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Phishing Email Analysis Tools
- Thread starter Trevor Chandler
- Start date
Spotting shady links and emails before they can reel you in.
precious, how do you go about spotting shady links? How do you go about spotting shady emails?
Thank you for sharing
Of course not always accurate like the tools you provided, when it comes to dubious web addresses, I must say they are just like spam email. If the web address is overly conspicuous or tempting or urges me to 'BE THE FIRST TO CLICK', I just ignore it.
You won't be failing an phishing assessments!!!Of course not always accurate like the tools you provided, when it comes to dubious web addresses, I must say they are just like spam email. If the web address is overly conspicuous or tempting or urges me to 'BE THE FIRST TO CLICK', I just ignore it.
Hahaha! Thanks! Funny though enough, even a college professor once fell so hard for a Nigerian prince scam that he drained his savings and even stole from the university to pay the scammers. When the FBI got involved, he accused them of trying to steal his 'millions.' Goes to show, anyone can slip up without awareness!
How about python regression models.
import pandas as pd
import numpy as np
df = pd.read_csv('../datasets/sms_spam_perceptron.csv')
X = df.iloc[:, [1, 2]].values
y = df.iloc[:, 0].values
y = np.where(y == 'spam', -1, 1)
from sklearn.linear_model import LinearRegression
linear_regression = LinearRegression()
linear_regression.fit(X,y)
print (linear_regression.score(X,y))
import pandas as pd
import numpy as np
df = pd.read_csv('../datasets/sms_spam_perceptron.csv')
X = df.iloc[:, [1, 2]].values
y = df.iloc[:, 0].values
y = np.where(y == 'spam', -1, 1)
from sklearn.linear_model import LinearRegression
linear_regression = LinearRegression()
linear_regression.fit(X,y)
print (linear_regression.score(X,y))
This expression is interesting but I need the person that can interpret it for me.