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Cyber Crime — Confusion Matrix
“PREVENTION IS BETTER THAN CURE”
Cyber-attacks are known to affect individual users as well as small, medium and large companies. When cyber-attacks happen, finding someone to help get rid of the problem becomes vital. If you don’t do that, you risk losing a lot of important data. Choosing the right people, however, is in itself a difficult task. Unfortunately, even working with the right team means using security methods that are simply outdated. The people behind recent and upcoming cyber threats are sure to know all the vulnerable parts of an antivirus by now. If they don’t, chances are they’ll find them very soon.
Common forms of cybercrime include:
- phishing: using fake email messages to get personal information from internet users
- misusing personal information (identity theft)
- hacking: shutting down or misusing websites or computer networks
- spreading hate and inciting terrorism
- Theft of financial or card payment data
- grooming: making sexual advances to minors
- Crypto jacking
Cybercriminals may infect computers with viruses and malware to damage devices or stop them working. They may also use malware to delete or steal data
Real-time response and quick detection are essential now more than ever.
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.
Artificial intelligence techniques can be used to learn how to remove noise or unwanted data and to enable security experts to understand the cyber environment in order to detect abnormal activity. Ai can also benefit cybersecurity with automated techniques to generate whenever cyber threats are detected.
A confusion matrix is the comparison summary of the predicted results and the actual results in any classification problem use case. It is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.
Lets Understand the Confusion matrix:
Consider the following example , which gives a clear view on confusion matrix.
- True Positive: You predicted positive and it’s true.
- False Positive: You predicted positive and it’s false.(Type 1 Error)
- True Negative: You predicted negative and it’s true.
- False Negative: You predicted negative and it’s false.(Type 2 Error)
Consider the case of Cyber Attack,
The most dangerous error is the False Positive [FP] error as the machine predicted false but it was not false it was true. For example, the machine predicted Cyber Attack doesn’t happen, but actually it was happened.
This error causes problems in the cybersecurity world where the tools used are based on machine learning or Ai, it may give a False Positive error that may cause dangerous impacts.
Therefore the role of the confusion matrix is important in the field of machine learning.
A parallel Support Vector Machine (pSVM) algorithm was proposed for the detection and classification of cyber attack datasets.
The performance of the support vector machine is greatly dependent on the kernel function used by SVM. Therefore, we modified the Gaussian kernel function in a data-dependent way in order to improve the efficiency of the classifiers. The relative results of both the classifiers are also obtained to ascertain the theoretical aspects. The analysis is also taken up to show that PSVM performs better than SDF.
The classification accuracy of PSVM remarkably improve (accuracy for Normal class as well as DOS class is almost 100%) and comparable to false alarm rate and training, testing times.
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