In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.

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Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.

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A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

The Internet of Things (IoT) is the next technological leap that will introduce significant improvements to various aspects of the human environment, such as health, commerce, and transport. However, despite the fact that it may bring beneficial economic and social changes, the security and the privacy protection of objects and users remain a crucial challenge that has to be addressed. Specifically, now the security measures have to monitor and control the actions both of users and objects. However, the interconnected and independent nature of objects, as well as their constrained capabilities regarding the computing resources make impossible the applicability of the conventional security mechanisms. Moreover, the heterogeneity of various technologies which the IoT combines increases the complexity of the security processes, since each technology is characterized by different vulnerabilities. Furthermore, the tremendous amounts of data which is generated by the multiple interactions between the users and objects or among the objects make harder their management and the functionality of the access control systems. In this context, this paper intends to provide a comprehensive security analysis of the IoT, by examining and assessing the potential threats and countermeasures. More detailed, after studying and determining the security requirements in the context of the IoT, we implement a qualitative and quantitative risk analysis, investigating the security threats per layer. Subsequently, based on this process we identify the suitable countermeasures and their limitations, paying special attention to the IoT protocols. Finally, we provide research directions for future work.

Securing the Internet of Things: Challenges, threats and solutions

Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.

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BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.

Survey on SDN based network intrusion detection system using machine learning approaches

In the last few years, new two-dimensional (2-D) materials garnered the scientific community's attention with the growing demand for functionalized materials for advanced electrical and biomedical applications. Among all the...

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Synthesis, Modification, and Application of Black Phosphorus, Few-Layer Black Phosphorus (FLBP), and Phosphorene: A Detailed Review

The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving efforts assist in learning how to optimise future optimisation instances. In recent years, learning to optimise approaches have shown success in solving TSP problems. However, they focus on one type of TSP problem, namely ones where the points are uniformly distributed in Euclidean spaces and have issues in generalising to other embedding spaces, e.g., spherical distance spaces, and to TSP instances where the points are distributed in a non-uniform manner. An aim of learning to optimise is to train once and solve across a broad spectrum of (TSP) problems. Although supervised learning approaches have shown to achieve more optimal solutions than unsupervised approaches, they do require the generation of training data and running a solver to obtain solutions to learn from, which can be time-consuming and difficult to find reasonable solutions for harder TSP instances. Hence this paper introduces a new learning-based approach to solve a variety of different and common TSP problems that are trained on easier instances which are faster to train and are easier to obtain better solutions. We name this approach the non-Euclidean TSP network (NETSP-Net). The approach is evaluated on various TSP instances using the benchmark TSPLIB dataset and popular instance generator used in the literature. We performed extensive experiments that indicate our approach generalises across many types of instances and scales to instances that are larger than what was used during training.

Learning to Optimise General TSP Instances.

Introduction: Urinary tract infections (UTIs) are the most common bacterial infections in women of all ages. The emergence of resistance to newer classes of antibiotics, including third generation cephalosporins, has limited the drug choices for treatment of UTI. We report here a recurrent UTI due to co‐infection with multiple‐antibiotic‐resistant (MAR) Escherichia coli pathotypes. Case presentation: A 73‐year‐old woman with diabetes mellitus type 2 presented with fever, nausea, vomiting, burning sensation, painful and frequent urination. She was diagnosed with recurrent UTI (RUTI) due to co‐infection with pansensitive enteroaggregative E. coli (EAEC) and MAR extended spectrum β‐lactamase (ESBL)‐producing uropathogenic E. coli (UPEC) and treated with azithromycin and levofloxacin (each for 10 days). Unfortunately, she did not respond. Plasmid profile analysis showed that the MAR E. coli strain harboured multiple plasmids including ∼38 and ∼80 MDa plasmids. PCR for detection of β‐lactamase genes showed that the isolate was negative for blaTEM, blaSHV, blaOXA and blaCTX‐M. To the best of our knowledge, this is the first report of co‐infection with an EAEC and MAR ESBL‐producer UPEC in Bangladesh. Conclusion: We recommend that all pathotypes of E. coli as well as the other more common uropathogens should be considered in the diagnosis of RUTI and multiple antibiotics should be prescribed only in severe conditions.

Recurrent urinary tract infection due to co‐infection with extended spectrum β‐lactamase‐producer uropathogenic Escherichia coli and enteroaggregative E. coli

https://doi.org/10.1016/j.psj.2022.101904

Nasrin Sultana

Papers

Survey on SDN based network intrusion detection system using machine learning approaches

Synthesis, Modification, and Application of Black Phosphorus, Few-Layer Black Phosphorus (FLBP), and Phosphorene: A Detailed Review

Learning to Optimise General TSP Instances.

Recurrent urinary tract infection due to co‐infection with extended spectrum β‐lactamase‐producer uropathogenic Escherichia coli and enteroaggregative E. coli

Role of steroid growth promoter on growth performance and meat quality traits in broiler