Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey (1701.02145v1)

Abstract: Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of ML IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

• The paper presents a comprehensive taxonomy of intrusion detection systems by classifying techniques based on data sources and detection methods.
• The paper evaluates various machine learning and deep learning approaches, highlighting their impact on improving threat detection accuracy.
• The paper analyzes key performance metrics such as false and true positive rates to inform future research and practical IDS deployment.

An Overview of "Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey"

The paper "Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey" presents a comprehensive assessment of Intrusion Detection Systems (IDS), exploring the efficacy of both shallow and deep network approaches for detecting cyber threats. The authors, Elike Hodo et al., aim to establish a detailed taxonomy of these systems, grounded in a critical review of existing literature and ML techniques used in anomaly detection within IDS frameworks. This survey is significant in its ambition to categorize and compare various detection methodologies, illustrating the nuanced capabilities and limitations inherent in each approach.

Key Contributions

1. Taxonomy of IDS:

The paper offers an exhaustive taxonomy for classifying IDS based on the source of data and the intrusion detection techniques employed. These techniques include host-based detection, network-based detection, anomaly-based detection, and signature-based detection, providing a comprehensive categorization that enhances the understanding of the current landscape in intrusion detection.

2. Evaluation of Machine Learning Integration:

A pivotal aspect of the paper is the survey of machine learning algorithms and their application to IDS. The researchers discuss the significance of feature selection, which is crucial in enhancing the training and effectiveness of ML-based IDS. They delve into a variety of ML techniques such as Bayesian networks, genetic algorithms, support vector machines, k-nearest neighbors, decision trees, and fuzzy logic, detailing how each can be applied to intrusion detection and the results they might yield.

3. Analysis of Performance Metrics:

The paper thoroughly investigates the performance of these systems in terms of accuracy, specifically scrutinizing false positive and true positive alarm rates. This analysis allows for a comparative assessment of different IDS techniques, highlighting where certain methods may outperform others, thereby guiding future research and development efforts.

4. Deep Learning Techniques:

A notable contribution of the paper is its focus on deep learning architectures in IDS, including deep belief networks, convolutional neural networks, and recurrent neural networks. The authors suggest that these architectures, although less explored compared to traditional shallow networks, hold potential in improving detection accuracy, particularly in handling complex and layered data sets.

Implications and Future Directions

The survey's implications are multifaceted for both the practical deployment of IDS and theoretical exploration in the field. By categorizing and comparing various approaches, the paper guides practitioners in selecting the appropriate IDS technique suited to specific network environments and threat landscapes. The exploration into deep learning highlights a particular avenue for future research, suggesting that deep networks, with their robust feature extraction and classification capabilities, could redefine the landscape of IDS efficacy.

While the paper indicates significant advancements in IDS technologies, it also identifies persistent challenges, notably concerning the reliability of signature-based systems in detecting unknown or novel threats. This gap underscores the necessity for evolving IDS towards more autonomous and adaptive models, leveraging advancements in machine learning and data science.

Conclusion

The survey by Hodo et al. effectively synthesizes current knowledge on IDS, offering valuable insights into the benefits and limitations of both shallow and deep learning approaches. This work serves as a crucial resource for researchers seeking to understand the dynamics of IDS technologies and paves the way for innovative methodologies that could enhance network security in increasingly complex digital ecosystems. Future research would benefit from deeper empirical evaluations of proposed models, considering not only their theoretical foundations but also their applicability in real-world scenarios.