A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented

A SURVEY OF IMPLEMENTATION OF OPPORTUNISTIC SPECTRUM ACCESS ATTACK WITH ITS PREVENTIVE SENSING PROTOCOLS IN COGNITIVE RADIO NETWORKS

Recently, the expansive growth of wireless services, regulated by governmental agencies assigning spectrum to licensed users, has led to a shortage of radio spectrum. Since the FCC (Federal Communications Commissions) approved unlicensed users to access the unused channels of the reserved spectrum, new research areas seeped in, to develop Cognitive Radio Networks (CRN), in order to improve spectrum efficiency and to exploit this feature by enabling secondary users to gain from the spectrum in an opportunistic manner via optimally distributed traffic demands over the spectrum, so as to reduce the risk for monetary loss, from the unused channels. However, Cognitive Radio Networks become vulnerable to various classes of threats that decrease the bandwidth and spectrum usage efficiency. Hence, this survey deals with defining and demonstrating framework of one such attack called the Primary User Emulation Attack and suggests preventive Sensing Protocols to counteract the same. It presents a scenario of the attack and its prevention using Network Simulator-2 for the attack performances and gives an outlook on the various techniques defined to curb the anomaly

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments

Defense and traceback mechanisms in opportunistic wireless networks

 In this thesis, we have identified a novel attack in OppNets, a special type of packet dropping attack where the malicious node(s) drops one or more packets (not all the packets) and then injects new fake packets instead. We name this novel attack as the Catabolism attack and propose a novel attack detection and traceback approach against this attack referred to as the Anabolism defence. As part of the Anabolism defence approach we have proposed three techniques: time-based, Merkle tree based and Hash chain based techniques for attack detection and malicious node(s) traceback. We provide mathematical models that show our novel detection and traceback mechanisms to be very effective and detailed simulation results show our defence mechanisms to achieve a very high accuracy and detection rate

Establishing trust relationships and secure channels in opportunistic networks

&nbsp;An effective system with techniques and algorithms that preserve the completeness and integrity of packets in a network and protects Opportunistic Networks from packet dropping and modification attacks has been proposed in this thesis. The techniques and attributes used to create the system involve using Merkle trees, trust, and reputation.<br /

Heterogeneous Dynamic Spectrum Access in Cognitive Radio enabled Vehicular Networks Using Network Softwarization

Dynamic spectrum access (DSA) in cognitive radio networks (CRNs) is regarded as an emerging technology to solve the spectrum scarcity problem created by static spectrum allocation. In DSA, unlicensed users access idle channels opportunistically, without creating any harmful interference to licensed users. This method will also help to incorporate billions of wireless devices for different applications such as Internet-of-Things, cyber-physical systems, smart grids, etc. Vehicular networks for intelligent transportation cyber-physical systems is emerging concept to improve transportation security and reliability. IEEE 802.11p standard comprising of 7 channels is dedicated for vehicular communications. These channels could be highly congested and may not be able to provide reliable communications in urban areas. Thus, vehicular networks are expected to utilize heterogeneous wireless channels for reliable communications. In this thesis, real-time opportunistic spectrum access in cloud based cognitive radio network (ROAR) architecture is used for energy efficiency and dynamic spectrum access in vehicular networks where geolocation of vehicles is used to find idle channels. Furthermore, a three step mechanism to detect geolocation falsification attacks is presented. Performance is evaluated using simulation results

FAPMIC: Fake Packet and Selective Packet Drops Attacks Mitigation By Merkle Hash Tree in Intermittently Connected Networks

Delay/Disruption Tolerant Networks (DTNs) are a special category of IntermittentlyConnectedNetworks (ICNs). It has features such as long-delay, frequent-disruption, asymmetrical-data-rates, and high-bundle-error-rates. DTNs have been mainly developed for planet-to-planet networks, commonly known as Inter-Planetary-Networks (IPNs). However, DTNs have shown undimmed potency in challenged communication networks, such as DakNet, ZebraNet, KioskNet and WiderNet. Due to unique characteristics (Intermittent-connectivity and long-delay) DTNs face tough/huge/several challenges in various research areas i.e bundle-forwarding, key-distribution, privacy, bundle-fragmentation, and malicious/selfish nodes particularly. Malicious/selfish nodes launch various catastrophic attacks, this includes, fake packet attacks, selective packet drops attacks, and denial-of-service/flood attacks. These attacks inevitably consume limited resources (persistent-buffer and bandwidth) in DTNs. Fake-packet and selective-packet-drops attacks are top among the challenging attacks in ICNs. The focus of this article is on critical analyses of fake-packet and selective-packet-drops attacks. The panoramic view on misbehavior nodes mitigation algorithms are analyzed, and evaluated mathematically through several parameters for detection probability/accuracy. This article presents a novel algorithm to detects/mitigates fake-packet and selective-packet-drops attacks. Trace-driven simulation results show the proposed algorithm of this article accurately (enhanced detection accuracy, reduces false-positive/false-negative rates) detects malicious nodes which launch fake-packet and selective-packet-drops attacks, unlike previously proposed algorithms which detect only one attack (fake-packet or packet-drops at a time) or detect only malicious path (do not exactly detect malicious nodes which launch attacks)

Security and Privacy in Heterogeneous Wireless and Mobile Networks: Challenges and Solutions

abstract: The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.Dissertation/ThesisPh.D. Electrical Engineering 201