Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Khan, Muhammad Adnan; Ghazal, Taher M.; Lee, Sang‐Woong; Rehman, Abdur

doi:10.32604/cmc.2022.020173

Cited by 69 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DRIFE configuration maintains the reliability of the data after processing, and CFS configuration determines the storage and usefulness of the processed Big Data. In the Saltus framework, the two important factors that decide its design are the users and the feature extraction algorithm [16] [17]. Let us consider the case: The District Law and Enforcement department faces a significant challenge in efficiently utilizing deep learning-based face recognition models for criminal suspect identification through CCTV footage across the province.…”

Section: Framework Of Saltus For Handling Big Datamentioning

confidence: 99%

Saltus - “A Sudden Transition” Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks

Pillai,

Sha

et al. 2024

Preprint

View full text Add to dashboard Cite

In the realm of sensor networks, the substantial rise in multimedia data production, covering audio, video, and acoustic measurements, has expanded the scale of big data. Multimedia Sensor Networks (MSN) excel in managing diverse sensor outputs, representations, and encoding across domains. Existing models for event detection in sensor networks fall short in handling the sheer volume and speed of these measurements from a Big Data perspective. This research work introduces “Saltus,” a model that aligns multimedia data from sensor networks to a standardized feature space. Saltus employs a machine learning-centric architecture to enhance data analysis possibilities. Crucially, the model integrates federated learning to address the evolving landscape of sensor networks. This approach optimizes the collaborative learning capabilities by allowing distributed nodes to train machine learning models locally, preserving data privacy. Saltus emerges as a solution that not only streamlines multimedia data processing but also establishes a more secure and privacy-preserving analytics framework in large-scale sensor networks. The model signifies a step forward in integrating multimedia data into an easily analyzable format, leveraging the advantages of federated learning in big data analytics.

show abstract

Section: Framework Of Saltus For Handling Big Datamentioning

confidence: 99%

Saltus - “A Sudden Transition” Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks

Pillai,

Sha

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It contains wireless network traffic data, including regular activities and various attacks like DoS and probing attacks. [55] Typically, these datasets are labeled to indicate if each instance represents normal traffic or a specific attack type. Researchers capitalize on these datasets to train and evaluate their AI-based IDS models.…”

Section: Datasetmentioning

confidence: 99%

A review of Artificial Intelligence Algorithms (Machine Learning Algorithm) for Intrusion Detection in Software-Defined Networking

Heymann,

Ahirhima

2024

Preprint

View full text Add to dashboard Cite

Demands for flexible and seamless system management necessitated the growth of software-defined networks (SDN). Yet, securing these environments with effective measures is critical as SDN continues to evolve into more intricate architectures. Intrusion detection is paramount among these measures; thus far, studies suggest that artificial intelligence (AI) approaches may be helpful in this domain. By systematically examining relevant works addressing AI-based intrusion prevention strategies within hyper-evolved SDN settings, our review aims to present an inclusive evaluation alongside suggesting areas requiring additional scrutiny. This research introduces readers to key concepts related to SDN and how deep learning algorithms, machine learning algorithms, and neural networks can be applied for effective intrusion detection within an SDN environment. Drawing from existing literature on this subject matter, our analysis critically examines the benefits and drawbacks of these AI-based techniques while highlighting gaps in knowledge requiring further research attention. Some areas include real-time protection capabilities, scalability concerns, and seamless integration with different security mechanisms. We then present future research directions in this area. This literature review employs a systematic approach to elucidate the current research on using AI methods to detect intrusions in SDN.

show abstract

“…In Reference 27, Real‐Time Sequential Deep Extreme Learning Machine (RTSDELM) methodology was proposed for wireless sensor networks for intrusion activity detection using data fusion. Then, the proposed methodology was tested on KDDCUP99 and NSLKDD datasets.…”

Section: Introductionmentioning

confidence: 99%

AWC‐NIDS: Attack‐wise customized network intrusion detection system using machine learning, concurrency, and distributed systems

Guney¹

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryWith digitization and modern network applications, information security has gained a tremendous importance. Therefore, accurate and efficient detection systems are crucial for maintaining proactive security in computer networks. Machine learning (ML) has shown great potential as a promising solution since it can teach a machine to distinguish malicious and normal network activities. However, recently proposed methods are suffering from at least one of the following: detection accuracy, false alarm rate, and computational complexity issues. The main reason behind this problem is the complexity of the model in terms of attack types. From the ML perspective, intrusion detection is a classification problem where each attack type is identified by a set of different features, and features are used for classifying network activities. Thus, training an ML algorithm to detect more than one attack type leads to a more complex model; the increasing number of used features contributes positively to the model complexity, and may result in relatively lower detection accuracy or a higher false positive rate. To tackle this problem, this study proposes an attack‐wise customized network intrusion detection system (AWC‐NIDS) based on ML, concurrency, and distributed systems to achieve accurate and efficient network‐wide intrusion detection. Since CICIDS2017 contains many modern attacks, it was used for model development and performance evaluation. The experimental results showed that the proposed methodology achieved high classification performance for all datasets with a small number of features. However, it was observed that the lowest accuracy was achieved for the comprehensive dataset (which contains all attack types); for the single attack‐type datasets, the obtained accuracy was above 99%. This finding proves the concept of attack‐wise customization for intrusion detection and shows the significance of the proposed methodology. In conclusion, this framework is promising for implementing robust and accurate cybersecurity systems for traditional and modern networking.

show abstract

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

Cited by 69 publications

References 47 publications

Saltus - “A Sudden Transition” Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks

Saltus - “A Sudden Transition” Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks

A review of Artificial Intelligence Algorithms (Machine Learning Algorithm) for Intrusion Detection in Software-Defined Networking

AWC‐NIDS: Attack‐wise customized network intrusion detection system using machine learning, concurrency, and distributed systems

Contact Info

Product

Resources

About