1,130 research outputs found
Resilient networking in wireless sensor networks
This report deals with security in wireless sensor networks (WSNs),
especially in network layer. Multiple secure routing protocols have been
proposed in the literature. However, they often use the cryptography to secure
routing functionalities. The cryptography alone is not enough to defend against
multiple attacks due to the node compromise. Therefore, we need more
algorithmic solutions. In this report, we focus on the behavior of routing
protocols to determine which properties make them more resilient to attacks.
Our aim is to find some answers to the following questions. Are there any
existing protocols, not designed initially for security, but which already
contain some inherently resilient properties against attacks under which some
portion of the network nodes is compromised? If yes, which specific behaviors
are making these protocols more resilient? We propose in this report an
overview of security strategies for WSNs in general, including existing attacks
and defensive measures. In this report we focus at the network layer in
particular, and an analysis of the behavior of four particular routing
protocols is provided to determine their inherent resiliency to insider
attacks. The protocols considered are: Dynamic Source Routing (DSR),
Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing
(RWR)
The Mason Test: A Defense Against Sybil Attacks in Wireless Networks Without Trusted Authorities
Wireless networks are vulnerable to Sybil attacks, in which a malicious node
poses as many identities in order to gain disproportionate influence. Many
defenses based on spatial variability of wireless channels exist, but depend
either on detailed, multi-tap channel estimation - something not exposed on
commodity 802.11 devices - or valid RSSI observations from multiple trusted
sources, e.g., corporate access points - something not directly available in ad
hoc and delay-tolerant networks with potentially malicious neighbors. We extend
these techniques to be practical for wireless ad hoc networks of commodity
802.11 devices. Specifically, we propose two efficient methods for separating
the valid RSSI observations of behaving nodes from those falsified by malicious
participants. Further, we note that prior signalprint methods are easily
defeated by mobile attackers and develop an appropriate challenge-response
defense. Finally, we present the Mason test, the first implementation of these
techniques for ad hoc and delay-tolerant networks of commodity 802.11 devices.
We illustrate its performance in several real-world scenarios
A virtual actuator approach for the secure control of networked LPV systems under pulse-width modulated DoS attacks
In this paper, we formulate and analyze the problem of secure control in the context of networked linear parameter varying (LPV) systems. We consider an energy-constrained, pulse-width modulated (PWM) jammer, which corrupts the control communication channel by performing a denial-of-service (DoS) attack. In particular, the malicious attacker is able to erase the data sent to one or more actuators. In order to achieve secure control, we propose a virtual actuator technique under the assumption that the behavior of the attacker has been identified. The main advantage brought by this technique is that the existing components in the control system can be maintained without need of retuning them, since the virtual actuator will perform a reconfiguration of the plant, hiding the attack from the controller point of view. Using Lyapunov-based results that take into account the possible behavior of the attacker, design conditions for calculating the virtual actuators gains are obtained. A numerical example is used to illustrate the proposed secure control strategy.Peer ReviewedPostprint (author's final draft
On the Impact of Wireless Jamming on the Distributed Secondary Microgrid Control
The secondary control in direct current microgrids (MGs) is used to restore
the voltage deviations caused by the primary droop control, where the latter is
implemented locally in each distributed generator and reacts to load
variations. Numerous recent works propose to implement the secondary control in
a distributed fashion, relying on a communication system to achieve consensus
among MG units. This paper shows that, if the system is not designed to cope
with adversary communication impairments, then a malicious attacker can apply a
simple jamming of a few units of the MG and thus compromise the secondary MG
control. Compared to other denial-of-service attacks that are oriented against
the tertiary control, such as economic dispatch, the attack on the secondary
control presented here can be more severe, as it disrupts the basic
functionality of the MG
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication
Jamming attacks target a wireless network creating an unwanted denial of
service. 5G is vulnerable to these attacks despite its resilience prompted by
the use of millimeter wave bands. Over the last decade, several types of
jamming detection techniques have been proposed, including fuzzy logic, game
theory, channel surfing, and time series. Most of these techniques are
inefficient in detecting smart jammers. Thus, there is a great need for
efficient and fast jamming detection techniques with high accuracy. In this
paper, we compare the efficiency of several machine learning models in
detecting jamming signals. We investigated the types of signal features that
identify jamming signals, and generated a large dataset using these parameters.
Using this dataset, the machine learning algorithms were trained, evaluated,
and tested. These algorithms are random forest, support vector machine, and
neural network. The performance of these algorithms was evaluated and compared
using the probability of detection, probability of false alarm, probability of
miss detection, and accuracy. The simulation results show that jamming
detection based random forest algorithm can detect jammers with a high
accuracy, high detection probability and low probability of false alarm
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