Book review: Applied cryptography: Protocols, algorithms, and source code in C

In this work, we take on the role of a wireless adversary and investigate one of its most powerful tools---radio frequency jamming. Although different jammer designs are discussed in the literature, reactive jamming, i.e., targeting only packets that are already on the air, is generally recognized as a stepping stone in implementing optimal jamming strategies. The reason is that, while destroying only selected packets, the adversary minimizes its risk of being detected. One might hope for reactive jamming to be too challenging or uneconomical for an attacker to conceive and implement due to its strict real-time requirements. Yet, in this work we disillusion from such hopes as we demonstrate that flexible and reliable software-defined reactive jamming is feasible by designing and implementing a reactive jammer against IEEE 802.15.4 networks. First, we identify the causes of loss at the physical layer of 802.15.4 and show how to achieve the best performance for reactive jamming. Then, we apply these insights to our USRP2-based reactive jamming prototype, enabling a classification of transmissions in real-time, and reliable and selective jamming. The prototype achieves a reaction time in the order of microseconds, a high precision (such as targeting individual symbols), and a 97.6% jamming rate in realistic indoor scenarios for a single reactive jammer, and over 99.9% for two concurrent jammers.

Short paper: reactive jamming in wireless networks: how realistic is the threat?

Securing internet of medical things systems: Limitations, issues and recommendations

We study cyber security issues in networked control of a linear dynamical system. Specifically, the dynamical system and the controller are assumed to be connected through a communication channel that face malicious attacks as well as random packet losses due to unreliability of transmissions. We provide a probabilistic characterization for the link failures which allows us to study combined effects of malicious and random packet losses. We first investigate almost sure stabilization under an event-triggered control law, where we utilize Lyapunov-like functions to characterize the triggering times at which the plant and the controller attempt to exchange state and control data over the network. We then provide a look at the networked control problem from the attacker's perspective and explore malicious attacks that cause instability. Finally, we demonstrate the efficacy of our results with numerical examples.

Networked Control Under Random and Malicious Packet Losses

This paper presents a comprehensive survey on anti-drone systems. After drones were released for non-military usages, drone incidents in the unarmed population are gradually increasing. However, it is unaffordable to construct a military grade anti-drone system for every private or public facility due to installation and operation costs, and regulatory restrictions. We focus on analyzing anti-drone system that does not use military weapons, investigating a wide range of anti-drone technologies, and deriving proper system models for reliable drone defense. We categorized anti-drone technologies into detection, identification, and neutralization, and reviewed numerous studies on each. Then, we propose a hypothetical anti-drone system that presents the guidelines for adaptable and effective drone defense operations. Further, we discuss drone-side safety and security schemes that could nullify current anti-drone methods, and propose future solutions to resolve these challenges.

Survey on Anti-Drone Systems: Components, Designs, and Challenges

We address the problem of selective jamming attacks in wireless networks. In these attacks, the adversary selectively targets specific packets of "high" importance by exploiting his knowledge on the implementation details of network protocols at various layers of the protocol stack. We illustrate the impact of selective jamming on the network performance by illustrating various selective attacks against the TCP protocol. We show that such attacks can be launched by performing real-time packet classification at the physical layer. We examine the combination of cryptographic primitives with physical layer attributes for preventing real- time packet classification and neutralizing the inside knowledge of the attacker.

/pdf/selective-jamming-attacks-in-wireless-networks-25f49nbjve.pdf

Selective Jamming Attacks in Wireless Networks

We address the problem of preventing the inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50 percent, and the end-to-end delay by more than 30 percent. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality.

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

We address the problem of preserving contextual information privacy in wireless sensor networks (WSNs). We consider an adversarial network of colluding eavesdroppers that are placed at unknown locations. Eavesdroppers use communication attributes of interest such as packet sizes, inter-packet timings, and unencrypted headers to infer contextual information, including the time and location of events reported by sensors, the sink's position, and the event type. We propose a traffic normalization technique that employs a minimum backbone set of sensors to decorrelate the observable traffic patterns from the real ones. Compared to previous works, our method significantly reduces the communication overhead for normalizing traffic patterns.

/pdf/perfect-contextual-information-privacy-in-wsns-30dnnfnzx6.pdf

Perfect contextual information privacy in WSNs undercolluding eavesdroppers

We address the problem of preserving the confidentiality of contextual information in wireless sensor networks (WSNs). Such information includes the time and location of events observed by the WSN, the position of the sink, and possible routes to the sink. Contextual information can be extracted via traffic analysis, even when all traffic is encrypted. We consider a global threat model in which the adversary is assumed to be capable of eavesdropping on all communications. Compared to previous works, our method significantly reduces the communication overhead for hiding contextual information. In our approach, we first reduce the number of bogus traffic sources necessary for hiding traffic patterns by finding minimum connected dominating sets that cover the deployment area. We then randomize the traffic distributions observed by eavesdropping nodes.

http://www2.engr.arizona.edu/~llazos/papers/PROANO_DSPAN12.pdf

Hiding contextual information in WSNs

The open nature of the wireless medium leaves it vulnerable to intentional interference attacks, typically referred to as jamming. This intentional interference with wireless transmissions can be used as a launchpad for mounting Denial-of-Service attacks on wireless networks. Typically, jamming has been addressed under an external threat model. However, adversaries with internal knowledge of protocol specifications and network secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this work, we address the problem of selective jamming attacks in wireless networks. In these attacks, the adversary is active only for a short period of time, selectively targeting messages of high importance. We illustrate the advantages of selective jamming in terms of network performance degradation and adversary effort by presenting two case studies; a selective attack on TCP and one on routing. We show that selective jamming attacks can be launched by performing real-time packet classification at the physical layer. To mitigate these attacks, we develop three schemes that prevent real-time packet classification by combining cryptographic primitives with physical-layer attributes. We analyze the security of our methods and evaluate their computational and communication overhead.

/pdf/packet-hiding-methods-for-preventing-selective-jamming-2o9uzbvr7x.pdf

Packet-Hiding Methods for Preventing Selective Jamming Attacks

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