural Network Convolution

From the understanding that these approaches are tested and templatized for scaling throughout networks or traded across the web, identifying and detecting these iterations/implementations/interruptions early on is one of the focuses of refining machine learning for this approach.

In this example, we begin to understand how side-channel attacks consider some of the elements we have mentioned. The perceptive training steps of refining the weights in the vectors and understanding their distribution. In a similar sense that blockchain stratifies the ability of the network in a sense, that limits tampering, applying data to convolutional neural networks strings along with a similar theory, generate patterns, around the data, make filtering a necessity for decoding and align data in an array of structured phases/layers. The cryptography measures apply in the generations of the factors in which weigh the outputs of generation.

DCGNN or Deep Convolutional Generative Neural Network is an idea in which factors these patterns repetitively and in itself, creates a blockchain around the intended data. In these senses, structuring, replication, and scaling of data in neural networks add layers of security to the things we are trying to hide — a needle in a haystack. The relation of both of these layered contexts is that in which we find to understand how adding padding to the sensitive data, distributing responsibility, and improving security measures, through layering is beneficial to the countermeasure, and the generative principles of both of the structures. What is outlined well in this context is the methods in which the CNN is trained through a scope of profiling to configure its gradients to match that of the predicted leak models. The ability for trained neurons to inference set parameters and define these flaws outweighs that of most in the sense of effectivity and speed.

This example of ANN’s, something we previously mentioned, how these networks have individual inputs in which backpropagation models are then factored and processed through the hidden layers to derive an output or input in a generative sense. The individual neurons in this example, connect to sort data, hash types, algorithms, and layers together and generate encrypted certificates. Combining these neurons allows each input to focus on processing one individual component, much as the blockchain records are constructed to capture the key attributes of information within encrypted meters. The hidden layers, in this case, are used to connect the initiators and dynamic/chaotic measures to apply these references as one structured output.

Structured Response Matrices From Generative Machine Models

Through tuning these models and developing the matrixes in which unite them we learn to devise unique methodologies to compose the iterations of solutions we see today. As we understand how these networks compile and process data through different measures we can acutely align them with their solution, outside of the algorithmic inputs, there are nuances to each kind and the combinations in a way that can produce and solve for extensive means.

In understanding for example how convolutional networks and LTSM recurrent networks can work to sort out social biases across NLP and generate outputs in which can be charted can provide overall insight over how our culture as a macro unit is feeling. Outside of the behavioral processing, which is quite curious alone, we can build these networks to identify patterns inversely, for example, the recent DDoS attack based on bot attributions of NLP, could have been filtered with such examples, and identified as each of the datasets have unique patterns of what was mentioned in the first example of leaky points in which become the break in a sense of the overall cipher, as it is in the dataset. If this is approached, as understanding the more research and inputs we have to train models, the more prepared we can be for understanding their means of attacking, small, or at scale, as these machine intelligent components, provide an even deeper capability with many fewer access points.

If we repeat the data is the gold mantra, we see it reigning evidently true in these means, though data is more our shield, the more structured and tagged examples in every scope, the more capable of developing machine intelligence with attention for specific attributes. This can be seen through training unique identifiers or labels for context to help the computer develop an understanding of what should be sorted. For computer vision, this could be interactions across multiple layers of inputs, live or stored, for structured language data this could be for contextual references, phrases, or developing an awareness of a specifically trained segment.

Sources Mentioned:

Convolutional Neural Networks with Data Augmentation (-), Cryptography based on Artificial Neural Networks (-)

Thanks for Reading, Keep Encrypting!

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