In the past year, 493.33 million ransomware attacks have been detected by organizations worldwide. Cybersecurity has been part of our lives since the advent of the internet. However, as our dependence on the digital world deepens, so does the creativity of those seeking to breach its defenses. The old saying "necessity is the mother of invention" has taken on a whole new meaning in the segment of cybersecurity.
Simple passwords and basic firewalls are things of bygone days. Today’s digital battleground stands stronger with Machine Learning. Discover how Machine Learning is revamping cybersecurity to help recognize and impart advanced protection from online threats.
Here’s what we’ll cover:
- Why Do We Need Machine Learning in Cybersecurity?
- How Do We Use Machine Learning in Cybersecurity?
- What Are the Types of Cybersecurity Machine Learning?
- Advantages and Disadvantages of Machine Learning
- Machine Learning Threat Detection in Real-Time
- Upgrade Your Machine Learning Career With Interview Kickstart
- Frequently Asked Questions on Machine Learning for Cybersecurity
Why Do We Need Machine Learning in Cybersecurity?
The traditional approaches to Machine Learning were based on signature-based detection systems. It worked by comparing the signature of incoming traffic with a database of already identified threats and malicious code signatures. The imparted security was beneficial in protection from known threats. However, in case of new threats, the approach failed. Simple code modification or easily developing new malware or any other malicious software is enough to defraud.
Additionally, the signature-based detection system, if matched with legitimate traffic, would lead to a loss of users. Combating the problem would require manual analysis, which guaranteed inaccuracy, requirement of expertise, and time.
In today’s evolving cybersecurity world, Machine Learning serves as a key to staying a step ahead of the threats. It arms us with the power of real-time threat detection.
How Do We Use Machine Learning in Cybersecurity?
Machine Learning algorithms are used in cybersecurity in the following ways:
- Logistic regression: To predict the network security outcomes
- Linear regression: To detect fraud
- Dimensionality reduction algorithms: To remove useless data
- Support Vector Machine (SVM): To detect, classify and predict blacklisted port and IP addresses
- Naive Bayes algorithm: To detect intrude
- Random forest algorithm: To classify phishing attacks
- Decision tree algorithm: To detect and classify attacks
- K-nearest neighbors classifier (kNN): To authenticate via facial features
- K-means clustering: To detect malware
What Are the Types of Cybersecurity Machine Learning?
Machine Learning is of the following types: supervised, unsupervised, semi-supervised, and reinforcement.
Supervised Machine Learning
It is used for data classification or outcome prediction. It contributes to machine learning and cybersecurity in the following ways:
- Scanning and spoofing: Recognition of uniquely labeled networks
- Prediction and classification: To understand specific security threats such as DDOS attacks through regression modeling. Further, ensemble learning combines the Machine Learning models to produce accurate predictions
- Training: Predict the novelness of malicious samples
- Classification of categories: Binary and multi-class classification
The different techniques used in supervised machine learning are Random forest, adaptive boosting, Naive Bayes, linear and logistic regression, and Support Vector Machines (SVM).
Unsupervised Machine Learning
It is used for pattern recognition and automated groupings. It assists in analyzing and clustering the datasets that are unclassified. It is used for:
- Alleviate zero-day attacks
- Recognize unusual behavior regardless of prior presence in the database
- Recognize new attack patterns.
- Anomaly detection
- Dimensionality reduction
- Association mining
Different techniques used in unsupervised machine learning in cybersecurity are neural networks, probabilistic clustering, K-mean clustering, singular value decomposition (SVD), and Principal component analysis (PCA).
Semi-Supervised Machine Learning
It is capable of feature extraction in the absence of labeled data. Tagged as an effective use case in the situation of expensive labeled data, semi-supervised machine learning serves cybersecurity through:
- Malware detection
- Ransomware detection
- Malicious and benign bot identification
- Adversarial neural networks
- Machine translation
- Fraud detection
- Labeling data
The specific techniques of significance here are self-training, consistency regularization, label propagation, and pseudo-labeling.
Reinforcement Machine Learning
Serving data classification and outcome prediction, reinforcement learning works by trial and error method. The usage in several ways:
- Cyber-physical systems
- Autonomous intrusion detections
- Distributed Denial of Service (DDoS) defenses
- Adversarial simulation
The reinforcement techniques that can be used here are Deep Q network (DQN), deep deterministic, and policy gradient (DDPG).
Machine Learning Threat Detection in Real-Time
Real-time threat identification is the most important need at current times. Leveraging the power of Machine Learning here is the best way to introduce the desirable.
How is it done: The stated task is achieved by training the models on previous for efficient recognition of behaviors and attack patterns. The ML or AI algorithms exhibit spontaneous adjustment of the models according to the evolution of threats and new infection methods. The increased processing efficiency owing to previous training and self-evolution helps in the recognition of anomalies, compromise indicators, and identifying suspicious patterns.
What it does: The identification of threats generates alerts and notifications for swift response. The detailed report is generated comprising threat classification, a measure of possible and most suited remediation actions. On recognition, it also performs automated actions like quarantining the affected system. It also isolates the affected systems, initiates incident response workflows, or blocks malicious activities. Moreover, the lowered detection time further reduces the damage the malicious activity can cause through automated response actions.
- Prevents data breach
- Detects and neutralizes threats
- Minimizes financial loses
- Minimize the dwell time of attackers
- Safeguards organizational reputation
- Reduces the likelihood of data exfiltration
- Reduces unauthorized access
Advantages and Disadvantages of Machine Learning
The following table will help you realize the possible advantages and disadvantages of ML in cybersecurity:
- Easily identifies trends and patterns
- No human intervention needed
- Continuous improvement
- Wide applications
- Handles multidimensional and multi-variety data
- Data acquisition
- Interpretation of results
- Time and resources
- Error susceptibility
Upgrade Your Machine Learning Career With Interview Kickstart
Machine learning has found novel applications in a wide spectrum of industries. Serving all the varieties, cybersecurity also leverages the benefits offered here. However, a successful career in the Machine Learning domain begins with an understanding of basic concepts. Excelling at these becomes a tedious task without a structured plan or guidance.
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Frequently Asked Questions on Machine Learning for Cybersecurity
Q1. What are the benefits of machine learning in cybersecurity?
Ans. Machine learning offers benefits like identification of hidden vulnerable areas, automation of cybersecurity processes, minimization of humane errors, reduction of workloads, helping prevent security threats against endpoints, and easing security analysts.
Q2. Is there a rising need for cybersecurity?
Ans. The switch to internet administration and cloud storage space in business places has led to increased demand for cybersecurity.
Q3. Do hackers use Machine Learning?
Ans. Machine Learning is used to create malware to dodge advanced security solutions.
Q4. What are the research topics for cybersecurity and Machine Learning?
Ans. The most popular topics for research in this field are ransomware, cyber threat identification, network intrusion detection, user behavior modeling, web shells, and others.
Q5. What are the four modules of cyber security?
Ans. The four modules of cybersecurity are the foundation of computing and cybersecurity, endpoint security, secure coding, and business infrastructure and security.
Q6. What are the types of cybersecurity?
Ans. The different types of cybersecurity are network, application, cloud, mobile, endpoint, data, Internet of Things (IoT), and critical infrastructure security.