AI Helps Make Cybersecurity Simple in 2023
Tags: cybersecurity, ai, python, scripting
Published: 2023-05-09 23:44:15

When it comes to AI and cybersecurity in 2023, I’ve got to say, count me in! I’m not just cautiously optimistic; I’m downright enthusiastic. In fact, I think AI might just be the hero that cybersecurity needs right now.

As cyber threats become more sophisticated, traditional security measures like firewalls and antivirus software are unfortunately no longer sufficient. To keep up with evolving threats, I, among others in this field, have increasingly started turning to artificial intelligence (AI) to help defend against attacks. In this article, I explore the specific tools and techniques available for cybersecurity professionals to harness AI effectively.

*Please note that throughout this article I use the British spelling of words other than the code, which had to be written using American English.*

## Machine Learning for Threat Detection

One of the most promising applications of AI in cybersecurity is in threat detection. By training machine learning models on large datasets of past attacks, these models can learn to identify new threats and respond more quickly and effectively than traditional signature-based approaches.

**For example, look at the below Python code, which uses the [scikit-learn ](https://scikit-learn.org/)library to train a machine learning model on a dataset of known malware samples:**

    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    import pandas as pd
    
    # Load the malware dataset
    malware_data = pd.read_csv('malware.csv')
    
    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(
        malware_data.drop('class', axis=1),
        malware_data['class'],
        test_size=0.2,
        random_state=42
    )
    
    # Train a random forest classifier on the training data
    clf = RandomForestClassifier()
    clf.fit(X_train, y_train)
    
    # Evaluate the performance of the classifier on the testing data
    score = clf.score(X_test, y_test)
    print(f"Classifier accuracy: {score}")

This loads a dataset of known malware samples, splits the data into training and testing sets, and trains a random forest classifier on the training data. Then it evaluates the performance of the classifier on the testing data, using the score() method to calculate the accuracy of the model.

Obviously, the process of training a machine learning model for threat detection is much more complex than this simple example. The basic idea is still the same, though: by leveraging machine learning algorithms, it is possible to detect new threats more effectively than traditional approaches.

## Natural Language Processing for Fraud Detection

Another area where AI can be used to combat digital threats is fraud detection. Natural language processing (NLP) techniques can be used to analyse large volumes of text data, such as emails and social media messages, to identify signs of fraudulent activity.

**This script uses the [Natural Language Toolkit (NLTK)](https://www.nltk.org/) library to analyze a sample of emails and identify potential signs of fraud:**

    import nltk
    import pandas as pd
    
    # Load the email data
    email_data = pd.read_csv('emails.csv')
    
    # Tokenize the text of each email
    tokenized_emails = [nltk.word_tokenize(email) for email in email_data['text']]
    
    # Identify named entities in the text of each email
    named_entities = [nltk.ne_chunk(nltk.pos_tag(email)) for email in tokenized_emails]
    
    # Extract the organisation entities from the named entities
    organizations = [[entity for entity in email if isinstance(entity, nltk.tree.Tree) and entity.label() == 'ORG'] for email in named_entities]
    
    # Count the frequency of each organisation entity
    org_counts = pd.Series([org[0][0] for email in organizations for org in email]).value_counts()
    
    # Print the top 10 most common organisation entities
    print(org_counts[:10])

The script loads email data from a CSV file, tokenizes the text of each email, identifies named entities in the text using part-of-speech tagging, extracts organisation entities from the named entities, and then counts the frequency of each organisation entity. Finally, it prints the top 10 most common organisation entities in the email data. This could be useful for tasks such as identifying potential phishing targets or detecting mentions of specific companies in a large email dataset.

## Anomaly Detection Algorithms

The Isolation Forest Algorithm can be effectively used when looking for anomalies in large pieces of data. I will walk you through implementing this more sophisticated algorithm for anomaly detection that can cope with high-dimensional datasets.

**First, import the necessary libraries:**

    import numpy as np
    from sklearn.preprocessing import StandardScaler
    from sklearn.ensemble import IsolationForest

**Next, load the data from the log file:**

    with open('system_log.txt') as f:
        data = []
        for line in f:
            # Parse the log line and extract the relevant features
            feature_1, feature_2, feature_3 = parse_log_line(line)
            data.append([feature_1, feature_2, feature_3])

**Then normalise the data using standard scaling:**

    data = np.array(data)
    scaler = StandardScaler()
    data = scaler.fit_transform(data)

**Now the fun AI-ey part of training the Isolation Forest model on the normalised data and using the model to predict the anomalies:**

    model = IsolationForest(random_state=0)
    model.fit(data)
    anomaly_scores = model.decision_function(data)
    threshold = -0.5

**and the result should be printed as so:**

    for i, score in enumerate(anomaly_scores):
        if score < threshold:
            label = 'anomaly'
        else:
            label = 'normal'
        print(f"Data point {i} has an anomaly score of {score:.3f} and is classified as {label}.")

The Isolation Forest algorithm is so popular because it’s an unsupervised machine learning algorithm that works by isolating anomalies in the data set by randomly partitioning the data points and building isolation trees.

This code, along with most of the code from [my articles,](https://medium.com/@williambaptist) can be easily adapted to work with different log files and clustering algorithms.

## AI Network Defence Systems

Your network can be defended by a deep learning system; in fact, [most companies](https://6sense.com/tech/endpoint-protection/crowdstrike-market-share) are using AI right now to protect their networks (including Medium!)

**Here is an example of a recent AI-powered network defence system using deep learning models:**

    import numpy as np
    import tensorflow as tf
    
    # Load the network traffic data
    data = np.loadtxt('traffic.csv', delimiter=',')
    
    # Preprocess the data
    x = data[:, :-1]
    y = data[:, -1]
    num_classes = len(np.unique(y))
    y = tf.keras.utils.to_categorical(y, num_classes=num_classes)
    
    # Define the deep learning model
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(32, activation='relu', input_shape=(x.shape[1],)),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    
    # Compile the model
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    # Train the model
    model.fit(x, y, epochs=10, batch_size=32)
    
    # Use the model for network defence
    def defend_network(new_data):
        # Preprocess the new data
        x_new = np.array(new_data)
        x_new = np.expand_dims(x_new, axis=0)
        
        # Predict the class of the new data
        prediction = model.predict(x_new)
        return np.argmax(prediction)
    
    # Test the network defence system
    test_data = [20, 300, 1000, 50, 200, 400, 800]
    print(defend_network(test_data))

The traffic.csv file contains preprocessed network traffic data, where the last column contains the class label. The data is split into input features (x) and class labels (y), which are one-hot encoded.

The deep learning model is defined using the tf.keras.Sequential API, with dense layers and a softmax output layer. The model is compiled using the adam optimizer and categorical cross-entropy loss. The model is trained using the fit method with a batch size of 32 and 10 epochs. The defend_network function is defined to preprocess new data, predict the class of the new data using the trained model, and return the predicted class label. A test data array is defined, and the defend_network function is called to predict the class label of the test data.

I’ve showcased that AI is a powerful tool that can greatly enhance cybersecurity defences by enabling faster and more accurate threat detection and response. From anomaly detection algorithms to natural language processing for fraud detection, AI is making a significant impact in the fight against digital threats rather than just contributing to more problems for blue teamers. It’s important to continue developing and implementing new AI-based technologies to stay ahead of the ever-evolving threat landscape.