Cyber Crime Cases Where They Talk About Confusion Matrix Or Its Two Types Of Error..
What is a Confusion Matrix?
A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making.
For a binary classification problem, we would have a 2 x 2 matrix as shown below with 4 values:
- true positives (TP): These are cases in which Model has predicted “yes” (predicting they have the disease), and in reality they do have the disease.
- true negatives (TN): Our model has predicted “no”, and in reality they don’t have the disease.
- false positives (FP): Our ML model has predicted “yes”, but they in reality don’t actually have the disease. (Also known as a “Type I error.”)
- false negatives (FN): Trained model has predicted “no”, but in reality they do have the disease. (Also known as a “Type II error.”)
For the Security Team in an organization Type 1 error is most dangerous as the IDS will inform wrong ( false ) that there isn’t any Attack, But it’s wrong so the security team has to work on such kind of IDS errors. In this way security team get to know when and where to take action. Confusion Matrix of IDS help them to take correct actions in time.
Impact of Confusion Matrix on Cyber Security
What is Cyber Security?
Cyber security is the application of technologies, processes and controls to protect systems, networks, programs, devices and data from cyber attacks. It aims to reduce the risk of cyber attacks and protect against the unauthorised exploitation of systems, networks and technologies.
Cyber Attack Detection and Classification Using Parallel Support Vector Machine:
Cyber attack is becoming a critical issue of organizational information systems. A number of cyber attack detection and classification methods have been introduced with different levels of success that is used as a countermeasure to preserve data integrity and system availability from attacks. The classification of attacks against computer network is becoming a harder problem to solve in the field of network security.
The rapid increase in connectivity and accessibility of computer system has resulted frequent chances for cyber attacks. Attack on the computer infrastructures are becoming an increasingly Serious problem. Basically the cyber attack detection is a classification problem, in which we classify the normal pattern from the abnormal pattern (attack) of the system. Subset selection decision fusion method plays a key role in cyber attack detection. It has been shown that redundant and/or irrelevant features may severely affect the accuracy of learning algorithms. The SDF is very powerful and popular data mining algorithm for decision-making and classification problems. It has been using in many real life applications like medical diagnosis, radar signal classification, weather prediction, credit approval, and fraud detection etc.
For the Security Team in an organization Type 1 error is most dangerous as the IDS will inform wrong ( false ) that there isn’t any Attack, But it’s wrong so the security team has to work on such kind of IDS errors. In this way security team get to know when and where to take action. Confusion Matrix of IDS help them to take correct actions in time.
Types of cybercrime
There are a lots of cyber attack that we usually see or hear about. Some of them are…
- Email and internet fraud
- Identity fraud (where personal information is stolen and used).
- Theft of financial or card payment data.
- Theft and sale of corporate data.
- Cyberextortion (demanding money to prevent a threatened attack).
- Ransomware attacks (a type of cyber extortion).
- Crypto jacking (where hackers mine cryptocurrency using resources they do not own).
- Cyberespionage (where hackers access government or company data).
