How to Use Opencv and Python to Create an Intelligent Intrusion Detection System

With the rise of the remote workforce in response to the COVID-19 pandemic, there has been a growing recognition amongst small and medium-sized enterprises of the necessity for reliable remote cyber security measures. The prolonged period of working remotely has widened the scope of possible threats, underscoring the criticality of prioritising cyber security in order to safeguard business operations and data.

To oversee network security incidents, it is imperative for businesses to deploy intrusion detection systems (IDS). This involves integrating both hardware and software components to detect and avert suspicious activities on the network. The use of IDS also furnishes protection against any possible attacks that could transpire on the network.

Data collected by IDS regarding malicious behaviour gets relayed to the IT department for subsequent scrutiny.

Could you provide an explanation of an IDS?

An IDS (Intrusion Detection System) is created to scrutinise data flow and detect any anomalous activity, triggering an alert if any dubious activity is detected. These refined systems are proficient in identifying malicious conduct, taking requisite measures to obstruct it and impede any possibly menacing Internet Protocol (IP) addresses.

Unlike an IDS, which is mainly deployed to identify cyber attacks, an Intrusion Prevention System (IPS) scrutinizes network packets to obstruct any malevolent or corrupt traffic from entering the system.

How does an IDS operate?

Intrusion Detection Systems (IDS) continuously oversee network traffic. These systems operate as an added security layer, utilizing two criteria to decide whether a packet is conceivably malicious:

  • Signs that are indicative of a well-documented cyberattack.
  • Anomalies that deviate from the norm.

The fundamental aim of an intrusion detection system (IDS) is to spot any upcoming security infiltrations by inspecting data for unusual activities. This exploration comprises dissecting the network packets to ascertain whether any of them correspond to a catalogue of well-known cyberattack signatures. Through this method, IDS can promptly and efficiently spot any plausible threats.

IDS can discern risks of this level with pattern correlation:

  • Malware is a type of threat (worms, ransomware, trojans, viruses, bots, etc.).
  • Scanning attacks entail transmitting packets to the network to explore its setup, comprising the open and shut ports, the allowed traffic varieties, and the operating programs. The objective of such an attack is to acquire information about the system, which can eventually result in probable network vulnerabilities.
  • Using asymmetrical routing, hostile packets can avoid security protections by entering and exiting from different paths.
  • Malicious executables can substitute database data through buffer overflow attacks.
  • Protocols such as ICMP, TCP, ARP, etc., developed to counteract attacks.
  • Prevent distributed denial of service attacks from triggering network downtime.

Upon detecting an unusual occurrence, the Intrusion Detection System (IDS) will indicate an alarm and produce an alert. An alarm would entail both a record in the audit log and a notification to the IT administrator. The team will then scrutinize the issue and locate the origin.

Intrusion detection system based on Python

To carry out this task, Python in combination with OpenCV and NumPy libraries will be employed.

Firstly, we will require importing the pertinent libraries. If not already installed, you can execute the pip command to install them. To accomplish this, open a command prompt, copy and paste the following command, and then hit Enter.

OpenCV-Python can be installed using the pip command.

It is advisable to use the built-in camera on your laptop or an external video source for performing this task. If you opt to use your laptop’s camera for recording, the first step is to begin the video or capture. Then, it is crucial to identify the area that requires protection.

To accomplish this task, we will be using a click event function. With this functionality, users can select a rectangle by clicking and dragging the left mouse button starting from the initial segment L1. The entire frame will be retrieved by default; however, if there are no alterations needed, users can press any key to proceed.

As shown in the following code, we are presently starting the procedure of reading video frames. In this procedure, we will examine two consecutive video frames and concentrate on the area specified in the first step.

It is improbable that an RGB image would be appropriate for this task, and its use would negatively impact the project’s efficiency. Therefore, we will transform these images to grayscale. Afterwards, we will compare the two frames to determine if there are any modifications. To detect these changes, we will make use of OpenCV’s absdiff() function.

To determine the absolute difference between two snapshots, we can utilise the absdiff() method. Once the distinction between the two frames has been established, we can separate the masked image, which will hold only white or black pixels, by applying thresholding to the image. White pixels would indicate variations between the two frames, while black areas would indicate continuity between them. In some situations, the shifts between the two frames may be so considerable or have several discontinuities, making them challenging to discern. To address these difficulties, certain image processing methods must be utilised.

To fill in the gaps between the white-masked images, we will employ Gaussian blur to smooth and dilate the image. The masked image will have a final appearance after the processing is finished, as shown below.

To precisely compute the total area of the white patches in the image, we will employ OpenCV’s detect contours functionality. Using the draw contours feature, we can identify the boundaries of each patch and provide the points as input to the contour area function, which will yield the area of each contour. Insignificant variations can be disregarded since these are likely to be caused by chance fluctuations.

We will set a threshold to identify the contours that necessitate more attention. Any contours with an area that is below the specified limit (in this scenario, 900) will be disregarded from our examination. To indicate that the intruder has been detected, we will draw a green box surrounding the contour.

If deemed necessary, we may install a doorbell to inform the owner. To accomplish this task effectively, it is advisable to include a buzzer alert mechanism. Numerous libraries, such as Win Sound and Beep, are available to aid in the process.

Classification of Intrusion in Security Systems

When implementing an Intrusion Detection System (IDS), it is essential to assess the intended usage scenario of the system. Similar to many other forms of cybersecurity, the next-generation IDS can be installed either on a single host or across a network. To select the most suitable IDS deployment solution, it is crucial to consider an organization’s security objectives and requirements.

  1. Host-Based Intrusion Detection System (HIDS)

    To enhance a device’s security and safeguard it against both external and internal threats, it is highly recommended to install a Host-Based Intrusion Detection System (IDS). This system is intended to monitor a device for any anomalous activity and generate an alert if any malevolent attempts are made to access the system.

    Intrusion Detection Systems Boast Various Features, Including:
    • Tracking all the information that enters and exits a device via a network.
    • Observing present operations.
    • Examining the server’s activity logs.

      Due to its restriction to monitoring only the on-host computer activities, a host-based Intrusion Detection System (HIDS) has limitations in terms of analysis and decision-making capabilities. However, the insights it can provide about the internal operations of the host computer are priceless.
  2. Network-based Intrusion Detection System (NIDS)

    Network-based Intrusion Detection Systems (NIDS) are security solutions that are designed to oversee complete network operations. This method enables the system to examine and evaluate the content and metadata of each data packet and utilise this information to make decisions about a network’s security.

    As a result, these systems are capable of identifying general threats but lack insights into the inner workings of the endpoints being protected.

    Deploying either a Host Intrusion Detection System (HIDS) or a Network Intrusion Detection System (NIDS) independently is inadequate for securing an organisation’s systems due to the differing levels of visibility between the two. An Integrated Threat Management System (ITMS) provides a more comprehensive level of protection by consolidating various security measures into a single, cohesive defence system.
  3. Signature-based Intrusion Detection System (SIDS)

    This system verifies all data packets against a database of previously identified malicious attacks. This repository stores information about previously detected harmful threats. This technology offers the same level of protection to computer systems as provided by antivirus software.
  4. Anomaly-based Intrusion Detection System (AIDS)

    This system conducts an extensive comparison of network traffic against predetermined parameters of acceptable values, including bandwidth, protocols, ports, among others. By employing machine learning technology, the system establishes a security policy baseline that can be utilised as a reference for evaluating network activities.

    The alert system utilises anomaly-based detection, which signals the IT department of any unusual activity, instead of signature-based detection, which relies on a limited range of signatures and traits. With this alert system, we can constantly monitor for any suspicious activities within our systems and respond immediately if necessary.

Why and When to Employ an Intrusion Detection System (IDS)

In this digital age, no network or firewall can be deemed genuinely secure. Hackers are continually developing new techniques to exploit security weaknesses and execute attacks. Ranging from destructive software to social engineering tactics, hackers can breach a system and steal information. Therefore, it is crucial to stay alert and take necessary measures to safeguard yourself and your organisation from such threats.

To ensure network security, it is vital to employ an extensive intrusion detection system (IDS) monitoring solution to detect and prevent malicious network activities. The primary objective of an IDS is to notify the relevant IT staff of any attempted attacks or breaches. Additionally, an IDS monitors all network incoming and outcoming traffic, as well as communication between computers on the same network.

Once an Intrusion Detection System (IDS) detects suspicious or malicious activity, it will send notifications to IT personnel. This alerts them to conduct a thorough investigation, enabling them to take proactive measures against potential attacks.

Benefits of Implementing an Intrusion Detection System (IDS)

Intrusion Detection Systems (IDS) have become a crucial component of a complete business security plan. IDS offer a variety of security advantages, including detecting suspicious network activity, monitoring system vulnerabilities and notifying administrators of possible risks. Moreover, IDS can assist with compliance requirements and offer additional layers of protection against malicious attacks. Though IDS cannot replace other security measures, they can be an efficient element of a comprehensive security strategy.

  • Content-based filtering of network packets.

    Firewalls can expose the ports and IP addresses exploited during a cyber attack, whereas Network Intrusion Detection Systems (NIDSs) can offer insights about the contents of each packet individually. Moreover, investigating the log files of compromised endpoint devices is an efficient technique for identifying botnets and exploitation attempts.
  • Protocol-level information analysis.

    As part of its probe into network protocol performance, a Network Intrusion Detection System (NIDS) analyses the data transmitted through Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). Given that NIDS sensors are familiar with the anticipated protocol operation, they can effortlessly identify any anomalies or inconsistencies.
  • Classification and measurement of threats.

    The primary objective of an Intrusion Detection System (IDS) is to evaluate the frequency and type of intrusion attempts. This gathered data can subsequently be used to improve current security measures or introduce new ones, as well as to detect any glitches or inaccurate configurations in network devices. Additionally, the data can be employed to assess possible risks in the future.
  • Compliance with regulations made easier.

    Intrusion Detection Systems (IDS) offer comprehensive network visibility, a crucial aspect of fulfilling security standards. Depending on your needs, logs from your IDS can serve as helpful documentation tools.
  • Increased productivity.

    Through packet data analysis, Intrusion Detection System (IDS) sensors can determine the types of software and services utilised on a network, providing an effective means of automation. Automating hardware inventory with an IDS is an efficient way to save costs by reducing the need for manual labour.

The Difficulty of Detecting an Intrusion

Due to the extended existence of intrusion detection systems (IDS), it is possible that they may face certain difficulties in the current information technology environment. In order to bypass these security systems, malicious attackers have developed numerous evasion strategies.

Below are several strategies utilized to evade intrusion detection systems (IDS).


By dividing the payload into multiple small packets, a sophisticated attack can go undetected. Although it’s difficult to trick an Intrusion Detection System (IDS) with small packets only, an attacker may be able to evade detection by employing advanced reassembly techniques.

Fragmentation is a technique employed by malicious actors to bypass Intrusion Detection Systems (IDSs) by distributing payloads in numerous parts. To accomplish this, transmissions are intermittently stopped while additional parts of the payload are transmitted, and packet fragments are randomly transmitted or rewritten. By using fragmentation, malicious actors are able to exhaust the IDS resources before the complete payload is received, resulting in undetected infiltration.


Malicious actors employ this technique to evade Intrusion Detection Systems (IDSs) by rerouting network traffic through various ports by manipulating the relevant protocols. If the IDS fails to respond to these protocol violations in the same manner as the intended host, the intrusion may go unnoticed.

Attacks utilising limited bandwidth

Cyber attackers can avoid being detected by intrusion detection systems (IDS) for prolonged periods by coordinating an attack from multiple sources at the same time. This strategy is frequently used to camouflage malicious activity, making it appear to be part of the typical noise and traffic generated by automated scanners.

Because of the absence of consistent identifying information, Intrusion Detection Systems (IDS) experience difficulty in accurately identifying malicious packets. Notwithstanding, there are still challenges that need to be tackled to guarantee the effectiveness of IDS technology. These include a comprehension of common circumvention methods, such as bypass tactics.

Initial Detection Systems (IDS) are frequently unreliable and may not provide accurate security alerts. False negatives and false positives are particularly common issues that can lead to squandered resources and a decline in the effectiveness of other security-related systems, including Security Operations Centres (SOC) and Intrusion Detection Systems (IDS). An excess of false positives can also cause security personnel to feel overwhelmed, resulting in missed opportunities to safeguard assets from possible harm.

Possible Restrictions of Intrusion Detection Systems

The prevalence of personal devices being utilized to access corporate networks puts organizations at a heightened risk of cyber attacks. This vulnerability provides cybercriminals with the opportunity to exploit and obtain access to sensitive data, which could lead to highly damaging consequences. In order to safeguard their networks and data, it is imperative that organizations adopt stringent security measures.

In order to enhance their network security, numerous businesses resort to implementing intrusion detection software as their main line of defence. Though this software can assist in providing added protection, it is crucial to recognize its potential limitations.

Source postal codes

Intrusion detection software can ascertain pertinent information by utilising the network address of an introduced IP packet. The value of the IP packet is elevated solely if the contained address is correct. Nonetheless, an IP packet can potentially contain an untrue or misleading address.

In either scenario, IT support personnel will expend time and effort pursuing false threats while being unable to prevent the network from being compromised.

Encrypted message delivery

Since intrusion detection software disregards encrypted packets, an intruder could potentially infiltrate the network using an encrypted packet, eluding detection and causing substantial damage before being discovered.

The automatic activation of encrypted data packets can be scheduled for future deployment into a network with relative ease. This process can thwart malicious software, including viruses or other types of software bugs, from being disseminated throughout the network. Intruder detection software can decrypt the packets to determine the origin of the threat, thereby facilitating appropriate countermeasures.

Analysis system

To enable intrusion detection during the analysis module’s data processing, a buffer, commonly referred to as a temporary storage area, must retain a portion of the initial data due to limitations in processing capability.

Though Information Technology personnel may be alerted to unusual activity within their network, the root cause of said activity may be indeterminate. The ability to block unauthorised network access effectively necessitates the availability of accurate data. By accessing more in-depth data, IT professionals can be better equipped to apply preventive measures that safeguard the security of their networks.

The hazards of erroneous alarms

Intrusion detection systems can potentially identify atypical activity within a network. Nevertheless, in cases where the intrusion detection software is unable to categorize such activity, it may result in a multitude of erroneous alerts. This is particularly relevant when numerous individuals are utilising the same network as it can contribute to an escalation in false positives.

IT personnel must undergo extensive instruction and training to differentiate between counterfeit alarms and credible security breaches. Unfortunately, businesses must bear the financial costs of this training in order to ensure the preparedness of their employees.

What is the role of machine learning in intrusion detection systems?

Certain facts require consideration prior to commencement:

  • Machine Learning is a domain of study that spotlights how computers can learn and progress autonomously, without the necessity for explicit programming. It encompasses both theoretical and practical elements aimed at endowing computers with the capacity to accomplish tasks that were previously the domain of humans. By employing Machine Learning, computers can be taught to react to novel data and situations, which were not pre-programmed. This renders an array of potential applications that can improve workflow efficiency and productivity.
  • To acquire knowledge, software must first amass or collect information. Subsequently, it must scrutinize the gathered data to detect patterns and regularities before finally making predictions based on the observed trends.
  • The primary objective is for computer programs to learn autonomously and adapt their behaviour accordingly without human intervention.

Machine learning algorithms can be categorised into the following groups:

  • Supervised Machine Learning Algorithms

    A machine learning algorithm with human oversight utilizes labelled examples to extrapolate from previous experiences and make predictions about future occurrences. An inferred function is created by the algorithm through analysis of the training data, which allows it to predict ultimate outcomes. When adequate data is accessible for training, the system can generate its own input targets.

    Supervised learning entails providing the machine with a fresh collection of examples to better learn from labelled data.
  • Unsupervised Machine Learning Algorithms

    In self-supervised learning, machines leverage input data to automatically learn to extract and comprehend relevant data. Also known as predictive or pretext learning, as the model predicts the result of a given input data.

    The objective is to transform unsupervised variables into supervised constructs using machine-generated labels. The incorporation of appropriate learning objectives is vital in order to make use of supervision provided by the data while addressing large quantities of unlabelled information.

    Weather prediction is a complex field and the accuracy of the resultant forecast heavily depends on the type of data used. While supervised and unsupervised learning that use pre-labelled and unlabelled datasets respectively are popular approaches, self-supervised learning is increasingly essential in reliable weather forecasting.
  • Unsupervised Learning Methods in Machine Learning

    In this type of learning, no labels or categories are employed at any phase of the data training procedure. Unsupervised learning extracts the hidden structure from data autonomously without human involvement. It is a machine learning algorithm category that derives insights from unlabelled datasets allowing it to detect information without explicit direction.

    As there is no prior information about the data at hand, sorting data into meaningful categories requires identification of similarities, pattern matching, and distinction recognition.
  • Partially Supervised Machine Learning Techniques

    Semi-supervised learning uses both unsupervised and supervised techniques to amalgamate labelled and unlabeled data in order to create a reliable model. This technique is typically employed when labelled data is scarce, usually due to a shortage of time, capital, or other resources, which makes obtaining additional data difficult. By utilizing both labelled and unlabeled data, semi-supervised learning is capable of creating more precise models than those generated only from labelled data.

What is an IPS and how does it function as an IDS/IPS?

In tandem with setting up Intrusion Prevention Systems (IPS), it is recommended to establish an Intrusion Detection System (IDS) aimed at precluding any malicious requests before they induce damage. To fully optimise IPS efficiency, it is crucial to adhere to security measures such as Web Application Firewalls and Traffic Filtering.

An intrusion prevention system (IPS) thwarts attacks by discarding malevolent packets, blocking problematic IP addresses, and notifying security personnel.

Through the analysis of a signature database, the system can detect attacks that arise from abnormal traffic and behaviour patterns.

Despite their efficacy in blocking prevalent security threats, not all Intrusion Prevention Systems (IPS) tools are equally effective. This is due to their heavy reliance on pre-programmed rules that, at times, result in false positives. These errors may lead to legitimate traffic being obstructed, thus reducing IPS effectiveness.


Though businesses can partially safeguard themselves by adopting Intrusion Detection Systems (IDSs), a comprehensive firewall set up that includes IDSs and Intrusion Prevention Systems (IPSs) is requisite for complete security. Regular updates of signature databases for IDSs and IPSs are critical for optimal efficacy.

Protecting networks requires system administrators to configure and monitor Intrusion Prevention Systems (IPS) so that they align with the specific requirements of their business. In recent years, there has been increased tendency among companies to outsource these services to third-party vendors providing managed Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Intrusion Detection and Prevention Systems (IDPS).

Python and OpenCV are two of the most extensively used programming languages and software libraries in the investigation of possible security risks. Due to their exceptional capabilities and user-friendly interface, they are ideally-suited for this type of work. Therefore, there is an escalating demand for experienced Python developers who can work on vulnerability research projects.

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