Enhancing business continuity management capacities in the insurance industry : a Southeast Asian perspective

As a resilience enhancing practice, business continuity management (BCM) can play an important role in aiding preparation of the insurance industry for coping with the losses incurred by major discontinuity incidents: regardless of cause. Acknowledging the increasing frequency of unpredictable man-made disasters and natural catastrophes, the insurance industry would benefit from examining and implementing, where suitable, key elements of BCM. Such strategic decisions would assist insurers and re-insurers collectively to enhance mutual capability to respond to, and recover from, the impact of significant losses. This paper presents a comparison of opinions about BCM practitioners in both retail and re-insurance companies on the importance of generic continuity practices with actual levels of BCM practice across the two industry groups in Southeast Asia. It suggests means by which multi-lateral cooperation across Asian economies and between retail and re-insurance market segments might enhance the viability of the insurance industry in the face of increased stress from major natural and socio-technical hazards.

Extending the task of diarization to speaker attribution

In this paper we extend the concept of speaker annotation within a single-recording, or speaker diarization, to a collection wide approach we call speaker attribution. Accordingly, speaker attribution is the task of clustering expectantly homogenous intersession clusters obtained using diarization according to common cross-recording identities. The result of attribution is a collection of spoken audio across multiple recordings attributed to speaker identities. In this paper, an attribution system is proposed using mean-only MAP adaptation of a combined-gender UBM to model clusters from a perfect diarization system, as well as a JFA-based system with session variability compensation. The normalized cross-likelihood ratio is calculated for each pair of clusters to construct an attribution matrix and the complete linkage algorithm is employed to conduct clustering of the inter-session clusters. A matched cluster purity and coverage of 87.1% was obtained on the NIST 2008 SRE corpus.

Fourier active appearance models

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

An efficient public key management system : an application in vehicular ad hoc networks

The major purpose of Vehicular Ad Hoc Networks (VANETs) is to provide safety-related message access for motorists to react or make a life-critical decision for road safety enhancement. Accessing safety-related information through the use of VANET communications, therefore, must be protected, as motorists may make critical decisions in response to emergency situations in VANETs. If introducing security services into VANETs causes considerable transmission latency or processing delays, this would defeat the purpose of using VANETs to improve road safety. Current research in secure messaging for VANETs appears to focus on employing certificate-based Public Key Cryptosystem (PKC) to support security. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This paper proposes an efficient public key management system for VANETs: the Public Key Registry (PKR) system. Not only does this paper demonstrate that the proposed PKR system can maintain security, but it also asserts that it can improve overall performance and scalability at a lower cost, compared to the certificate-based PKC scheme. It is believed that the proposed PKR system will create a new dimension to the key management and verification services for VANETs.

Defending web services against denial of service attacks using client puzzles

The interoperable and loosely-coupled web services architecture, while beneficial, can be resource-intensive, and is thus susceptible to denial of service (DoS) attacks in which an attacker can use a relatively insignificant amount of resources to exhaust the computational resources of a web service. We investigate the effectiveness of defending web services from DoS attacks using client puzzles, a cryptographic countermeasure which provides a form of gradual authentication by requiring the client to solve some computationally difficult problems before access is granted. In particular, we describe a mechanism for integrating a hash-based puzzle into existing web services frameworks and analyze the effectiveness of the countermeasure using a variety of scenarios on a network testbed. Client puzzles are an effective defence against flooding attacks. They can also mitigate certain types of semantic-based attacks, although they may not be the optimal solution.

Wide baseline correspondence extraction beyond local features

Robust, affine covariant, feature extractors provide a means to extract correspondences between images captured by widely separated cameras. Advances in wide baseline correspondence extraction require looking beyond the robust feature extraction and matching approach. This study examines new techniques of extracting correspondences that take advantage of information contained in affine feature matches. Methods of improving the accuracy of a set of putative matches, eliminating incorrect matches and extracting large numbers of additional correspondences are explored. It is assumed that knowledge of the camera geometry is not available and not immediately recoverable. The new techniques are evaluated by means of an epipolar geometry estimation task. It is shown that these methods enable the computation of camera geometry in many cases where existing feature extractors cannot produce sufficient numbers of accurate correspondences.

Gap analysis of intrusion detection in smart grids

Given the recent emergence of the smart grid and smart grid related technologies, their security is a prime concern. Intrusion detection provides a second line of defense. However, conventional intrusion detection systems (IDSs) are unable to adequately address the unique requirements of the smart grid. This paper presents a gap analysis of contemporary IDSs from a smart grid perspective. This paper highlights the lack of adequate intrusion detection within the smart grid and discusses the limitations of current IDSs approaches. The gap analysis identifies current IDSs as being unsuited to smart grid application without significant changes to address smart grid specific requirements.

Multi-objective optimisation of bijective s-boxes

In this paper we investigate the heuristic construction of bijective s-boxes that satisfy a wide range of cryptographic criteria including algebraic complexity, high nonlinearity, low autocorrelation and have none of the known weaknesses including linear structures, fixed points or linear redundancy. We demonstrate that the power mappings can be evolved (by iterated mutation operators alone) to generate bijective s-boxes with the best known tradeoffs among the considered criteria. The s-boxes found are suitable for use directly in modern encryption algorithms.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Can audio-visual speech recognition outperform acoustically enhanced speech recognition in automotive environment?

The use of visual features in the form of lip movements to improve the performance of acoustic speech recognition has been shown to work well, particularly in noisy acoustic conditions. However, whether this technique can outperform speech recognition incorporating well-known acoustic enhancement techniques, such as spectral subtraction, or multi-channel beamforming is not known. This is an important question to be answered especially in an automotive environment, for the design of an efficient human-vehicle computer interface. We perform a variety of speech recognition experiments on a challenging automotive speech dataset and results show that synchronous HMM-based audio-visual fusion can outperform traditional single as well as multi-channel acoustic speech enhancement techniques. We also show that further improvement in recognition performance can be obtained by fusing speech-enhanced audio with the visual modality, demonstrating the complementary nature of the two robust speech recognition approaches.

Parametric differences between a real-world distributed denial-of-service attack and a flash event

Distributed Denial-of-Service (DDoS) attacks continue to be one of the most pernicious threats to the delivery of services over the Internet. Not only are DDoS attacks present in many guises, they are also continuously evolving as new vulnerabilities are exploited. Hence accurate detection of these attacks still remains a challenging problem and a necessity for ensuring high-end network security. An intrinsic challenge in addressing this problem is to effectively distinguish these Denial-of-Service attacks from similar looking Flash Events (FEs) created by legitimate clients. A considerable overlap between the general characteristics of FEs and DDoS attacks makes it difficult to precisely separate these two classes of Internet activity. In this paper we propose parameters which can be used to explicitly distinguish FEs from DDoS attacks and analyse two real-world publicly available datasets to validate our proposal. Our analysis shows that even though FEs appear very similar to DDoS attacks, there are several subtle dissimilarities which can be exploited to separate these two classes of events.

CAT Detect (Computer Activity Timeline Detection) : a tool for detecting inconsistency in computer activity timelines

The construction of timelines of computer activity is a part of many digital investigations. These timelines of events are composed of traces of historical activity drawn from system logs and potentially from evidence of events found in the computer file system. A potential problem with the use of such information is that some of it may be inconsistent and contradictory thus compromising its value. This work introduces a software tool (CAT Detect) for the detection of inconsistency within timelines of computer activity. We examine the impact of deliberate tampering through experiments conducted with our prototype software tool. Based on the results of these experiments, we discuss techniques which can be employed to deal with such temporal inconsistencies.