A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
@cite_20 @cite_7 the authors introduced an AIS based misbehavior detection system for ad hoc wireless networks. They used Glomosim for simulating data traffic, their setup was an area of 800 @math 600m with 40 mobile nodes (speed 1 m s) of which 5-20 are misbehaving; the routing protocol was DSR. Four genes were used to capture local behavior at the network layer. The misbehavior implemented is a subset of misbehavior introduced in this paper; their observed detection rate is about 55 @cite_10 the authors describe an AIS able to detect anomalies at the transport layer of the OSI protocol stack; only a wired TCP IP network is considered. Self is defined as normal pairwise connections. Each detector is represented as a 49-bit string. The pattern matching is based on r-contiguous bits with a fixed @math .
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In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper we investigate the use of an Artificial Immune System (AIS) to detect node misbehavior in a mobile ad-hoc network using DSR. The system is inspired by the natural immune system of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. We describe the first step of our design; it employs negative selection, an algorithm used by the natural immune system. We define how we map the natural immune system concepts such as self, antigen and antibody to a mobile ad-hoc network, and give the resulting algorithm for misbehavior detection. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters impact the results. Further steps will extend the design by using an analogy to the innate system, danger signals, costimulation and memory cells. In mobile ad-hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as Dynamic Source Routing (DSR). The networks are vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. Mobile ad-hoc networking works properly only if the participating nodes cooperate in routing and forwarding. However, it may be advantageous for individual nodes not to cooperate. We propose a protocol, called CONFIDANT, for making misbehavior unattractive; it is based on selective altruism and utilitarianism. It aims at detecting and isolating misbehaving nodes, thus making it unattractive to deny cooperation. Trust relationships and routing decisions are based on experienced, observed, or reported routing and forwarding behavior of other nodes. The detailed implementation of CONFIDANT in this paper assumes that the network layer is based on the Dynamic Source Routing (DSR) protocol. We present a performance analysis of DSR fortified by CONFIDANT and compare it to regular defenseless DSR. It shows that a network with CONFIDANT and up to 60 of misbehaving nodes behaves almost as well as a benign network, in sharp contrast to a defenseless network. All simulations have been implemented and performed in GloMoSim. The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7 on VOC07, 80.4 on VOC12, and 34.4 on COCO. Codes will be made publicly available.