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A 10-node ad hoc network with priming. s 1 is the only data flow source node. s 3 , s 7 and s 10 are sink nodes for three distinct data flows.
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We propose and evaluate an immuno-inspired approach for misbehavior detection in ad hoc wireless networks. Misbehavior is
the result of an intrusion, or a software or hardware failure. Our misbehavior detection approach is inspired by the role
of co-stimulation and priming in the biological immune system (BIS). We translate priming into a computati...
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... propagation and co-stimulation is executed within the same node; see Fig. 3. Notice that some nodes in the example network, for the shown data flows, are unable to computê F 1 since they do not have any two-hop neighbor s i+2 . On the other hand, several nodes receive multiplê f si+2 ...
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Citations
... We apply g = {0.0129, 0.0643, 1.0}, which we derived from the energy cost model presented in [29], based on communication and processing costs incurred by a wireless device. They reflect how much costlier is K 2 classification relative to K 1 classification. ...
Due to the fact that in well-engineered systems, normal behavior is the dominant case, it is necessary that normal behavior can be recognized in an energy-efficient way. When cascading classification is applied, the task of the first classifier is to impose energy efficiency, whereas the task of the other classifier (or classifiers) is to provide false positives control. Adaptivity to novel or re-occurring errors can be in this setup carried out by including a feedback loop enabling that the first classifier improves its capability to recognize normal behavior. However, in order to sustain adaptivity, it is necessary to provide the feedback loop with some minimum number of instances from both behavior classes. This results in a trade-off between energy efficiency and error detection performance. In order to obtain a trade-off that is suitable in a given situation, it is necessary that a methodology for comparing trade-offs across qualitatively different types of error detection approaches is available. Therefore, we propose and evaluate an approach for comparing trade-offs that is based on iso-performance lines. In addition, we also propose and evaluate several modifications to k-nearest neighbor and decision tree classifiers, so that different grades of trade-offs can be obtained. Copyright © 2015 John Wiley & Sons, Ltd.
... Using the immune system-inspired error detection algorithm, it was shown that various objectives, such as energy efficiency, final false positives rate, adaptivity, and novel error detection, could be taken into account. By simulating the experiments conducted by Drozda et al. (2011b), improvements in the adaptive strategy-based error detection method were validated with minimum impact on energy efficiency and intact false positives control (Drozda et al. 2011a). ...
Structural systems are susceptible to damage throughout their operational lifetime. Thus, structural health monitoring technologies and, in particular, sensors that could monitor structural performance and detect damage are needed. While there exist a variety of different sensing platforms, this continues to be an active area of research due to the many challenges associated with identifying and quantifying structural damage, which is inherently very complex. This chapter discusses an emerging area of sensors research in which sensor design or functionality is inspired by biological systems. By borrowing concepts from and learning how nature's creations sense and interact with its environment, the goal is to create novel sensors with unparalleled performance as compared to the current state-of-art. This chapter is not meant to be an exhaustive literature review on this topic. Rather, only a small selection of published work is sampled and presented to showcase different ideas and the breadth of research. Topics ranging from bio-inspired algorithms, creature-like robots, and skin-like sensors are presented.
... Thus, while in the present study we instantiate such agents by interrelated genotypes invading the body and idiotypes defending it, taking a broader view may come to benefit several other areas. One area with obvious conceptual ties to our invader-defender setting is that of computer security, and in fact proposals have been put forward that are based on immune-related notions [18,19]. This has also been the case of other areas with less obvious but equally relevant connections to our study, such as combinatorial optimization (cf. ...
A quasispecies is the stationary state of a set of interrelated genotypes
that evolve according to the usual principles of selection and mutation.
Quasispecies studies have invariably concentrated on the possibility of errors
during genotype replication and their role in promoting either the survival or
the demise of the quasispecies. In a previous work [V. C. Barbosa, R.
Donangelo, and S. R. Souza, J. Theor. Biol. 312, 114 (2012)], we introduced a
network model of quasispecies dynamics, based on a single probability parameter
($p$) and capable of addressing several plausibility issues of previous models.
Here we extend that model by pairing its network with another one aimed at
modeling the dynamics of the immune system when confronted with the
quasispecies. The new network is based on the idiotypic-network model of
immunity and, together with the previous one, constitutes a network model of
interacting genotypes and idiotypes. The resulting model requires further
parameters and as a consequence leads to a vast phase space. We have focused on
a particular niche in which it is possible to observe the trade-offs involved
in the quasispecies' survival or destruction. Within this niche, we give
simulation results that highlight some key preconditions for quasispecies
survival. These include a minimum initial abundance of genotypes relative to
that of the idiotypes and a minimum value of $p$. The latter, in particular, is
to be contrasted with the stand-alone quasispecies network of our previous
work, in which arbitrarily low values of $p$ constitute a guarantee of
quasispecies survival.
... In the attack scenario with epidemic and pandemic features, the knowledge of one single node is far from enough to detection and reaction, global knowledge is required. M. Drozda et al. [3][10][11] introduced AIS for intrusion detection to sensor networks including WSN and ad hoc network. They focus on misbehaviors as packet dropping, packet delaying, wormholes and so on. ...
The resource constraint characteristic of sensor nodes make wireless sensor networks (WSN) very vulnerable to resource depletion attack such as DoS/DDos attack. On the other hand, the resource constraint characteristic also makes anomaly detection a challenging problem in WSN. To address the challenge, this paper presents an anomaly detection framework by taking the advantages of artificial immune system (AIS) and fuzzy theory. The proposed framework incorporates three components: local danger sensing, co-stimulation and global recognition. Due to the hierarchical structure and cooperative mechanism, the proposed model shows more advantages in detection performance than conventional method. The simulation results show that compared to watchdog method, the proposed method can provide higher detection rate and lower false detection rate. Moreover, the propose method shows advantages in flexibility and adaptability.
... The fixed constant 2 in Eq. 18 reflects the fact that Stat must hop twice to reach A. We assume that k data packets need to be overheard in order to detect an erroneous node. The experimental results in [17], [18] show that considering data packet dropping and data packet delaying errors, it is possible to construct a (γ 1 , γ 2 )-transformation that fulfils the conditions stated in Eqs. 16 and 17. ...
... The experimental setup is similar to that used in [18]. We consider a network with 1718 nodes. ...
... size increases; see [17]. It is straightforward to couple the energy model introduced in Eq. 4 with energy consumption model of a wireless device in order to get a quantitative estimate; see [18]. The average class distribution for the top 20 nodes was as follows: i) win. ...
In a number of areas, for example, sensor networks and systems of systems, complex networks are being used as part of applications that have to be dependable and safe. A common feature of these networks is they operate in a de-centralised manner and are formed in an ad-hoc manner and are often based on individual nodes that were not originally developed specifically for the situation that they are to be used. In addition, the nodes and their environment will have different behaviours over time, and there will be little knowledge during development of how they will interact. A key challenge is therefore how to understand what behaviour is normal from that which is abnormal so that the abnormal behaviour can be detected, and be prevented from affecting other parts of the system where appropriate recovery can then be performed. In this paper we review the state of the art in bio-inspired approaches, discuss how they can be used for error detection as part of providing a safe dependable sensor network, and then provide and evaluate an efficient and effective approach to error detection.
The exponential raise in traffic across network has obliged the need to distinguish unauthorized access. The process of identifying an individual usually based on a user name and password, which is the process of giving individuals access to system objects based on their identity. Authentication can involve highly complex and secure methods. This paper proposes an innovative algorithm based on neural networks and fuzzy logic that can be used for easy identification of unauthorized users in a network. The algorithm has been tested and validated and the results have been discussed. The result analysis shows that the algorithm is successful in intrusion detection.
According to the challenges of intrusion detection technique for wireless sensor networks, a danger theory inspired intrusion detection model was proposed by taking advantage of the working principle of artificial immune system. Due to the distributed and cooperative mechanism, the proposed model shows more advantages in detection performance and energy consumption than methods adopt promiscuous mode to get global knowledge. The simulation results show that compared to traditional Watchdog algorithm with single threshold and self-nonself (SNS) model, the model inspired by danger theory can provide highest correct detection rate and lowest false detection rate. Moreover, the model can also decrease the energy consumption of the system.
