Many functions in the OpenSSL library require the use of the OpenSSL pseudo-random number generator. Even if you use something like /dev/urandom yourself, OpenSSL will use its own API under the hood and thus must be seeded properly. Unfortunately, some platforms and some older versions of OpenSSL require the user to provide a secure seed. Even modern implementations of OpenSSL merely read a seed from /dev/urandom when it is available; a paranoid user may wish to do better. When using OpenSSL, you may want to use the provided PRNG for other needs, just for the sake of consistency.

A small insurance company has an online application system that allows its customers to interactwith the business (e.g., log claims). Backups of the customers’ details are done on a spreadsheetdocument stored on Google drive. However, anyone who has access to the Google drive link ofthe file can view and modify all the customer details. On the backup, the system administratordoes not have visibility of the modifications made on the file.  By analysing the scenario above, use the security design principles to criticise theoperations of the small insurance company and propose any two simple controlsthat could influence a good security program.

If two links to an existing file are created in the same directory as the original (using the link syscall), how many inodes are used to store information about the three directory entries?

I've wrote a script to find the top three features for a random forest, but it is not work.please assist to fix the below code. #Import scikit-learn dataset libraryfrom sklearn import datasets#Load datasetiris = datasets.load_iris()# Creating a DataFrame of given iris dataset.import pandas as pdimport numpy as npdata=pd.DataFrame({'sepal length':iris.data[:,0],'sepal width':iris.data[:,1],'petal length':iris.data[:,2],'petal width':iris.data[:,3],'species':iris.target})iris['target_names']print(data.head())# Import train_test_split functionfrom sklearn.model_selection import train_test_splitX=data[['sepal length', 'sepal width', 'petal length', 'petal width']] # Featuresy=data['species'] # Labels# Split dataset into training set and test setX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)#Import Random Forest Modelfrom sklearn.ensemble import RandomForestClassifier#Create a Gaussian Classifierrf=RandomForestClassifier(n_estimators=100)#Train the model using the…