How AI helps to detect Anomalies in the Network?
- Suresh M
- Nov 26, 2024
- 4 min read
Discover what pulls us back on a network packets and how AI models helps us to ensure safer networks!
Hello Everyone, Good Morning!
In this AI and digital world, nothing moves without communication networks. Communication is essential on every part of our day — say working in an office environment, browsing through the internet, casually scrolling through the youtube videos, connecting with people using social media, taking to people over voice over phones, etc.
Network data Packets:
Each piece of data is sent across a network, which is broken into multiple smaller manageable pieces of data packets, which is then transmitted to the targeted destination, then reassembled and delivered as a complete message. Network data packet analysis is required for many reasons they are primary tasks for cyber security professionals, network administrators, and IT infrastructure professionals, and even the software architects, cloud prefessionals, etc.
Such a network packet analysis helps to understand the network performance, security vulnerabilities, and potential threats. In this era of technology where network tools and capabilities are very sophisticated, such a strong ability analyzing the network packets can help prevent security breaches by providing useful insights on predicting threats.
There are couple of tools to analyze the network packets for managing the networks and troubleshoot the issues.
Network tools for visualization:
Wireshark provide the user friendly graphical interface for packet inspection, scanning through the packets in depth, visually analyzing the complex traffic patterns.
Tcpdump is a command line tool helps to capturing and filtering packets quickly in no time, especially in a resource constrained environment.
These tools also help the IT professionals with detailed insights to diagnose and resolve issues before it gets bombarded.
As two decaded over from the internet discovery during late 1990, threats in computer network systems are not new! Such a digital viruses, & cyber threats are many, and these can corrupt the files, steal the keys, make the users lose their financial assets, can make the system & the network completely broken, hacked and unusable. Hence Security is utmost the top priority for any kind of digital assets in the world, as an analogy, it can be comparable to the ceiling for a physical building, which is must for a building, same way is the network security for digital assets..
Anomaly Detection:
Anomaly detection in a network packet is finding the suspicious state of things within the network data that is different from the normal network behaviour patterns. Its useful for identifying security framework threats, network intrusions, and other malicious activities. These threats need to be identified as early as possible for the lesser damage to the network, improved incident response, utilizing the network resource in an optimized way.
There are multiple ways to detect the anomalies starting with traditional rule based methods, signature based methods to the statistical & AI based methods. AI based methods plays a significant role providing the innovative solution to anomaly detection for network packet analysis within cyber security.
Due to huge data being created from Internet, IoT, & cloud networks, the amount of data that needs to monitor for attacks is extremely difficult.AI-driven systems can recognize patterns, and irregularities which is not possible from the traditional systems.
AI Approaches for the Anomaly Detection:
Supervised Learning models (SL): Training the AI Models using history of data having normal (+ve signal states) and malicious network data (-ve signal states) packets. These models learn to categorize traffic as usual or irregular based on patterns observed in the training data. Some of the SL algorithms are Random Forest, Support Vector Machines, Logistic Regression, Deep Learning models.
Unsupervised Learning models (USL): USL not required to feed in the history network data (no label or signal states required), they can group the data by analyzing them finding characteristics and group them based upon their characteristics. Some of the USL algorithms are Clustering using Kmeans, DBSCAN,.. These can help in identifying extreme data states (outliers) easily from the whole dataset given.
Taking an example: I have 2 packets of data — first one from 9.00 am to 9.30 am — data is the usual snapshot of the usages/network traffic signal, uplink/upload, downlink/download and the second one from 9.30 am to 10.00 am — data is the usual snapshot of the usages/network traffic signal, uplink/upload, downlink/download + there is an intrusion entry into the system with dll files (dynamic link library files in windows systems, these files can trigger another activity which does unexpected email sends from the system). By looking closely at the data signals between 9.30am — 10.00am — there could be a way to identify this behaviour, Once we have this data stored and available to analyze, that’s the win situation. Our ML algorithms can do all the magics, provide the model solution which can help predict 99% of the network issues.
Major data pre-requisites:
Availability of data which help to distinguish which is malicious which is not (label).
Complex network patterns — what is changing in the network because of the malicious data, what is not changing as expected. This signal is the primary data component, without having this data, AI model won’t be able to predict desired result.
Balancing the accuracy of detection Vs Computational capacity required to produce the model accuracy.
Some AI Tools for Network Security:
Blu Vector (from Comcast tech solutions) automated Netwok Detection and Response: helps to detect and mitigates cyber attacks in no time. It can be extended to the customer premises too.
Cisco Stealth Watch (Secure network analytics): is a extensive network security tool utlizing advanced AI and ML tools to provide constant, real-time monitoring, anomaly detection, threat response for on-premises, remote work, & cloud environments.
AI powered detector which can detect threats such as malware, zero-day attacks (attacks happening much before user come to know/being vulnerable about it) , Distributed Denial-of-service (DDoS) attacks, Advanced Persistent threats (APT), & Insider threats.
We have reached the end of the story. We will meet next time with more interesting topics. Stay tuned!
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