We consider the problem of detecting clones in wireless mobile adhoc networks. We assume that one of the devices of the network has been cloned. Everything, including certificates and secret keys. This can happen quite easily, because of a virus that immediately after sending all the content of the infected device to the adversary destroys itself, or just because the owner has left his device unattended for a few minutes in a hostile environment. The problem is to detect this attack. We propose a solution in networks of mobile devices carried by individuals. These networks are composed by nodes that have the capability of using short-range communication technology like blue-tooth or Wi-Fi, where nodes are carried by mobile users, and where links appear and disappear according to the social relationships between the users. Our idea is to use social physical contacts, securely collected by wireless personal smart-phones, as a biometric way to authenticate the legitimate owner of the device and detect the clone attack. We introduce two mechanisms: Personal Marks and Community Certificates. Personal Marks is a simple cryptographic protocol that works very well when the adversary is an insider, a malicious node in the network that is part, or not very far, from the social community of the original device that has been cloned. Community Certificates work very well when the adversary is an outsider, a node that has the goal of using the stolen credentials when interacting with other nodes that are far in the social network from the original device. When combined, these mechanisms provide an excellent protection against this very strong attack. We prove our ideas and solutions with extensive simulations in a real world scenario-with mobility traces collected in a real life experiment
The authors in @cite_26 propose two intrusion detection systems that have similar biometrics ideas: The first one is build upon Radio Frequency Fingerprinting (RFF), whereas the second one leverages User Mobility Profiles (UMP). However both detection systems are centralized, and rely on the fact that the intruder (the clone) behaves substantially differently from the real user in terms of geographical movements. Thus, compared with the solutions proposed in this paper, both systems are based on a completely different idea and are not able to detect anomalies when the clone behaves similarly to the original node (for example, when the clone attack happens in a building). Lastly, the solutions are not distributed.
@cite_26
2136975665 2531448500 2138948290 2512144135
Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks—each one containing real and fake samples of a given biometric modality and attack type—and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come. The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets. Abstract With the growing usage of technology, intrusion detection became an emerging area of research. Intrusion Detection System (IDS) attempts to identify and notify the activities of users as normal (or) anomaly. IDS is a nonlinear and complicated problem and deals with network traffic data. Many IDS methods have been proposed and produce different levels of accuracy. This is why development of effective and robust Intrusion detection system is necessary. In this paper, we have built a model for intrusion detection system using random forest classifier. Random Forest (RF) is an ensemble classifier and performs well compared to other traditional classifiers for effective classification of attacks. To evaluate the performance of our model, we conducted experiments on NSL-KDD data set. Empirical result show that proposed model is efficient with low false alarm rate and high detection rate.