Wireless device identification from a phase noise prospective

As wireless devices become increasingly pervasive and essential, they are becoming both a target for attacks and the very weapon with which such an attack can be carried out. Wireless networks have to face new kinds of intrusion that had not been considered previously because they are linked to the open nature of wireless networks. In particular, device identity management and intrusion detection are two of the most significant challenges in any network security solution but they are paramount for any wireless local area networks (WLANs) because of the inherent non-exclusivity of the transmission medium. The physical layer of 802.11-based wireless communication does not offer security guarantee because any electromagnetic signal transmitted can be monitored, captured, and analyzed by any sufficiently motivated and equipped adversary within the 802.11 device's transmission range. What is required is a form of identification that is nonmalleable (cannot be spoofed easily). For this reason we have decided to focus on physical characteristics of the network interface card (NIC) to distinguish between different wireless users because it can provide an additional layer of security. The unique properties of the wireless medium are extremely useful to get an additional set of information that can be used to extend and enhance traditional security mechanisms. This approach is commonly referred to as radio frequency fingerprinting (RFF), i.e., determining specific characteristics (fingerprint) of a network device component. More precisely, our main goal is to prove the feasibility of exploiting phase noise in oscillators for fingerprinting design and overcome existing limitations of conventional approaches. The intuition behind our design is that the autonomous nature of oscillators among noisy physical systems makes them unique in their response to perturbations and none of the previous work has ever tried to take advantage of thi

IoT device fingerprinting with sequence-based features

Exponential growth of Internet of Things complicates the network management in terms of security and device troubleshooting due to the heterogeneity of IoT devices. In the absence of a proper device identification mechanism, network administrators are unable to limit unauthorized accesses, locate vulnerable/rogue devices or assess the security policies applicable to these devices. Hence identifying the devices connected to the network is essential as it provides important insights about the devices that enable proper application of security measures and improve the efficiency of device troubleshooting. Despite the fact that active device fingerprinting reveals in depth information about devices, passive device fingerprinting has gained focus as a consequence of the lack of cooperation of devices in active fingerprinting. We propose a passive, feature based device identification technique that extracts features from a sequence of packets during the initial startup of a device and then uses machine learning for classification. Proposed system improves the average device prediction F1-score up to 0.912 which is a 14% increase compared with the state-of-the-art technique. In addition, We have analyzed the impact of confidence threshold on device prediction accuracy when a previously unknown device is detected by the classifier. As future work we suggest a feature-based approach to detect anomalies in devices by comparing long-term device behaviors

Routing for Flying Networks using Software-Defined Networking

Nos últimos anos, os Veículos Aéreos Não Tripulados (UAVs) estão a ser usados de forma crescente em inúmeras aplicações, tanto militares como civis. A sua miniaturização e o preço reduzido abriram o caminho para o uso de enxames de UAVs, que permitem melhores resultados na realização de tarefas em relação a UAVs independentes. Contudo, para permitir a cooperação entre UAVs, devem ser asseguradas comunicações contínuas e fiáveis.Além disso, os enxames de UAVs foram identificados pela comunidade científica como meio para permitir o acesso à Internet a utilizadores terrestres em cenários como prestação de socorros e Eventos Temporários Lotados (TCEs), tirando partido da sua capacidade para transportar Pontos de Acesso (APs) Wi-Fi e células Long-Term Evolution (LTE). Soluções que dependem de uma Estação de Controlo (CS) capaz de posicionar os UAVs de acordo com as necessidades de tráfego dos utilizadores demonstraram aumentar a Qualidade de Serviço (QoS) oferecida pela rede. No entanto, estas soluções introduzem desafios importantes no que diz respeito ao encaminhamento do tráfego.Recentemente, foi proposta uma solução que tira partido do conhecimento da CS sobre o estado futuro da rede para atualizar dinamicamente as tabelas de encaminhamento de modo a que as ligações na rede voadora não sejam interrompidas, em vez de se recuperar da sua interrupção, como é o caso na maioria dos protocolos de encaminhamento existentes. Apesar de não considerar o impacto das reconfigurações na rede de acesso, como consequência da mobilidade dos APs, ou o balanceamento da carga na rede, esta abordagem é promissora e merece ser desenvolvida e implementada num sistema real.Esta dissertação tem como foco a implementação de um protocolo de encaminhamento para redes voadoras baseado em Software-Defined Networking (SDN). Especificamente, aborda os problemas de mobilidade e de balanceamento da carga na rede de uma perspetiva centralizada, garantindo simultaneamente comunicações ininterruptas e de banda-larga entre utilizadores terrestres e a Internet, permitindo assim que os UAVs se possam reposicionar e reconfigurar sem interferir com as ligações dos terminais à rede.In recent years, Unmanned Aerial Vehicles (UAVs) are being increasingly used in various applications, both military and civilian. Their miniaturisation and low cost paved the way to the usage of swarms of UAVs, which provide better results when performing tasks compared to single UAVs. However, to enable cooperation between the UAVs, always-on and reliable communications must be ensured.Moreover, swarms of UAVs are being targeted by the scientific community as a way to provide Internet access to ground users in scenarios such as disaster reliefs and Temporary Crowded Events (TCEs), taking advantage of the capability of UAVs to carry Wi-Fi Access Points (APs) or Long-Term Evolution (LTE) cells. Solutions relying on a Control Station (CS) capable of positioning the UAVs according to the users' traffic demands have been shown to improve the Quality of Service (QoS) provided by the network. However, they introduce important challenges regarding network routing.Recently, a solution was proposed to take advantage of the knowledge provided by a CS regarding how the network will change, by dynamically updating the forwarding tables before links in the flying network are disrupted, rather than recovering from link failure, as is the case in most of the existing routing protocols. Although it does not consider the impact of reconfigurations on the access network due to the mobility of the APs, it is a promising approach worthy of being improved and implemented in a real system.This dissertation focuses on implementing a routing solution for flying networks based on Software-Defined Networking (SDN). Specifically, it addresses the mobility management and network load balancing issues from a centralised perspective, while simultaneously enabling uninterruptible and broadband communications between ground users and the Internet, thus allowing UAVs to reposition and reconfigure themselves without interfering with the terminals' connections to the network

A Survey of Green Networking Research

Reduction of unnecessary energy consumption is becoming a major concern in wired networking, because of the potential economical benefits and of its expected environmental impact. These issues, usually referred to as "green networking", relate to embedding energy-awareness in the design, in the devices and in the protocols of networks. In this work, we first formulate a more precise definition of the "green" attribute. We furthermore identify a few paradigms that are the key enablers of energy-aware networking research. We then overview the current state of the art and provide a taxonomy of the relevant work, with a special focus on wired networking. At a high level, we identify four branches of green networking research that stem from different observations on the root causes of energy waste, namely (i) Adaptive Link Rate, (ii) Interface proxying, (iii) Energy-aware infrastructures and (iv) Energy-aware applications. In this work, we do not only explore specific proposals pertaining to each of the above branches, but also offer a perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate; Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications. 18 pages, 6 figures, 2 table

Technologies, Methodologies and Challenges in Network Intrusion Detection and Prevention Systems

This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). Intrusion Detection and Prevention System (IDPS) technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy