Cyber Security And Machine Learning

In last few decade, Use of internet is increasing rapidly.  And Cyberthreats are now one of the most urgent concerns in the world.

What is Machine Learning?

             Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. 

What is Cyber security?

             Cyber security refers to the body of technologies, processes, and practices designed to protect networks, devices, programs, and data from attack, damage, or unauthorized access. Cyber security may also be referred to as information technology security.

How we use Machine learning in cyber security?

             With machine learning, cybersecurity systems can analyze patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help cybersecurity teams be more proactive in preventing threats and responding to active attacks in real time.

What is Confusing Matrix?

             A confusion matrix is a technique for summarizing the performance of a classification algorithm. Classification accuracy alone can be misleading if you have an unequal number of observations in each class or if you have more than two classes in your dataset.

             The evaluation of the classifier is done based on the confusion matrix. For a binary problem, this is a 2×2 matrix whose columns are labeled predicted classes and whose rows are actual classes. Sometimes it may be the other way around — it depends on the implementation, but for someone who understands the confusion matrix and hasn’t learned it by heart, this is not a problem. Based on the test set, the Confusion Matrix distributes the decisions that the classifier made:

True Positive: Machine Predicted Positive and its true.

True Negative: Machine Predicted Negative and its true.

False Positive (Type 1 error) : Machine Predicted Positive and its False.

False Nagative (Type 2 error) : Machine Predicted Negative and its False.

In the confusion matrix, the main diagonal contains the counted correct decisions. Classifier errors are false positives and false negatives.

Most Dangerous error is false positive error. Its like machine predicted today weather will be clear and suddenly heavy raining started.

The Detection of Attack and Normal Pattern Can be Generalized as Follows:

In above Case, True Positive (TP): The amount of attack detected when it is actually attack. True Negative (TN): The amount of normal detected when it is actually normal. False Positive (FP): The amount of attack detected when it is actually normal (False alarm). False Negative (FN): The amount of normal detected when it is actually attack.

In the confusion matrix above, rows correspond to predicted categories, while columns correspond to actual categories.