A Review on Hybrid Intrusion Detection System using Artificial Immune System Approaches
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paper_title: Artificial Immune Network Clustering approach for Anomaly Intrusion Detection

paper_content:

Many Intrusion Detection approaches (IDS) have been developed in the literature. Signature based approaches for anomaly detection need to be updated with the latest signatures of unknown attacks and hence being impractical. Anomaly based approaches on the other hand, suffer from high false alarms as well as low detection rates and need labeled dataset to construct the detection profile. In fact this kind of labeled dataset cannot be obtained easily. In this paper, we investigate the application of bio-inspired clustering approach, named Artificial Immune Network, for clustering attacks for intrusion detection systems. To reduce the dimension of the DARPA KDD Cup 1999 dataset, Rough Set method was applied to get the most significant features of the dataset. Then the Artificial Immune Network clustering algorithm, aiNet, has been applied on the reduced dataset. The results show that detection rate was enhanced when most significant features were used instead of the whole features. In addition, it shows that, Artificial Immune Network is robust in detecting novel attacks.
---
paper_title: Intrusion detection: a brief history and overview

paper_content:

The goal of intrusion detection is seemingly simple: to detect intrusions. However, the task is difficult, and in fact intrusion detection systems do not detect intrusions at all, they only identify evidence of intrusions, either while they are in progress or after the fact. The paper considers data collection issues, intrusion detection techniques, system effectiveness and network wide analysis.
---
paper_title: Intrusion detection: a brief history and overview

paper_content:

The goal of intrusion detection is seemingly simple: to detect intrusions. However, the task is difficult, and in fact intrusion detection systems do not detect intrusions at all, they only identify evidence of intrusions, either while they are in progress or after the fact. The paper considers data collection issues, intrusion detection techniques, system effectiveness and network wide analysis.
---
paper_title: Artificial immune systems—today and tomorrow

paper_content:

In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.
---
paper_title: Artificial immune systems—today and tomorrow

paper_content:

In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.
---
paper_title: Artificial immune systems—today and tomorrow

paper_content:

In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.
---
paper_title: A Dendritic Cell Algorithm for real-time anomaly detection

paper_content:

The analysis process of the classical Dendritic Cell Algorithm (DCA) is performed offline, which has constrained its application in the area of anomaly detection. In order to continuously detect abnormal behaviors as soon as they occur, a real-time analysis algorithm is proposed, when an antigen has been presented by sufficient dendritic cells, it will be immediately output and assessed, thus the purpose of real-time or near-to real-time analysis can be achieved. Sufficient assessments will reduce the influence of the errors, and the antigen and signal pool of temporal correlation will eliminate the mutual interference of the antigens and signals, which are far apart. The results of the experiments show that the realtime analysis algorithm proposed has the considerable detection accuracy.
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paper_title: A Survey of artificial immune applications

paper_content:

The artificial immune system (AIS) community has been vibrant and active for a number of years now, producing a prolific amount of research ranging from modeling the natural immune system, to tackling real world applications, using an equally diverse set of immune inspired algorithms. We review the current immune applications of the AIS approach, and propose a number of suggestions to the AIS community that can be undertaken to help move the area forward. Despite many successes of AIS techniques, there remain some open issues which have to be addressed in order to make the AIS a real-world problem solving technique.
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paper_title: Immune Network Based Text Clustering Algorithm

paper_content:

The principles of the immune system and Monoclonal were introduced briefly. Focused on the text expressed by the vector space model which was processed by semantic computation, an adaptive polyclonal clustering algorithm was proposed. Firstly, the calculation method was defined for the affinity between antibody and antigens and the affinity of antibodies, the genetic operation factors were designed, replacement, inverse, colonel, crossover, mutation, death, concatenate and clustering included, secondly, the process was given, and lastly, the clustering processes and analysis were done based on the text sets in a corpora. The experiments verifies that the algorithm proposed above can get the rational clustering number and have a better correct identification rate and recall rate.
---
paper_title: Artificial Immune Network Clustering approach for Anomaly Intrusion Detection

paper_content:

Many Intrusion Detection approaches (IDS) have been developed in the literature. Signature based approaches for anomaly detection need to be updated with the latest signatures of unknown attacks and hence being impractical. Anomaly based approaches on the other hand, suffer from high false alarms as well as low detection rates and need labeled dataset to construct the detection profile. In fact this kind of labeled dataset cannot be obtained easily. In this paper, we investigate the application of bio-inspired clustering approach, named Artificial Immune Network, for clustering attacks for intrusion detection systems. To reduce the dimension of the DARPA KDD Cup 1999 dataset, Rough Set method was applied to get the most significant features of the dataset. Then the Artificial Immune Network clustering algorithm, aiNet, has been applied on the reduced dataset. The results show that detection rate was enhanced when most significant features were used instead of the whole features. In addition, it shows that, Artificial Immune Network is robust in detecting novel attacks.
---

Title: A Review on Hybrid Intrusion Detection System using Artificial Immune System Approaches

Section 1: INTRODUCTION
Description 1: Introduce the concept of Intrusion Detection Systems (IDS), their types, and the need for a Hybrid Intrusion Detection System combining Host IDS and Network IDS. Also, introduce the role of Artificial Immune Systems (AIS) in enhancing IDS.

Section 2: INTRUSION DETECTION SYSTEM
Description 2: Provide a historical overview of Intrusion Detection Systems, discuss their types, functionalities, and the basic workflow and principles behind IDS.

Section 3: Host based Intrusion Detection System (HIDS)
Description 3: Discuss the Host Based Intrusion Detection System, its architecture, functionalities, and list its advantages and disadvantages.

Section 4: Network Intrusion Detection System (NIDS)
Description 4: Describe the Network-based Intrusion Detection System, its components, architecture, functionalities, and list its advantages and disadvantages.

Section 5: HYBRID INTRUSION DETECTION SYSTEM
Description 5: Define and discuss Hybrid Intrusion Detection Systems, how they combine the features of HIDS and NIDS, and their advantages over traditional IDS.

Section 6: ARTIFICIAL IMMUNE SYSTEM
Description 6: Introduce the Artificial Immune System (AIS), discuss its inspiration from the biological immune system, and describe its major techniques and algorithms such as Negative Selection Algorithm, Clonal Selection Algorithm, Immune Network Algorithm, and Dendritic Cell Algorithm.

Section 7: RELATED WORK
Description 7: Review and discuss previous related research work on Hybrid Intrusion Detection Systems and Artificial Immune Systems in the field of anomaly detection and intrusion detection.

Section 8: CONCLUSION
Description 8: Summarize the paper, highlighting the need for Hybrid IDS and the potential of AIS in enhancing the robustness and effectiveness of IDS.