Artificial Intelligence & Machine Learning for Log Analysis
Manual threat detection methods can’t keep up with the evolving tactics of cybercriminals. Machine learning (ML) can analyze large volumes of data quickly and accurately to detect potential threats. In this blog, we explore how ML can transform cybersecurity and Data analytics to keep our systems safe.
Introduction
The world of cybersecurity can be complex and overwhelming. It’s like looking for a needle in a haystack of log data, where potential threats are hidden amongst the overwhelming volume and complexity of data. Every day, organizations face the challenge of sorting through terabytes of logs, hoping to find clues to identify malicious threats or anomalies. But this manual process is time-consuming, prone to human error, and struggles to keep up with the constantly evolving attack methods used by cybercriminals. Traditional threat detection methods often fall short of catching the sophisticated tactics used by malicious actors. Machine learning (ML) is one of the dynamic and capable automated log analysis tools in the digital world that can help efficiently and accurately analyze vast amounts of log data, uncovering threats that might otherwise remain undetected. This technology can potentially transform how we approach cybersecurity and keep our systems safe.
What is log analytics in machine learning?
Application of different techniques and algorithms to examine the data generated from sources such as networks, applications & systems also known as “Machine Data” & then proactively extract insights, find patterns, recognize an anomaly & serve accurate predictions based on the findings from the logs.
What is AI log analysis?
It involves using artificial intelligence methodologies such as natural language processing(nlp) to examine logs from applications, networks & systems to get insights, find abnormalities & improve system performance.
What is the purpose of log analysis?
- Infrastructure monitoring and troubleshooting to find out errors, and issues in real time or historically
- Performance optimization by finding out the bottlenecks and understanding the metrics
- Security Monitoring, which involves finding unauthorized access or any suspicious activities
- Compliance & audit to follow regulations and internal policies
- Predictive maintenance for the forecast potential failures or issues before they occur
- Business Intelligence to figure out a user action or a user pattern to make business decisions
Benefits of ML/AI for log Analysis
1. Categorize info rapidly/faster
It helps quickly categorize log data from log streams, resulting in proper organization and prioritization of logs for analysis or action.
2. Identification of issues automatically
It helps to detect issues automatically and enables proactive identification by finding the root cause of an issue, sorting out the hard problems before they escalate.
3. Alert critical info
Generate alerts for critical event types & abnormalities in real time, which helps create proper mitigation strategies and timely responses to prevent security breaches and service disruptions.
4. Allocation of resource in an efficient manner
It helps to optimize allocation, thereby increasing efficiency and reducing cost by making sure that actual demand is taken into consideration to allocate resources.
5. Scalable options
Can easily accommodate growth in data size & complexity without affecting the performance significantly.
6. Environments agnostic
It can seamlessly adjust to different environments like development, testing, and production, increasing its ability to generalize patterns and abnormalities across diverse setups and configurations.
What is an example of a log analysis?
Below are some of the points on How to automate log analysis!
1. Data collection & Preprocessing
The former refers to gathering logs from the web server that might include IP addresses, time stamps, response codes, request types, etc. The latter refers to Cleaning and preprocessing, such as converting timestamps into a standard format and parsing out relevant fields like IP addresses or request types.
2. Feature Extraction
Extracting meaningful features from the figure collected, such as the frequency of requests per IP address, errors encountered & last but not least average response times within a specific given time window.
3. Visualizations and Interpreting
Imaginations of results to understand patterns and outliers in the log data by executing time series data, generating & observing heatmaps, or using dashboards.
4. Deployment and Monitoring
Deploy log anomaly detection model & set up alerts for continuous processes of incoming log info and send an alert to administrators when significant changes are noticed respectively.
Common Types of ML/AI models
Below are a few AI / ML log analysis approaches!
1. Supervised machine learning models
supervised learning models trained on historical malware samples can quickly classify the threat, allowing security teams to take instant action to contain and eliminate the infection.
(E.g) – Logistic Regression, Linear Support Vector Machines (SVM)(identifies patterns in string based data), Decision Trees, Random Forest, Neural Networks (for classification tasks), Linear Regression, Polynomial Regression, Support Vector Regression, Neural Networks (for regression tasks).
2. Unsupervised machine learning
Unsupervised learning algorithms can uncover a series of unauthorized access attempts across multiple user accounts, signaling a potential data breach in progress.
(E.g) – K-means & Hierarchical Clustering, DBSCAN, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding, Autoencoders.
3. Semisupervised model
It uses labeled and unlabelled data (two major categories falling under data labeling) for training.
(E.g) – Self-training & Co-training, Transfer learning approaches.
4. Reinforcement model
Makes use of trial and error methods to achieve its goal.
(E.g) – Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods.
5. Deep Learning model
Deep Learning to detect anomalies in logs is an extremely expensive way for Neural networking with multiple layers (deep architecture) to learn representations of info.
(E.g) – Convolutional Neural for image info, Recurrent Neural for sequential info, Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) for handling sequential with long-range dependencies, Transformer like BERT and GPT for natural language processing tasks.
6. Ensemble Model
Contains the combination of multiple Popular models resulting in performance improvement.
(E.g) – Bagging: Random Forest, Boosting: Gradient Boosting Machines (GBM), AdaBoost, Stacking: Combining multiple models via a meta-learner.
7. Instance-based Model
Uses specific examples rather than generic rules to learn.
(E.g) – k-Nearest Neighbors (k-NN).
8. Probabilistic Model
Makes use of probability distributions and represents uncertainty.
(E.g) – Bayesian Networks, Gaussian Mixture & Hidden Markov.
AI/Machine learning approach to log analytics & challenges
The sheer volume, velocity, and variety of log data generated by modern systems is expanding rapidly and is nothing short of staggering. Terabytes of logs are produced every second, capturing all the actions, log events, and transactions happening across the digital ecosystem. This escalating growth poses a major challenge for traditional security tools, which can struggle to keep up with the scale and complexity of the Months of data set.
In the age of information, it’s easy to overlook valuable security insights amidst the abundance of available data. More often than not, crucial indicators of malicious activity are hidden among the haystack of logs, making real-time threat detection feel like searching for a needle in a stack of needles. The sheer volume of benign data can easily lead to overlooking critical alerts.
Compounding this challenge, security teams also deal with an overwhelming number of false alarms triggered by traditional methods. These false positives create confusion and waste valuable time and resources. Moreover, they divert attention from real threats, which remain hidden in the shadows.
Analyzing huge amounts of data manually is a challenging task, which would require various approaches from a large team of analysts and an indefinite amount of time. Even with a dedicated workforce, the sheer size of the data makes manual inspection impractical, if not impossible. This leaves organizations vulnerable to undetected security threats that may be hidden within the data.
Furthermore, traditional rule-based security systems are no longer effective in keeping up with the constantly evolving tactics of threat actors. These systems rely on static rule sets that can’t adapt quickly enough to counter emerging attack vectors. As the threat landscape continues to change, the limitations of rule-based systems become more apparent, highlighting the need for a more agile and proactive & different approach to threat detection.
Machine Learning Comes to the Rescue
The challenge of detecting hidden threats from a vast amount of logs can be overwhelming, but machine learning offers a promising solution. Unlike humans, machine learning algorithms can analyze large volumes of data with lightning speed and unwavering accuracy. Using advanced statistical models, ML models can sift through logs, detecting subtle anomalies and patterns that often elude the human eye. This makes machine learning a valuable tool that helps find that needle in the haystack.
In the field of cybersecurity, machine learning (ML) algorithms are utilized in advanced platforms equipped with a range of techniques designed for threat detection. Anomaly detection algorithms identify metric anomalies from normal behavior, warning security teams of possible malicious activity. Supervised training models requires labeled datasets to accurately identify known threats, while unsupervised learning algorithms reveal new threats that are hidden in the background without the requirement of predefined labels.
Imagine a situation where a cybersecurity platform powered by machine learning detects a malware infection in an organization’s network. By analyzing log data from different endpoints and network devices, the platform’s anomaly detection algorithms can identify unusual patterns of file access and execution that may indicate a potential malware outbreak.
With these insights, security teams can act quickly to mitigate the breach and protect sensitive information from being compromised. In both cases, machine learning proves to be an asset in the ongoing fight against cyber threats. By transforming the haystack of logs into actionable intelligence, ML helps security teams stay a step ahead of cybercriminals.
Evolution of Log analysis using AI & ML
1969: Bell Labs Unix was the one who laid down the foundation. During this period, operating systems did not have the necessary tools to aggregate log files, due to which admins had to rely on text manipulation tools to understand the log files on an as-needed basis.
1990s: During this period, the traditional log analysis evolved and became even more complicated. Each boot-up, system event type, and application had separate logs. A proprietary log analysis tool like BootHawk was introduced, which catered to specific tasks and helped enhance visibility into log data.
1998: Syslog-ng was introduced during this phase to deal with the growing demand for log collection. They played a pivotal role in enhancing data transmission providing a wide range of support to applications and operating systems in the form of a unified interface, which helped IT teams study data from multiple locations.
2004: Rsyslog, which came from the Sysklogd standard package, emerged in the market. This open source tool aimed to provide rich features & reliable syslog daemon without affecting its drop-in replacement capabilities to stock syslogd.
Current scenario: People have adapted to agile/DevOps instead of using Waterfall for reinforcing automation, Machine learning and AI, Which was very important looking at the volumes of data generated each day & it wasn’t feasible to entirely rely on manual operations.
Your Dynamic Threat Defense Platform
Welcome to the cutting-edge world of modern cybersecurity defense with NewEvol, our Dynamic Threat Defense Platform. Our all-in-one cybersecurity platform uses machine learning for advanced threat detection and response. Harnessing the unparalleled capabilities of ML, it navigates through the haystack of logs with accuracy and agility, making it an innovative solution for any organization concerned about cybersecurity.
With the advanced log aggregation capability of the Data Lake solution, NewEvol consolidates logs from different sources into a central repository for comprehensive analysis & log management. It uses sophisticated anomaly detection algorithms to sift through vast Log volumes, quickly identifying any variations from normal behavior that could indicate potential threats. Each anomaly is thoroughly evaluated and assigned a threat score, giving security teams actionable insights into the severity and urgency of the threat.
Our DTD platform is equipped with an automated incident response capability that enables organizations to respond to security threats in real time with remarkable speed and efficiency. This feature automates response actions based on predefined rules and policies, which helps reduce response times and mitigate the impact of security incidents before they escalate into severe breaches.
Our Dynamic Threat Defense Platform has been tested and proven in the real world, helping organizations across various industries detect anomalous activities and respond to threats more efficiently. It can detect sophisticated malware infections, stop unauthorized access attempts, and mitigate data breaches, ultimately safeguarding sensitive assets and preserving the integrity of organizational networks.
A client sought a solution to monitor various smart devices in a smart city, with a wide range of devices placed in public places. Compliance requirements set by the government also had to be met. We deployed a Security Information and Event Management (SIEM) system with cybersecurity analytics platform and threat intelligence capabilities to monitor the smart city’s end-to-end environment, including custom alerts, dashboards, and reports created combining log lines. Machine learning algorithms detected anomalies in large volumes of network traffic on public devices, and threat intelligence capabilities kept the analyst updated with the latest threat feeds.
With NewEvol, enterprises can stay one step ahead of cyber threats, transforming the haystack of logs into a powerful management tool for proactive defense in today’s ever-evolving threat landscape.
Conclusion
Amidst the overwhelming volume and complexity of logs, machine learning offers unprecedented speed, accuracy, and agility in threat detection. With our Dynamic Threat Defense Platform, organizations can consolidate & Analyze logs, detect anomalies in logs, and automate response actions to stay ahead of threat vectors and protect sensitive assets. Join us in redefining the future of cybersecurity and Book a Demo today.
