http://dtstc.ugr.es/~jedv/descargas/2009_CoSe09-Anomaly-based-network-intrusion-detection-Techniques,-systems-and-challenges.pdf

Anomaly-based network intrusion detection: Techniques, systems and challenges

This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

An overview of anomaly detection techniques: Existing solutions and latest technological trends

The Transmission Control Protocol.

Review: Intrusion detection system: A comprehensive review

Signal representation is crucial for designinga speechrecognizer. The feature extractor selects the information to be used by the classifier to perform the recognition. In noisy environments, the data vectors representing the speech signal are changed and the recognizer performance is degraded by two main facts: (1) the mismatch between the training and the recognition conditions and (2) the degradation of the signal to be recognized. In such a situation, the representation of the speech signal plays an important role. In this paper, we analyze the importance of the representation for speechrecognition in noise. We apply the Discriminative Feature Extraction (DFE) method to optimize the representation. The experiments presented in this work show that the DFE method, which has been successfully applied in clean environments, leads also to improvements of the speech recognizers in noise.

Discriminative feature extraction for speech recognition in noise.

Relay node (RN) placement is a challenging problem, specially in mobile ad hoc networks (MANETs) environments where the locations of the nodes are continuously changing. In this context we propose here a novel, modular and adaptable RN placement solution based on the joint maximization of the network connectivity and throughput. For that, we use a combination of particle swarm optimization (PSO) and model predictive control (MPC) inspired methodologies to drive the RN movements. In order to corroborate the validity and applicability of the approach we implement, deploy and test the proposed solution in a real MANET environment where the nodes are mobile robots.

DRNS: Dynamical Relay Node Placement Solution

Wireless sensor networks (WSNs) are highly sensible to data integrity attacks, which have an important impact on a number of relevant deployments and services. This paper introduces a tolerance approach to fight against data modification attacks in WSNs, which is based on a missing data imputation scheme. The proposal relies on two principal contributions: (1) a multivariate statistical technique where the dynamics of the sensor measurements for the monitored area are captured through the use of dynamic PCA (DPCA), and (2) a variable routing strategy that improves the recovering performance by spreading the effects of the data tampering attack. On the other hand, a complementary multivariate statistical anomaly detection module is implemented to determine the occurrence of data tampering attacks and trigger the subsequent reaction procedure to recover the affected data. As shown by the results obtained, the proposed tolerance approach improves the robustness of a WSN against data tampering attacks, and so its survivability and normal operation over time.

/pdf/tampered-data-recovery-in-wsns-through-dynamic-pca-and-3ezxpeaydr.pdf

Tampered Data Recovery in WSNs through Dynamic PCA and Variable Routing Strategies

Defenses against Packet-Dropping Attacks in Wireless Multihop Ad Hoc Networks

In this paper, we introduce a new methodology for network access control for Android devices based on app risk assessment. Named ARANAC (which stands for Application Risk Assessment based Network Access Control ), this methodology is specially tailored for scenarios using the Bring-Your-Own-Device (BYOD) policy, where the adoption of some solutions can lead to problems in security and privacy for both the employees and the business organization. ARANAC mainly relies on the analysis of an aggregate of permissions declared in the manifests of installed applications on users’ devices. The access control scheme combines three operational modules: i) a device monitoring tool, ii) a novel permission-based risk model, and iii) an anomaly-based detection machine learning module based on a methodology (called MSNM, from Multivariate Statistical Network Monitoring ) that provides both detection and diagnostic capabilities. ARANAC’s novelty is in the combination of four features. Firstly, it is privacy-aware, and thus, it does not require detailed information about installed applications but only an aggregate of permissions. Secondly, it builds a normality model by combining expert knowledge with data, capturing the behavior of a complete population of mobile devices. Thirdly, it is dynamic, as permissions are updated in real time, allowing the network to re-assess access control on a continuous basis. Finally, its diagnostic capabilities allow for giving recommendations to final users so that they are capable of mitigating their risks when accessing networks. We evaluated the approach with more than 80 Android devices at a university campus network and obtained interesting results regarding security risks in the usual deployment of device apps.

/pdf/aranac-a-bring-your-own-permissions-network-access-control-27xa6u90j5.pdf

Pedro García-Teodoro

Papers

Discriminative feature extraction for speech recognition in noise.

DRNS: Dynamical Relay Node Placement Solution

Tampered Data Recovery in WSNs through Dynamic PCA and Variable Routing Strategies

Defenses against Packet-Dropping Attacks in Wireless Multihop Ad Hoc Networks

ARANAC: A Bring-Your-Own-Permissions Network Access Control Methodology for Android Devices