We propose PiNcH, a methodology to detect the presence of a drone and its current status leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH does not require either any special hardware or to transmit any signal. Indeed, it is fully passive and it resorts to cheap and general purpose hardware. To evaluate the effectiveness of our solution, we collected real communication measurements from the 3DR SOLO drone, being the most popular open-source hardware, running the widespread ArduCopter open-source firmware, mounted on-board on a wide range of commercial amateur drones. Then, we test our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movement of the drone; and, finally, (iv) estimate a lower bound on the time required to identify a drone with the requested level of assurance. The quality and viability of our solution do prove that network traffic analysis can be successfully adopted for drone identification and pave the way for future research in the area.
In @cite_17 , authors demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK).
@cite_17
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Drones cooperate with each other by transmitting and receiving packets. Therefore, it is important to conjecture the packet transmission rates within the network. However, the conventional methods are not suitable to describe the transmission patterns with satisfactory computing speed and accuracy. In this paper, we demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK). We found out SVM-QK can precisely predict the communication between drones. The Support Vector Machine (SVM) of Vapnik (1998) has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions in terms of a linear combination of kernel functions centred on a subset of the training data, known as support vectors. Despite its widespread success, the SVM suffers from some important limitations, one of the most significant being that it makes point predictions rather than generating predictive distributions. Recently Tipping (1999) has formulated the Relevance Vector Machine (RVM), a probabilistic model whose functional form is equivalent to the SVM. It achieves comparable recognition accuracy to the SVM, yet provides a full predictive distribution, and also requires substantially fewer kernel functions. The original treatment of the RVM relied on the use of type II maximum likelihood (the evidence framework') to provide point estimates of the hyperparameters which govern model sparsity. In this paper we show how the RVM can be formulated and solved within a completely Bayesian paradigm through the use of variational inference, thereby giving a posterior distribution over both parameters and hyperparameters. We demonstrate the practicality and performance of the variational RVM using both synthetic and real world examples. My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently. Communication networks such as wireless sensor networks, Internet of Things and vehicular ad-hoc networks are becoming more complex and increasing in size. This leads to high overhead (network and computation) and difficulty in determining the accurate network topology, which is an important information for traffic engineering and network management. Localization of link failures in such networks is a challenging problem and requires a novel approach to achieve the goal without any prior information about the network topology. In this paper, we present a traffic engineering (TE)-based machine learning approach to detect and localize link failures. Instead of using topology information and actively injecting additional packets to localize a failed link, the proposed machine learning model adopts a passive mechanism to learn the network traffic behavior from propagation delay, number of flows and average packet loss at every node in the network under normal working conditions and failure scenarios. We train the learning model with machine learning algorithms such as naive Bayes, logistic regression, support vector machine, multi-layer perceptron, decision tree and random forest. We implement the proposed approach and carry out extensive experiments using the Mininet platform. The performance study shows that our proposed approach localizes link failures with at least 90 accuracy using random forest algorithm while requiring less time-to-localization of a link failure compared to other existing works.