Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognizing attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this article we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.

Experimental Review of Neural-Based Approaches for Network Intrusion Management

Distributed Denial of Service (DDoS) attacks aims exhausting the target network with malicious traffic, which is a threat to the availability of the service. Many detection systems, specifically Intrusion Detection System (IDS) have been proposed throughout the last two decades as the Internet evolved, although users and organizations find it continuously challenging and defeated while dealing with DDoS. Though, IDS is the first point of defense for protecting critical networks against ever evolving issues of intrusive activities, however it should be up to date all the time to detect any anomalous behavior so that integrity, confidentiality and availability of the service can be preserved. But, the accuracy of new detection methods, techniques, algorithms heavily rely on the existence of well-designed datasets for training purposes and evaluation by creating the classifier model. In this work, experimentation has been carried out using major supervised classification algorithms to classify the DDoS attack accurately from the legitimate flows. Among all the classifier, tree-based classifiers and distance-based classifiers performed the best.

Evaluation of Classification algorithms for Distributed Denial of Service Attack Detection

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognising attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this paper we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.

Experimental Review of Neural-based approaches for Network Intrusion Management

The current digital world is using the internet almost everywhere. The usage of internet has been increasing, however, threats are also increasing in numbers. One such threat is DoS attack which uses reasonable service requests to gain excessive computing and network resources and results in an inability to access them by legitimate users. The DoS attack can happen at different layers of OSI model such as network, transport and application layers. The aim of this paper is to detect DoS attack effectively using Machine learning (ML) and Neural Network (NN) algorithms. The detection is specifically focused on application layer DoS attack detection rather than at transport and network DoS attack detection. The latest DoS attack dataset CIC IDS 2017 dataset is used in the experiment. The experimentation has divided the dataset into different splits and the best split is found for each algorithm i.e. RF and MLP. Results of RF and MLP are compared and it is shown that RF provides better results than MLP.

Shreekh Wankhede

Papers

DoS Attack Detection Using Machine Learning and Neural Network