Industrial use cases of neural network

What is Neural Network ?

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process the same way as the human brain operates.

neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. it contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.

Application of Neural Network

Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics and product maintenance, Documents and form processing, Graphics recognition, Finance Industry, Quality assurance and Medical and health care Industry.

NEURAL NETWORKS IN CYBER INDUSTRY

Protecting digital assets and intellectual property is a greatest challenge for organizations. Today external hacking as the primary cause of data loss in the corporate industry. Organizations want to take adequate measures to protect data from loss or leakage.

unchecked IT cyber security risk factors that remain unmitigated are the greatest cause for unexpected cyber attacks.

The artificial neural network is playing most important role in network management. Most of the research in the area of intrusion detection system relies extensively on AI techniques to design, implement and enhance security monitoring system.

How Intrusion Detection System is using neural networking?

Intrusion detection systems (IDS) have been created to predict current and future attacks. Neural networks are used to identify and predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm plays key roles in this procedure.

An artificial Neural Network consists of a collection of treatments to transform a set of inputs to a set of searched outputs, through a set of simple processing units, or nodes and correlation between them.nodes between input and output form hidden layers. then it is determine how much one unit will affect the other i.e weights.

Artificial neural networks offer the potential to resolve a number of the problems encountered by the other current approaches to intrusion detection.

Supervised training algorithms: the learning phase, the network learns the desired output for a given input or pattern. supervised neural network is the Multi-Level Perceptron (MLP).the MLP is employed for Pattern Recognition problems.

Unsupervised training algorithms: in the learning phase, the network learns without specifying desired output.

ARTIFICIAL NEURAL NETWORKS (ANNS) IN INTRUSION DETECTION

Some IDS designers use soft computing techniques for dealing with uncertain and partially true data makes them attractive to be applied in intrusion detection some studies have used soft computing techniques other than ANNs in intrusion detection

Some IDS designers use ANN approach as a pattern recognition technique. Pattern recognition is implemented by using a feed-forward neural network according to the data for training.

training results optimized neural network parameters to associate outputs with corresponding input patterns. When the neural network is used, it identifies the input pattern and tries to output the corresponding class. When a connected record has no output associated with it is given as an input. The neural network gives the output that corresponds to a taught input pattern that is least different from the given pattern.

MISUSE IDS WITH NEURAL NETWORKS

Misuse systems are based on expert knowledge of the usual attacks

It spends more time doing compression of the system activity with database of attack signatures. Some signatures are to define and check the database directly.

if it encounters Port scan then usually it is an attempt to intrude the system usually via internet.

Misuse system analyzes the incoming packets, which could be in some items modified by intruder to meet the intentions.

Neural Network and Artificial Neural network has resulted in greater accuracy and sensitive data security such IDS are later on integrated with further automated security actions for that particular database.

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reference: International Research Journal of Computer Science (IRJCS)