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

As the remote workforce continues to grow in the wake of COVID-19, small and medium-sized enterprises are increasingly recognising the need for robust cyber security protection. The extended period of remote work has expanded the range of potential threats, making it even more important for businesses to prioritise cyber security in order to protect their data and operations.

Businesses need to implement intrusion detection systems (IDS) in order to monitor network security incidents. This is achieved by combining hardware and software components, with the aim of detecting and preventing malicious activities on the network. IDS also provides protection against potential attacks that may occur on the network.

Information gathered by IDSs about harmful behaviour is sent to the IT department for further investigation.

Can you explain what an IDS is?

An Intrusion Detection System (IDS) is designed to monitor data flow and recognise any unusual activity, raising an alarm if something suspicious is detected. These sophisticated systems are capable of detecting malicious behaviour, taking the necessary steps to prevent it, and blocking any potentially dangerous Internet Protocol (IP) addresses.

In comparison to an Intrusion Detection System (IDS), which is primarily used to detect cyber attacks, an Intrusion Prevention System (IPS) examines network packets for any malicious or damaged traffic in order to prevent such traffic from entering the system.

What is the procedure of an IDS?

Network traffic is consistently monitored by Intrusion Detection Systems (IDS). These systems act as an additional layer of security, using two criteria to determine if a packet is potentially malicious:

  • The telltale marks of a well-documented cyberattack.
  • Disruptions to the status quo.

The primary objective of an intrusion detection system (IDS) is to identify any security breaches that may occur. It does this by analysing data for any abnormal activity. This process involves examining the network packets to determine if any of them match a library of known cyberattack signatures. By using this approach, the IDS can quickly and effectively identify any potential threats.

IDS can detect threats like these using pattern correlation:

  • Malware, a kind of threat (worms, ransomware, trojans, viruses, bots, etc.).
  • Scanning attacks involve sending packets to the network in order to detect its configuration, including its open and closed ports, the types of traffic that are permitted and the programs that are running. This type of attack is used to gain knowledge about a system and can lead to potential vulnerabilities in the network.
  • By entering and leaving through separate paths, hostile packets are able to circumvent security protections via asymmetric routing.
  • Buffer overflow attacks may be used to replace database data with malicious executables.
  • Protocols like ICMP, TCP, ARP, etc., that are designed to thwart assaults.
  • Stop distributed denial of service attacks from causing network outages.

When an anomalous event is detected, the Intrusion Detection System (IDS) will raise a flag and generate an alert. Both an entry into the audit log and a prompt notification to the IT administrator will constitute an alarm. Subsequently, the team will then analyse the situation and pinpoint the source of the problem.

Python-based intrusion-detection system

Python, together with the OpenCV and NumPy libraries, will be used to complete this task.

To start off, we will need to import the relevant libraries. If you have not already done so, you can use the pip command to install them. To do this, open a command prompt, copy and paste the following command, then press Enter.

OpenCV-Python might be set up using the pip command.

It is recommended to utilise either the pre-installed camera on your laptop or an external video source for this task. If you decide to employ your laptop’s camera for filming, the initial step is to initiate the movie or capture the footage. Subsequently, it is essential to pinpoint the area that needs to be secured.

In order to complete this task, we will be implementing a click event function. This feature will allow users to make a rectangle selection by clicking and dragging the left mouse button from the beginning of segment L1. By default, we will be retrieving the entire frame; however, if the user does not wish to make any changes, they can simply press any key.

As demonstrated in the code below, we are now initiating the process of reading the video frames. In this process, we will take two consecutive video frames and focus on the area identified in the first step.

It is likely that an RGB image will not be suitable for this task, and its utilisation will have an adverse effect on the project’s efficiency. As a result, we will convert these images to grayscale. Subsequently, we will compare the two frames to ascertain whether there has been any alteration. We will utilise OpenCV’s absdiff() function to identify these changes.

The absolute difference between two snapshots can be determined using the absdiff() method. Once the difference between the two frames has been identified, the masked image, which will contain only white or black pixels, can be extracted by applying thresholding to the image. The white pixels will indicate differences between the two frames, while the black area will demonstrate the continuity between them. In some cases, the shifts between the two frames may be so drastic or contain several discontinuities, making them difficult to spot. To account for this, certain image processing methods must be employed to resolve the issue.

We will be applying Gaussian blur in order to dilate and smooth out the picture, thereby filling in the gaps between the white-masked images. After the processing is complete, our masked picture will have a finished look, with the following appearance.

We will utilise OpenCV’s detect contours feature to accurately calculate the total surface area of the white patches in the picture. Using the draw contours feature, we can pinpoint the boundaries of each patch and use the points as input to the contour area function, which will return the area of each contour. Minor deviations may be ignored, as these are likely to be the result of random fluctuations.

Consequently, we will set a boundary to identify the contours that require further consideration. Any contours with an area that is less than the predetermined limit (in this case, 900) will be excluded from our investigation. To make it clear that the intruder has been located, we will draw a green box around the contour.

If deemed appropriate, we may deploy a doorbell to notify the owner. In order to successfully complete this task, it is recommended to consider incorporating a buzzer alert mechanism. There are a variety of libraries available to assist in this, such as Win Sound and Beep.

Security system intrusion classifications

When it comes to deploying an Intrusion Detection System (IDS), it is important to consider the context in which the system will be used. As with many other forms of cyber security, the next-generation IDS can be deployed either locally on a single host or across a network. It is essential to take into account the security requirements and objectives of an organisation in order to determine the most suitable IDS deployment solution.

  1. Immune Detection System Running on a Host-Based System (HIDS)

    In order to bolster the security of a device and protect it from both external and internal threats, it is highly recommended to install a Host-Based Intrusion Detection System (IDS). This system is designed to monitor a device for any suspicious activity and provide an alert if any malicious attempts to access the system are detected.

    Identification Systems Have Many Different Features, Such as:
    • Keeping tabs on all the data going into and out of a device through a network.
    • Monitoring current activity.
    • Checking the server’s activity logs.

      A host-based Intrusion Detection System (HIDS) is limited in its scope of analysis and the decisions it can make due to the fact that it can only observe the activities on the host computer. Nevertheless, the information it can provide about the internal workings of the host computer is invaluable.
  2. Intrusion Detection System in Networks (NIDS)

    Network-based Intrusion Detection Systems (NIDS) are security solutions designed to monitor a network as a whole. This approach enables the system to analyse the content and metadata of each packet and use this information to make decisions about its security.

    Because of this, these systems may identify broad dangers, but they lack insight into the workings of the endpoints they are meant to guard.

    The deployment of either a Host Intrusion Detection System (HIDS) or a Network Intrusion Detection System (NIDS) alone is not sufficient to safeguard an organisation’s systems, due to the varying extents of visibility between the two. An Integrated Threat Management System (ITMS) offers a more comprehensive level of protection as it consolidates numerous security measures into a single, cohesive defence system.
  3. Intrusion detection using signatures (SIDS)

    This system checks all data packets against a repository of known malicious attacks, which stores information about previously identified harmful threats. This technology provides the same level of protection for computer systems as antivirus programs do.
  4. A technique for detecting intrusion based on anomalies (AIDS)

    This system performs a detailed comparison of network traffic against a pre-defined baseline of acceptable parameters such as bandwidth, protocols, ports, etc. By leveraging machine learning technology, the system establishes a security policy baseline which can be used as a reference for assessing network activities.

    Anomaly-based detection is employed instead of signature-based detection, which relies on a limited range of signatures and traits, to notify the IT department whenever something unusual is detected. This alert system enables us to continuously monitor our systems for any suspicious activity and intervene as soon as possible if necessary.

When should you use an IDS and why?

In the age of digital information, no network or firewall can truly be considered secure. Attackers are constantly developing new methods of exploiting vulnerabilities and launching attacks. From malicious software to social engineering tactics, hackers are capable of gaining access to a system and stealing data. As such, it is essential to remain vigilant and take the necessary precautions to protect yourself and your organisation from such threats.

Maintaining network security necessitates the implementation of a comprehensive intrusion detection system (IDS) monitoring solution to identify and prevent malicious network activities. The primary purpose of an IDS is to alert the relevant IT personnel of any attempted attacks or intrusions. An IDS also monitors all incoming and outgoing traffic on the network, as well as communication between computers on the same network.

When suspicious or malicious activity is detected, Intrusion Detection Systems (IDSs) will trigger notifications to IT personnel, prompting further investigation and allowing for proactive measures to be taken against potential attacks.

There are several advantages to using an IDS.

Intrusion Detection Systems (IDSs) are becoming a necessary component of any comprehensive business security plan. IDSs provide a range of security benefits, such as identifying suspicious activity on a network, monitoring system vulnerabilities and alerting administrators of any potential risks. In addition, IDSs can help with compliance requirements and provide additional layers of protection against malicious attacks. While IDSs are not a replacement for other security measures, they can be an effective part of a comprehensive security strategy.

  • Network packets may be filtered according to their content.

    Firewalls can reveal the ports and IP addresses that were used in the process of a cyber attack, while Network Intrusion Detection Systems (NIDSs) can provide details on the contents of individual packets. Additionally, examining the log files of endpoint devices that have been compromised can be a useful tool for identifying botnets and exploitation attempts.
  • They may examine information inside the framework of the protocol.

    A Network Intrusion Detection System (NIDS) examines the data transmitted via Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) as part of its investigation into network protocol performance. Since NIDS sensors are familiar with the expected operation of these protocols, they can easily detect any irregularities or discrepancies.
  • Threats may be categorised and measured with their help.

    An Intrusion Detection System (IDS) is primarily utilised to analyse the frequency and type of intrusion attempts. This collected data can then be utilised to enhance existing security measures or introduce new ones, as well as to identify any flaws or incorrect configurations in network devices. Additionally, this data can be utilised to assess potential risks in the future.
  • They simplify compliance with rules and regulations.

    Intrusion Detection Systems (IDSs) provide comprehensive visibility into your network, a critical component for meeting security standards. Depending on your requirements, logs from your IDS may be beneficial for documentation purposes.
  • As a result, they may improve productivity.

    By analysing packet data, Intrusion Detection System (IDS) sensors can identify the types of software and services being used on a network, thus providing an efficient means of automating the process. Utilising an IDS to automate hardware inventory is an effective way of saving money by reducing the need for manual labour.

How Difficult Is It To Detect An Intrusion?

Given the fact that intrusion detection systems (IDS) have been in existence for an extended period of time, it is likely that they will encounter certain challenges in the contemporary information technology environment. Malicious attackers have developed a multitude of avoidance strategies in order to bypass these security systems.

Strategies for evading intrusion detection systems (IDS) include the following.


The sophisticated attack is able to remain undetected due to its payload being divided into many small packets. It is challenging to fool an Intrusion Detection System (IDS) with only small packets, but an attacker may be able to successfully bypass detection by using complex reassembly.

Fragmentation is a technique used by malicious actors to evade Intrusion Detection Systems (IDSs) by sending payloads in multiple pieces. This is accomplished by pausing transmissions while additional parts of the payload are sent and transmitting packets at random or rewriting packet fragments. The malicious actors use fragmentation to exhaust the resources of the IDS before the entire payload is received, thereby avoiding detection.


This technique of circumventing Intrusion Detection Systems (IDSs) involves the redirection of network traffic through a variety of different ports, which is achieved by manipulating the associated protocols. If the IDS does not respond to these protocol violations in the same manner as the intended host does, then the intrusion will remain unrecognised.

Attacks using a limited amount of bandwidth

Cyber attackers have the potential to remain undetected by intrusion detection systems (IDS) for extended amounts of time by orchestrating an attack from multiple sources simultaneously. This tactic is often employed to disguise malicious activity and make it appear as if it is part of the normal, everyday traffic and noise generated by automated scanners.

Due to the lack of consistent identifying information, it is difficult for Intrusion Detection Systems (IDS) to accurately identify malicious packets. Despite this, there are still challenges that must be addressed in order to ensure the efficacy of IDS technology. These include being aware of common methods of circumventing the system, such as through bypass tactics.

Initial Detection Systems (IDS) are often unreliable and can be ineffective in providing accurate security alerts. False positives and false negatives are especially common, and can result in wasted resources and a decrease in the effectiveness of other security-related systems, such as Intrusion Detection Systems (IDS) and Security Operations Centres (SOC). Excessive false positives can also lead to security personnel feeling overwhelmed, which can result in missed chances to protect assets from potential harm.

If there are any restrictions on intrusion detection systems, what are they?

As the use of personal devices to access corporate networks becomes more commonplace, organisations are increasingly vulnerable to cyber attacks. This presents a substantial risk to companies, as cybercriminals are able to exploit this vulnerability and gain access to confidential data, resulting in potentially devastating consequences. It is essential that organisations take the necessary steps to protect their networks and data by implementing robust security measures.

As a primary form of defence, many businesses implement intrusion detection software to help protect their network security. Although this type of software can be effective in providing extra protection, it is important to be aware of its potential drawbacks.

Postal codes of origin

When an IP packet is introduced into a network, intrusion detection software can use the packet’s network address to lookup the relevant information. The value of the IP packet is increased if and only if the address it contains is accurate. However, it is possible for an IP packet to contain an incorrect or deceptive address.

In any case, the IT support staff will waste time and energy chasing ghosts and will be unable to stop the network from being breached.

Messages sent in encrypted packages

Due to the fact that intrusion detection software ignores all encrypted packets, an intruder may be able to access the network through an encrypted packet, thereby avoiding detection and causing a significant amount of damage before being noticed.

It is feasible to arrange for the automatic activation of encrypted data packets to be deployed into a network at a predetermined future date and time. This procedure can be used to prevent malicious software such as viruses or other software bugs from being disseminated throughout the network, as intruder detection software can decrypt the packets and identify the source of the threat, thus enabling the appropriate countermeasures to be taken.

Analysis subsystem

Due to the limitation of the analytical module’s capacity to process data from the source while performing intrusion detection, some of the original data must be retained in a temporary storage area, known as a buffer.

While Information Technology staff may be notified of suspicious activity within their network, they are unable to determine the cause of such activity. However, in order to effectively block illegal network access, accurate data is required. If IT experts have access to more detailed data, they can be better equipped in taking preventative measures to ensure the safety and security of their networks.

The dangers of false alarms

It is possible for intrusion detection systems to detect unusual network activity. However, if the intrusion detection software is unable to recognise this activity, it can result in an excessive number of false alarms. This is especially true in situations where multiple individuals are utilising the same network, as this can lead to a greater number of false positives.

It is essential that IT personnel receive detailed instruction and education in order to be able to identify and distinguish false alarms from genuine threats. The downside to this is that businesses must bear the financial cost of providing such training for their staff.

To what end does machine learning serve in intrusion detection systems?

Before you begin, there are a few things you need to know:

  • Machine Learning is a field of study that focuses on how computers can learn and develop independently, without the need for explicit programming. It involves both the theoretical and practical aspects of making computers perform tasks that were previously carried out by humans. By utilising Machine Learning, computers can be trained to respond to new data and circumstances in ways that were not pre-determined. This opens up a wide range of potential applications that can improve workflow efficiency and productivity.
  • In order to acquire knowledge, a software must initially gather or accumulate information, subsequently evaluate the acquired data to identify trends or regularities, and finally, make forecasts based on the observed trends.
  • The key goal is for computer programs to be able to learn independently of human contact and to modify their behaviour appropriately.

Algorithms for machine learning may be broken down into the following groups:

  • Algorithms in machine learning with human supervision

    Labelled examples enable the algorithm to draw inferences about upcoming events based on its prior experiences. By creating an inferred function from the training data, the algorithm is able to make predictions about the ultimate outcome. When supplied with a sufficient amount of data for training, the system can come up with its own input targets.

    A fresh collection of examples is given to the computer in supervised learning so that the algorithm may learn from the labelled data.
  • Machine Learning Without Supervision

    When it comes to machine learning, self-supervised learning is the process of utilising input data to enable a model to automatically learn to extract and interpret relevant information. This is commonly referred to as pretext learning or predictive learning, as the model is able to predict the outcome of a given input.

    The aim of this process is to construct supervised problems from unsupervised ones with the help of machine-generated labels. Taking advantage of the supervision provided in the data while dealing with a large amount of unlabeled information necessitates the establishment of suitable learning objectives.

    Forecasting the weather is an intricate process and the credibility of the resulting report is heavily dependent on the type of data used. Supervised learning, whereby datasets are pre-labelled, and unsupervised learning, which is based on unlabeled datasets, are both popular methods. However, self-supervised learning is becoming increasingly important in the realm of weather forecasting, as it enables the production of reliable and accurate reports.
  • Methods of machine learning without human supervision

    Here, no labels or categories are used at any stage of the data training process. Unsupervised learning is a method of discovering a function that captures an unseen structure in the data without any human intervention. It is a type of machine learning algorithm that draws insight from unlabeled datasets, allowing the algorithm to act on its own to discover information without being explicitly told what to look for.

    Due to the lack of prior information about the data, it is necessary to use pattern matching, identification of similarities, and recognition of distinctions in order to sort the data into meaningful categories.
  • Learning techniques for machines that are only partially supervised

    Semi-supervised learning combines unsupervised and supervised techniques to utilise both labelled and unlabeled data in order to generate a reliable model. This approach is often used when there is a shortage of labelled data available, due to factors such as limited time, money, or other resources, that would otherwise be required to acquire further data. By utilising a combination of labelled and unlabeled data, semi-supervised learning is able to generate models that are more accurate than those created using only labelled data.

What is an IDS/IPS and how does it work? (IPS)

In addition to installing Intrusion Prevention Systems (IPS), it is advisable to set up an Intrusion Detection System (IDS) to detect and block any malicious requests before they can cause harm. To maximise the effectiveness of an IPS, it is important to include security measures such as Web Application Firewalls and Traffic Filtering.

An intrusion prevention system (IPS) stops assaults by discarding malicious packets, banning problematic IP addresses, and alerting security staff.

After analysing a signature database, the system is able to identify attacks based on traffic and behavioural irregularities.

Despite the usefulness of Intrusion Prevention Systems (IPS) in blocking common security threats, not all IPS tools are equally effective. This is because these systems rely heavily on pre-programmed rules, which can sometimes lead to false positives. False positives may result in legitimate traffic being blocked, thus reducing the effectiveness of the IPS.


Businesses can gain some advantage from using Intrusion Detection Systems (IDSs), however, in order to achieve complete security, the combination of Firewalls, IDSs, and Intrusion Prevention Systems (IPSs) is essential. To ensure their efficacy, it is important that the signature databases of IDSs and IPSs are kept up-to-date.

In order to ensure that their networks are secure, administrators must configure and monitor intrusion prevention systems (IPS) to meet the specific needs of their business. In recent years, there has been a growing trend among companies to outsource these services to third-party providers that offer managed intrusion detection systems (IDS), intrusion prevention systems (IPS), and intrusion detection and prevention systems (IDPS).

Python and OpenCV are two of the most widely used programming languages and software libraries for the purpose of investigating potential vulnerabilities. With their immense capabilities and intuitive nature, Python and OpenCV are well-suited for this type of task. As a result, there is an increasing demand for experienced Python developers who can work on projects related to vulnerability research.

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