Host-based Intrusion Detection System Research Topics

Host-based Intrusion Detection System Research Topics is one of the detection techniques to find and investigate any threats in the system. It is one of the IDS techniques on the basis of the host to detect each for malicious actions. It is widely utilized to secure the data from threats. In this we provide the details or information that are relevant to the proposed HIDS technology.

1. Define Host-based Intrusion Detection System (HIDS)

In the beginning of the research we start with the definition for this proposed HIDS technique. It is a protection tool which monitors separate hosts for malicious activities such as network connections and file changes. It assists in event examination by offering forensic data for examining. It contrasts these against known dangers and generates alarms if any are identified, improving the host protection.

2. What is Host-based Intrusion Detection System (HIDS)?

Next to the definition we look for the comprehensive explanation for HIDS technique. It examines and monitors the actions of separate computers for signs of unwanted access, protection breaches or suspicious actions. It offers brief monitoring and alarming but is resource-intensive and only secures the particular host it is updated on. HIDS concentrates on system integrity, process behavior and log files on the host machine.

3. Where Host-based Intrusion Detection System (HIDS) used?

Afterwards the comprehensive explanation we interpret where to use this proposed HIDS technique. It is utilized in cloud environments, enterprise networks and data centers to handle and secure individual hosts from security threats. It assists to improve the overall network protection by offering identification capacities in the host level and actual-time monitoring. It plays an essential role in identifying malicious access, malware infections and the other suspicious actions on workstations, servers and other endpoints

4. Why Host-based Intrusion Detection System (HIDS) technology proposed? , previous technology issues

Here for this research we proposed the HIDS technology to tackle the issues in the existing technologies like depend individually on perimeter defenses such as antivirus and firewalls software. This technique is frequently unsuccessful in identifying internal attacks or threats that target particular hosts. HIDS provides granular visibility into host actions, enabling for early identification of intrusions or suspicious behavior. Moreover it adds network-based identification systems by offering understandings into host-level incidents. This entire technique improves the whole protection condition by closing gaps remained by traditional perimeter defenses.

5. Algorithms / protocols

In this research we proposed the HIDS technology to solve the issues in the existing technologies. The methods that we utilized for this research are ZeroX-AdaBoost, Z-Score Standardization and GA-RF-PCA.

6. Comparative study / Analysis

We have to compare the methods or techniques in this research to obtain the accurate possible outcome when compared to the existing research. Some of the methods that we compared are:

• At first we employ the Z-Score Standardization technique for preprocessing the gathered data.
• For choosing the related structures, to generate a best model for intrusion detection, we use a novel method GA-RF-PCA (“Genetic Algorithm, Random Forest and Principal Component Analysis”).
• We proposed a novel technique namely ZeroX-AdaBoost. In this the Zero-X is a security model that is proposed to find both N-day and 0-day threats. Then the AdaBoost method is utilized for classification techniques.

7. Simulation results / Parameters

Our proposed research is compared with the different parameters or performance metrics to obtain the best findings for this research. The metrics that we compared are False Alarm Rate, F1-score, Accuracy and Precision with the No of Epochs.

8. Dataset LINKS / Important URL

Below we provide some important links that are very useful to know the details about this proposed HIDS Technology. The link offers some information about Machine Vision Technology that is helpful to notice the details of this technology:

• https://www.sciencedirect.com/science/article/pii/S0031320302000262
• https://ieeexplore.ieee.org/abstract/document/5453694/
• https://www.academia.edu/download/51052237/IJCNIS-V6-N8-6.pdf

9. Host-based Intrusion Detection System (HIDS) Applications

Now we see the applications that to be used this HIDS technology will contain critical endpoints, monitoring sensors and work stations for malicious actions. It intends to detect intrusions, unwanted access attempts and malware infections, thereby supporting the whole network defenses. It is used in different environments such as data centers, enterprise networks, and cloud infrastructure to improve the protection at the host level.

10. Topology for Host-based Intrusion Detection System (HIDS)

The topology that generally contains arranging the software agents on separate hosts or endpoints over the network. This centralized technique allows extensive management and monitoring of protection along the whole network framework. These agents monitor the host activities and then report back to a localized management system for examining and alarming.

11. Environment for Host-based Intrusion Detection System (HIDS)

Let’s see the environment to be used in this proposed HIDS technology. It is arranged in different environments such as cloud infrastructure, enterprise networks and data centers to monitor the separate hosts for security threats. It offers granular visibility into the host actions and protects against internal threats, improving the whole network security.

12. Simulation tools

The software requirements that needed for this research are as follows. The developmental tool that is used for this proposed research is NS 3.26 with Python. Then the operating system here we utilized to implement this research is Ubuntu 16.04 LTS.

13. Results

In this the proposed research is compared with several methods and then the performance metrics or the parameters are compared to obtain the best findings. The HIDS technique is proposed to overcome the issues in the existing technologies. This can be implemented by utilizing the operating system Ubuntu 16.04 LTS.

Host-based Intrusion Detection System Research Ideas:

The following are the research topics that are based on HIDS technology. These topics give some information about HIDS like concepts, uses, application and some other details related to our proposed research.

1. Application of Sequence Embedding in Host-based Intrusion Detection System
2. Dual Mode Host-Based and Cloud-Based Smartphone Intrusion Detection System
3. Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things
4. A Systematic Literature Review on Host-Based Intrusion Detection Systems
5. A Novel Host Based Intrusion Detection System using Supervised Learning by Comparing SVM over Random Forest
6. Host-Based Intrusion Detection Model Using Siamese Network
7. DAHID: Domain Adaptive Host-based Intrusion Detection
8. Closing the Security Gaps in SOME/IP Through Implementation of a Host-Based Intrusion Detection System
9. Host-Based Intrusion Detection System for IoT using Convolutional Neural Networks
10. Dataset Generation Framework for Evaluation of IoT Linux Host–Based Intrusion Detection Systems
11. Generating Host-Based Data from Network Traces for Intrusion Detection
12. A clustered learning framework for host based intrusion detection in container environment
13. NLP methods in host-based intrusion detection systems: A systematic review and future directions
14. A tfidfvectorizer and singular value decomposition based host intrusion detection system framework for detecting anomalous system processes
15. Host-based intrusion detection with multi-datasource and deep learning
16. Syscall-BSEM: Behavioral semantics enhancement method of system call sequence for high accurate and robust host intrusion detection
17. Host-based IDS: A review and open issues of an anomaly detection system in IoT
18. A Host Intrusion Detection System architecture for embedded industrial devices
19. DL-HIDS: deep learning-based host intrusion detection system using system calls-to-image for containerized cloud environment
20. An Evolutionary Computation-Based Federated Learning for Host Intrusion Detection in Real-Time Traffic Analysis
21. Evaluating Word Embedding Feature Extraction Techniques for Host-Based Intrusion Detection Systems
22. HIDSC2: Host-Based Intrusion Detection System in Cloud Computing
23. Host-Based Intrusion Detection: A Behavioral Approach Using Graph Model
24. Improving Host-Based Intrusion Detection Using Thread Information
25. Stacking ensemble-based HIDS framework for detecting anomalous system processes in Windows based operating systems using multiple word embedding

26 SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks

27. Developing Cross-Domain Host-Based Intrusion Detection
28. APT attack response system through AM-HIDS
29. Implementation of Honeypot, NIDs, and HIDs Technologies in SOC Environment
30. Survey of Machine Learning HIDS Techniques
31. Grasp the Key: Towards Fast and Accurate Host-Based Intrusion Detection in Data Centers
32. BR-HIDF: An Anti-Sparsity and Effective Host Intrusion Detection Framework Based on Multi-Granularity Feature Extraction
33. Collaborative Feature Maps of Networks and Hosts for AI-driven Intrusion Detection
34. A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets
35. Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks
36. Intrusion detection systems using long short-term memory (LSTM)
37. IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism
38. A New Ensemble-Based Intrusion Detection System for Internet of Things
39. Graph-based Intrusion Detection System Using General Behavior Learning
40. Intrusion Detection Systems Fundamentals
41. Performance Analysis of Network Intrusion Detection Systems using J48 and Naive Bayes Algorithms
42. A Review of Rule Learning-Based Intrusion Detection Systems and Their Prospects in Smart Grids
43. Hybrid optimization and deep learning based intrusion detection system
44. Research Trends in Network-Based Intrusion Detection Systems: A Review
45. WINDS: A Wavelet-Based Intrusion Detection System for Controller Area Network (CAN)
46. Machine Learning-based Intrusion Detection System using Wireless Sensor Networks
47. Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research
48. An Analysis of Signature-Based Components in Hybrid Intrusion Detection Systems
49. Analytical Termination of Malicious Nodes (ATOM): An Intrusion Detection System for Detecting Black Hole attack in Mobile Ad Hoc Networks
50. Graph Neural Networks for Intrusion Detection: A Survey