Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

Evaluating the Provision of Botnet Defences using Translational Research Concepts.

Botnet research frequently draws on concepts from other fields. An example is the use of epidemiological models when studying botnet propagation, which facilitate an understanding of bot spread dynamics and the exploration of behavioural theory. Whilst the literature is rich with these models, it is lacking in work aimed at connecting the insights of theoretical research with day-to-day practice. To address this, we look at botnets through the lens of implementation science, a discipline from the field of translational research in health care, which is designed to evaluate the implementation process. In this paper, we explore key concepts of implementation science, and propose a framework-based approach to improve the provision of security measures to network entities. We demonstrate the approach using existing propagation models, and discuss the role of implementation science in malware defence

Multiphase deployment models for fast self healing in wireless sensor networks

The majority of studies on security in resource limited wireless sensor networks (WSN) focus on finding an efficient balance among energy consumption, computational speed and memory usage. Besides these resources, time is a relatively immature aspect that can be considered in system design and performance evaluations. In a recent study(Castelluccia and Spognardi, 2007), the time dimension is used to lower the ratio of compromised links, thus, improving resiliency in key distribution in WSNs. This is achieved by making the old and possibly compromised keys useful only for a limited amount of time. In this way, the effect of compromised keys diminish in time, so the WSN selfheals. In this study we further manipulate the time dimension and propose a deployment model that speeds up the resilience improvement process with a tradeoff between connectivity and resiliency. In our method, self healing speeds up by introducing nodes that belong to future generations in the time scale. In this way, the duration that the adversary can make use of compromised keys become smaller

Improved fuzzy vault scheme for fingerprint verification

Fuzzy vault is a well-known technique to address the privacy concerns in biometric identification applications. We revisit the fuzzy vault scheme to address implementation, efficiency, and security issues encountered in its realization. We use the fingerprint data as a case study. We compare the performances of two different methods used in the implementation of fuzzy vault, namely brute force and Reed Solomon decoding. We show that the locations of fake (chaff) points in the vault leak information on the genuine points and propose a new chaff point placement technique that makes distinguishing genuine points impossible. We also propose a novel method for creation of chaff points that decreases the success rate of the brute force attack from 100% to less than 3.5%. While this paper lays out a complete guideline as to how the fuzzy vault is implemented in an efficient and secure way, it also points out that more research is needed to thwart the proposed attacks by presenting ideas for future research

A Security Pattern for Cloud service certification

Cloud computing is interesting from the economic, operational and even energy consumption perspectives but it still raises concerns regarding the security, privacy, governance and compliance of the data and software services offered through it. However, the task of verifying security properties in services running on cloud is not trivial. We notice the provision and security of a cloud service is sensitive. Because of the potential interference between the features and behavior of all the inter-dependent services in all layers of the cloud stack (as well as dynamic changes in them). Besides current cloud models do not include support for trust-focused communication between layers. We present a mechanism to implement cloud service certification process based on the usage of Trusted Computing technology, by means of its Trusted Computing Platform (TPM) implementation of its architecture. Among many security security features it is a tamper proof resistance built in device and provides a root of trust to affix our certification mechanism. We present as a security pattern the approach for service certification based on the use TPM.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Verifying privacy by little interaction and no process equivalence

While machine-assisted verification of classical security goals such as confidentiality and authentication is well-established, it is less mature for recent ones. Electronic voting protocols claim properties such as voter privacy. The most common modelling involves indistinguishability, and is specified via trace equivalence in cryptographic extensions of process calculi. However, it has shown restrictions. We describe a novel model, based on unlinkability between two pieces of information. Specifying it as an extension to the Inductive Method allows us to establish voter privacy without the need for approximation or session bounding. The two models and their latest specifications are contrasted