Intrusion Detection Projects

Intrusion detection system (IDS) is a mechanism to classify the abnormal users from the normal users based on their activities with the warning system. In general, all the web-based information has the threat of intrusion attack while data processing and transmission. So, the current system requires an efficient security system, this gradually increases the growth of intrusion detection systems. Although we have many security applications, IDS is essential to protect all the layers of the system.  This page is about the more innovative ideas for Intrusion Detection Projects with major network attacks!!! 

Performance Analysis of Intrusion Detection Projects

Introduction to Intrusion detection

Now, we can see how the intruders are detected from the network. At first, this system monitors the network and analyzes its traffic. If there is any abnormal actions are detected in the network while passing data then it is notified to the corresponding authority/user with the details of the illegal activities of the intruders. To the great extent, it also identifies the other malicious attacks in the network to normalize the flow of transmission without attacks. Below, we have listed out the common important approaches for detecting intrusion.

Intrusion Detection Approaches

  • Signature-based IDS
  • Anomaly-based IDS

Our researchers have years of experience in handling intrusion detection projects. These areas are highly looked forward to by the researchers in the current research. Beyond these ideas, we have more creative research notions to support you in all respects of intrusion detections.

Intrusion Detection Research Topics

  • Intrusion detection in WSN and ad-hoc networks
  • One-class classifier based anomaly multi-attacks detection
  • Network intrusion detection for secure cloud-oriented systems
  • Blockchain-based gateway selection in intrusion detection 

Process of Intrusion Detection Projects

Now, we can the process of IDS. As we know already, IDS is the primary process to normalize the data in the network by preventing attacks. In this, if the malicious activities are identified in-network observation, then it is taken to the application layer for further inspection to check whether it affects the system or not. Also, there are several tools to detect and track network anomalies for protecting user information. IDS is the passive technique to capture anomalies through the incoming network traffic. Here, we have given the IDS’s general workflow in their system design.

  • At first, it accumulates the data about the attack through the data collection module
  • Next, it examines the attacks in the application layer when the processing is completed
  • At last, if the attacks are identified as harmful to the system then it is reported to the user

For more clarity, now we are going to explain in detail. The data collection module has the responsibility to detect malicious actions in the network. For that, it collects all the possible inputs from the system and looks for the abnormal behavior in communication. Then, it detects the abnormalities at the initial stage itself.

Similarly, the analysis module has the responsibility to inspect the nature of the abnormalities that is whether it is harmful or not through several intelligent techniques. Through learning approaches (ML and DL), it examines how the data are transmitted between the devices in the IoT environment. Further, these learning techniques can forecast future attacks by learning the history and context data of the earlier systems. Further, we have given the different methods that we can use for developing intrusion detection projects. It is classified into data sources, response, analysis, infrastructure, time-based detection, and system design.

Design Choices of Intrusion Detection 

  • Analysis Approaches
    • Misuse-based IDS
    • Anomaly-based IDS
    • Signature-based IDS
    • Hybrid-based IDS
  • Response
    • Active or Passive response
  • Data Sources
    • Wireless communication
    • Host-based
    • Network-based
    • Sensor-based Warnings
    • Hybrid-based
    • Log Files Services / Applications
  • Time of Detection 
    • Off-line intrusion forecast
    • Real-time / Online Detection
  • Infrastructure / Environment 
    • Ad-hoc networks
    • Wired networks
    • Wireless networks
  • Architectural Design Type 
    • Decentralized (heterogeneous)
    • Centralized (homogeneous)

Next, we can see the different techniques for intrusion detection in the internet of things environments. In this, we mentioned several machines and deep learning techniques based on supervised and unsupervised learning classification.

Intrusion Detection Techniques for IoT

Deep Learning

  • Hybrid Approach 
    • Generative Adversarial Network (GAN)
    • EDIN
  • Unsupervised Learning 
    • Restricted Boltzmann Machine (RBM)
    • Auto-Encoder (AE)
    • Deep Belief Network (DBN)
  • Supervised Learning 
    • Recurrent Neural Networks (RNN)
    • Convolutional Neural Networks (CNN)

Machine Learning

  • Unsupervised Learning 
    • Principal Component Analysis (PCA)
    • K-Means
  • Supervised Learning 
    • SEL Algorithm
    • Naive Bayes (NB)
    • Random Forest (RF)
    • Decision Tree (DT)
    • K-Nearest Neighbour (kNN)
    • Support Vector Machine (SVM) 

Performance Analysis of Intrusion Detection Projects

Similar to other security systems, IDS maintains the log information and sends the alert to the respective users regarding the existence of the attacks. The IDS logs are comprised of the following information to assess the IDS performance.

  • Link lifespan
  • Number of sender packets
  • Receiver host port usage
  • Initial Link Duration
  • Number of receiver packets
  • Quantity of user bytes
  • Protocol or Service
  • Sender host port usage
  • Quantity of receiver bytes
  • Different quantifier and determiners (connection)

To execute the data verification processes, it involves some challenges faced by the intrusion detection system. And, they are listed below for your reference.

Research Constraints of Intrusion Detection

  • Highly flexible and scalable
  • Accurate completeness
  • High robustness
  • Data timeliness
  • Preciseness

Now from the development point of view, we can numerous datasets that are used for developing intrusion detection projects. Choosing the best dataset is also equally important to research and development. The effective dataset yields the best results in classifying the normal and anomaly activities in the network

Datasets for Intrusion Detection

    • It includes the characteristics such as port scan, DoS/DDoS, U2R, probing, and more but the flow data doesn’t include any supporting features
  • CICIDS2017
    • It includes both normal and other usual attacks in the real-world deployment based on the background traffic info
    • It comprises 25 users behavior based on SSH, HTTP, FTP, HTTPS, and more
    • It also fails to detect the ground truth of the attacks but it includes proper information on labeling
    • It is intended to overcome the shortcomings of the KDD-99
    • It follows the resampling technique to identify and classify the missed attacks in the network
  • N-BaIoT 
    • It is the current dataset preferred by many IDS systems in IoT
    • It gathers the traffic information from two networks as IoT and IP security camera networks
    • Here, the IP camera includes the attacks that disturb the video uplink in terms of readiness and reliability
    • And, IoT network includes several IoT sensors, laptops that get attacked by Mirai botnet malware
    • Also, it includes the attacks of fuzzing, video injection, SSDP flood, OS scan, SYN DoS, and many more
  • KDD99 
    • It is the most widely used dataset to detect the normal and abnormal behavior of the network
    • However it is commonly used in the initial stage by taking more effort, it produces negative and inaccurate results
    • It is originated from the DARPA98 dataset to classify the incoming normal and attack in the communication links
  • BoT-IoT 
    • It generates the testbeds to overcome the complexity of labeling, network, attacks, and many more
    • It uses learning mechanisms such as deep learning and machine learning
    • It supports file formats of CSV, argus, and pcap which classify the files based on classes and sub-classes
    • It includes DoS/DDoS, scan (port, service, and OS), keylogging, and many more
  • DEFCON-8
    • DEFCON-8 includes the attacks of buffer overflow and port scan
    • Similarly, the DEFCON-10 dataset includes the attacks of the sweep, FTP telnet, port scan, and other administrative privileges
    • It has realistic network traffic with constrained IDS assessment support because of the normal contextual traffic
    • Most probably, it used to evaluate the alert correlation methods
  • KDD CUP 99
    • It is followed by the KDD99 to overcome its accuracy problem
    • It is popularly used instead of KDD99 to yield precise results in classifying the anomalies
    • Though it is popular, it has some drawbacks similar to the KDD99 in terms of the present context like unrelated characteristics, non-stationary among datasets, pattern redundancy, etc.
    • CAIDA OC48 – include different kinds of OC48 link data
    • CAIDA DDOS – include distributed denial of service (DDoS) attack info about 1 hour
    • CAIDA Internet – include passive network traffic in ultra-fast internet service
    • However, these datasets are used to detect the anomalies along with their source, destination, payload, and protocol, they fail to analyze the ground truth of the attacks
  • IoTPoT 
    • It has constrained network traffic which is produced by honeypots
    • It doesn’t include labor-intensive anonymization and labeling
    • It inspects the Telnet-based attacks that have dissimilar CPU designs (PPC, ARM, and MIPS)
    • It includes the 39 days of malware binaries actions
  • LBNL 
    • It includes the anonymized traffic with header information
    • It is collected from the real-world inbound, routing, and outbound traffic between two edge routers but it no proper information on labeling
  • UNSW-NB15 
    • This dataset is about 100 GB in size which includes several sustainable features (pcap file format)
    • It comprises both benign, and anomalous factors such as shellcode, backdoors, fuzzer, DoS, worms, etc. 

Performance Metrics of Intrusion Detection Projects

To the end, now we can see about the performance assessment metrics used to measure how the intrusions are classified into normal and abnormal through the pre-defined models in the IDS. For that, it uses the confusion matrix which reveals the true value of the IDS in the following aspects,

  • True negative (TN)

– Capability to detect the non-intrusive action

  • True positive (TP)

Competency to detect the actual intrusions

  • False-negative (FN)

– Wrongly detect the intrusion as normal

  • False-positive (FP)

Mistakenly detect the normal traffic as intrusion

Generally speaking, if you are looking for the best research service and more creative project topics then communicate with us. We will help you to know the latest trends in intrusion detection projects.

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