An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm

Deep abstraction and weighted feature selection for Wi-Fi impersonation detection

The recent advances in mobile technologies have resulted in Internet of Things (IoT)-enabled devices becoming more pervasive and integrated into our daily lives. The security challenges that need to be overcome mainly stem from the open nature of a wireless medium, such as a Wi-Fi network. An imper- sonation attack is an attack in which an adversary is disguised as a legitimate party in a system or communications protocol. The connected devices are pervasive, generating high-dimensional data on a large scale, which complicates simultaneous detections. Feature learning, however, can circumvent the potential problems that could be caused by the large-volume nature of network data. This paper thus proposes a novel deep-feature extraction and selection (D-FES), which combines stacked feature extraction and weighted feature selection. The stacked autoencoding is capable of providing representations that are more meaningful by recon- structing the relevant information from its raw inputs. We then combine this with modified weighted feature selection inspired by an existing shallow-structured machine learner. We finally demonstrate the ability of the condensed set of features to reduce the bias of a machine learner model as well as the computational complexity. Our experimental results on a well-referenced Wi-Fi network benchmark data set, namely, the Aegean Wi-Fi Intrusion data set, prove the usefulness and the utility of the proposed D-FES by achieving a detection accuracy of 99.918% and a false alarm rate of 0.012%, which is the most accurate detection of impersonation attacks reported in the literature

Network Intrusion Detection with Two-Phased Hybrid Ensemble Learning and Automatic Feature Selection

The use of network connected devices has grown exponentially in recent years revolutionizing our daily lives. However, it has also attracted the attention of cybercriminals making the attacks targeted towards these devices increase not only in numbers but also in sophistication. To detect such attacks, a Network Intrusion Detection System (NIDS) has become a vital component in network applications. However, network devices produce large scale high-dimensional data which makes it difficult to accurately detect various known and unknown attacks. Moreover, the complex nature of network data makes the feature selection process of a NIDS a challenging task. In this study, we propose a machine learning based NIDS with Two-phased Hybrid Ensemble learning and Automatic Feature Selection. The proposed framework leverages four different machine learning classifiers to perform automatic feature selection based on their ability to detect the most significant features. The two-phased hybrid ensemble learning algorithm consists of two learning phases, with the first phase constructed using classifiers built from an adaptation of the One-vs-One framework, and the second phase constructed using classifiers built from combinations of attack classes. The proposed framework was evaluated on two well-referenced datasets for both wired and wireless applications, and the results demonstrate that the two-phased ensemble learning framework combined with the automatic feature selection engine has superior attack detection capability compared to other similar studies found in the literature

An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm

Towards Effective Wireless Intrusion Detection using AWID Dataset

In the field of network security, intrusion detection system plays a vital role in the procedure of applying machine learning (ML) techniques with the dataset. This study is an IDS related in machine, developed the literature by utilizing AWID dataset. There tends to be a need in balancing a dataset and its existing approaches from the analysis of its respective works. A taxonomy of balancing technique was introduced due to the lack of treatment of imbalance. This attempt has provided a proper structure defined on all levels and a hierarchical group was formed with the collected papers. This describes a comparative study on the proposed or treated aspects. The main aspect from the surveyed papers were found that: understanding of the existing taxonomies were not in detail and there were no treatment of imbalance for the utilized dataset. So, this study concludes a gathered information in these aspects. Regardless, there are factors or weakness have been seen in any adaptations of the intrusion detection system. In this context, there are few findings that are multifold with contributions. Thus, to best of our knowledge, the study provides an integration with the observation of threshold limit and feature drop selection method by random samples. Thus, the work contributes a better understanding towards imbalanced techniques from the literature surveyed. Hence, this research would benefit for the development of IDS using ML

DEMISe: interpretable deep extraction and mutual information selection techniques for IoT intrusion detection

Recent studies have proposed that traditional security technology – involving pattern-matching algorithms that check predefined pattern sets of intrusion signatures – should be replaced with sophisticated adaptive approaches that combine machine learning and behavioural analytics. However, machine learning is performance driven, and the high computational cost is incompatible with the limited computing power, memory capacity and energy resources of portable IoT-enabled devices. The convoluted nature of deep-structured machine learning means that such models also lack transparency and interpretability. The knowledge obtained by interpretable learners is critical in security software design. We therefore propose two novel models featuring a common Deep Extraction and Mutual Information Selection (DEMISe) element which extracts features using a deep-structured stacked autoencoder, prior to feature selection based on the amount of mutual information (MI) shared between each feature and the class label. An entropy-based tree wrapper is used to optimise the feature subsets identified by the DEMISe element, yielding the DEMISe with Tree Evaluation and Regression Detection (DETEReD) model. This affords ‘white box’ insight, and achieves a time to build of 603 seconds, a 99.07% detection rate, and 98.04% model accuracy. When tested against AWID, the best-referenced intrusion detection dataset, the new models achieved a test error comparable to or better than state-of-the-art machine-learning models, with a lower computational cost and higher levels of transparency and interpretability