In this article we will see that what is confusion matrix and why do we need it and how with the help of machine learning we can detect cyber crime any many other things.
What is Confusion Matrix and why you need it?
Well, it is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.
It is extremely useful for measuring Recall, Precision, Specificity, Accuracy and most importantly AUC-ROC Curve.
Let’s understand TP, FP, FN, TN in terms of pregnancy analogy.
True Positive:
Interpretation: You predicted positive and it’s true.
You predicted that a woman is pregnant and she actually is.
True Negative:
Interpretation: You predicted negative and it’s true.
You predicted that a man is not pregnant and he actually is not.
False Positive: (Type 1 Error)
Interpretation: You predicted positive and it’s false.
You predicted that a man is pregnant but he actually is not.
False Negative: (Type 2 Error)
Interpretation: You predicted negative and it’s false.
You predicted that a woman is not pregnant but she actually is.
Just Remember, We describe predicted values as Positive and Negative and actual values as True and False.
This is a list of rates that are often computed from a confusion matrix for a binary classifier:
Accuracy: Overall, how often is the classifier correct
(TP+TN)/total
Error rate: Overall, how often is it wrong?
(FP+FN)/total
Recall: When it’s actually yes, how often does it predict yes
TP/actual yes
False Positive Rate: When it’s actually no, how often does it predict yes —
FP/actual no
Specificity: When it’s actually no, how often does it predict no
TN/actual no
Precision: When it predicts yes, how often is it correct
TP/predicted yes
Cyber crime and use of machine learning in cyber crime detection.
Cybercrime, or computer crime, is a crime that involves a computer and a network. The computer may have been used in the commission of a crime, or it may be the target.Cybercrime may harm someone’s security and financial health.Warren Buffet describes Cybercrime as the "number one problem with mankind" and "poses real risks to humanity."
Use of machine learning in cyber crime detection-
Machine learning has become a vital technology for cybersecurity. Machine learning preemptively stamps out cyber threats and bolsters security infrastructure through pattern detection, real-time cyber crime mapping and thorough penetration testing.
A subset of artificial intelligence, machine learning uses algorithms born of previous datasets and statistical analysis to make assumptions about a computer’s behavior. The computer can then adjust its actions — and even perform functions for which it hasn’t been explicitly programmed.
Check out these companies that use machine learning to bolster their cybersecurity systems and keep malware at bay.
MICROSOFT
Location: Redmond, Washington
How it's using machine learning: Microsoft uses its own cybersecurity platform, Windows Defender Advanced Threat Protection (ATP), for preventative protection, breach detection, automated investigation and response. Windows Defender ATP IS built into Windows 10 devices, automatically updates and employs cloud AI and multiple levels of machine learning algorithms to spot threats.
CHRONICLE
Location: Mountain View, California
How it's using machine learning: Chronicle is a cybersecurity company that sprang from Google's parent company Alphabet. Its first product, Backstory, has been described as “designed for a world where companies generate massive amounts of security telemetry and struggle to hire enough trained analysts to make sense of it.” Backstory analyzes large amounts of security data (such as internal network activity, known bad domains and suspected malware) and uses machine learning to condense it into more easily digestible insights.
SPLUNK
Location: San Francisco, California
How it's using machine learning: Splunk software has a variety of applications, including IT operations, analytics and cybersecurity. It's designed to identify a client’s current digital weak points, automate breach investigations and respond to malware attacks. Products like Splunk Enterprise Security and Splunk User Behavior Analytics use machine learning to detect threats so they can be quickly eliminated.
Thank you for reading and I hope this will help you.
