development

AI-Based Vulnerability Scanning for Cloud Infrastructure: A New Era in Security The rapid adoption of cloud computing has brought about numerous advantages for organizations, including scalability, cost-efficiency, and increased agility. However, this shift has also introduced new security challenges, particularly regarding the identification and mitigation of vulnerabilities within complex and dynamic cloud environments. Traditional vulnerability scanning methods often struggle to keep pace with the evolving threat landscape and the ephemeral nature of cloud resources. This is where AI-based vulnerability scanning emerges as a crucial component of modern cloud security strategies. The Limitations of Traditional Vulnerability Scanning: Traditional vulnerability scanners typically…

import boto3 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import joblib import tempfile from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.feature_selection import SelectKBest, f_classif from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix # Define S3 details bucket_name="employee-data" file_key = 'inputfiles/employee_cleaned_data.csv' # Load dataset from S3 s3_client = boto3.client('s3') obj = s3_client.get_object(Bucket=bucket_name, Key=file_key) df = pd.read_csv(obj['Body']) # Data preprocessing df = df.drop(columns=['employee_id']) df['region'] = df['region'].str.extract('(d+)').astype(int) X = df.drop(columns=['turnover']) y = df['turnover'] # Column transformer preprocessor = ColumnTransformer( transformers=[ ('num', StandardScaler(), ['age', 'salary']), ('cat', OneHotEncoder(), ['department',…