Sentiment analysis of the text data available on the web either in the form of blogs or at social media sites such as Twitter, Facebook, and LinkedIn, offers information through which to assess people's perspective of products and services that are of interest to them. Consumers routinely scour the Internet to assess other user's reviews for a product or a service before making their own decision. Likewise, these same data can potentially provide businesses a snapshot in time of the users' response to their products/services and even the trends over time. This information gained through sentiment analysis can then be used by businesses to make decisions about improving their products and services, and gain an increased edge over their competitors. In this paper, we illustrate the potential of sentiment analysis of Twitter data to gauge users' response to popular smart phone brands and their underlying operating systems. Specifically, our objective is to investigate whether the tweets available on the web are sufficient to gain useful insight about the performance of popular smart phone brands, their battery life, screen quality, and on the perceived performance of the phones operating systems. Our results show that although the Twitter data does provide some information about users' sentiments to the popular smart phone brands and their underlying operating systems, the amount of data available for different brands varies significantly. This limitation makes the comprehensive analysis of users' response somewhat more challenging for some brands compared to others and consequently makes the comparison between brands almost impossible.

The impact on battery life has become a key design criteria for mobile device applications (apps). Poor user perceptions about an app's energy use can now quickly lead to negative social media reviews and the resulting adverse impact son the app's marketability. Traditionally, assessments of an app's energy use have been done by simply measuring the total current draw experienced by the device while the app is on. Such approaches have become insufficient within modern devices due to the prevalence of energy management features within their hardware and operating systems (OSes) and the increased complexity of modern apps. This leads to: (i) non-stationary time domain current signals, due to the devices' dynamics power management, and (ii) different operational "modes" within an app having distinct energy use profiles. Within this work, an approach based on matched filters is developed to allow per-mode energy profiles to be correctly identified and characterized under(i) and (ii). This approach is then applied to quantitatively assess the energy use profile for a dual-mode multi-platform commercial Android quality of experience (QoE) assessment app across a number of mobile devices and OS variants, where it is shown that such differences can lead to significant differences in the app-level energy profiles that are produced.

Botnets are a well recognized global cyber-security threat as they enable attack communities to command large collections of compromised computers (bots) on-demand. Peer to-peer (P2P) distributed hash tables (DHT) have become particularly attractive botnet command and control (C & C) solutions due to the high level resiliency gained via the diffused random graph overlays they produce. The injection of Sybils, computers pretending to be valid bots, remains a key defensive strategy against DHT-structured P2P botnets. This research uses packet level network simulations to explore the relative merits of random, informed, and partially informed Sybil placement strategies. It is shown that random placements perform nearly as effectively as the tested more informed strategies, which require higher levels of inter-defender co-ordination. Moreover, it is shown that aspects of the DHT-structured P2P botnets behave as statistically nonergodic processes, when viewed from the perspective of stochastic processes. This suggests that although optimal Sybil placement strategies appear to exist they would need carefully tuning to each specific P2P botnet instance.

Telecommunication networks are under stress due to rapid traffic increase. Since most of this traffic increase is due to large file transfers, this paper proposes a cross-layer transport protocol specifically designed to efficiently handle large transactions. Traffic generated from large transactions is shaped into a periodic succession of fixed-size data frames. Each transaction is then scheduled for transmission using a two-way reservation protocol. Simulation results show that the proposed approach is capable of significantly improving goodput and end-to-end delay relative to TCP, improving efficiency of bandwidth utilization by over 40%.

This paper presents flott, a fast, low memory T-transform algorithm which can be used to compute the string complexity measure T-complexity. The algorithm uses approximately one third of the memory of its predecessor while reducing the running time by about 20 percent. The flott implementation has the same worst-case memory requirements as state of the art suffix tree construction algorithms. A suffix tree can be used to efficiently compute the Lempel-Ziv production complexity, which is another measure of string complexity. The C-implementation of flott is available as Open Source software.

Despite defensive advances, malicious software (malware) remains an ever present cyber-security threat. Cloud environments are far from malware immune, in that: i) they innately support the execution of remotely supplied code, and ii) escaping their virtual machine (VM) confines has proven relatively easy to achieve in practice. The growing interest in clouds by industries and governments is also creating a core need to be able to formally address cloud security and privacy issues. VM introspection provides one of the core cyber-security tools for analyzing the run-time behaviors of code. Traditionally, introspection approaches have required close integration with the underlying hypervisors and substantial re-engineering when OS updates and patches are applied. Such heavy-weight introspection techniques, therefore, are too invasive to fit well within modern commercial clouds. Instead, lighter-weight introspection techniques are required that provide the same levels of within-VM observability but without the tight hypervisor and OS patch-level integration. This work introduces Maitland as a prototype proof-of-concept implementation a lighter-weight introspection tool, which exploits paravirtualization to meet these end-goals. The work assesses Maitland's performance, highlights its use to perform packer-independent malware detection, and assesses whether, with further optimizations, Maitland could provide a viable approach for introspection in commercial clouds.

This paper focuses on the problem of resource consolidation management within cloud computing environments and extends our previous IMPROMPTU model which demonstrated the viability of distributed Multiple Criteria Decision Analysis (MCDA) to provide a resource consolidation management that simultaneously achieves lower numbers of reconfiguration events and fewer service level agreement (SLA) violations as compared to other approaches. A core limitation of our previous work was that it only assessed the PROMETHEE II outranking-based MCDA method, leaving open the question of whether better outranking schemes exist and, more generally, what denotes the properties of good outranking approaches for this problem domain. This work addresses these deficiencies through extending the IMPROMPTU model to directly compare PROMETHEE II with ELECRE III and PAMSSEM II, two other well-known outranking-based MCDA schemes. An in-depth analysis of the generated simulation results are then used to highlight the core trade-offs between each of these MCDA approaches.

The wide-scale adoption of smart phones has begun to provide a pragmatic real-world deployment environments for mobile ad hoc networks, (i.e., as peer-to-peer game platforms, for emergency services, etc.). Such deployments are likely to occur with urban cores where device densities would easily exceed those that have traditionally been studied. Moreover, the quality of the resulting solutions will innately rest on the capabilities of the underlying routing protocols. Of current protocols, the OLSR proactive routing protocol makes the strongest arguments regarding its suitability to such larger, denser network environments. This work tests OLSR's true suitability by analyzing its performance within a 360-node network existing within a standard 1 km ×1.5 km communications area, (i.e., innately for a network with approximately 3 × the node densities typically studied). It is shown that OLSR largely fails for such denser networks, with these failure arising due to OLSR's underlying presumption that routing tables updates should occur relatively infrequently. This limitation within OLSR has not been previously reported and this work highlights the reasons why these issues were likely not observed within prior OLSR studies.

Wireless secret key generation (WKG) methods based on communications channel characterizations have gained significant interest as a mechanism for providing secure point-to-point communications. Ultra-wide band (UWB) WKG solutions provide an advantage over narrow- and wide-band approaches in that they can generate secure keys even when Alice and Bob, the two communicating parties, and all objects within the environment in which they exist, are stationary (i.e., in scenarios involving zero movement). This creates a secondary problem in that the high temporal correlations of such environments can lead to successive WKG processes producing identical or nearly identical secret keys, whereas the ideal is that all generated keys from successive WKG processes should be independent. This work shows that one method available to address this problem is to move to multiple-input multiple-output (MIMO)-based antenna systems and the use of random beamforming. The security of the resulting approach is assessed with respect to the worst case scenario whereby the eavesdropper, Eve, is assumed to possess perfect knowledge of Alice and Bob's MIMO channel and is able to estimate, with some degree of error, the coefficients of Alice and Bob's independent random beamformers.

Event-based simulation has become a primary means of pursuing mobile ad hoc network (MANET) research. The stochastic nature of MANETs has been well studied with respect to mobility models, but less work has looked at the statistical behaviors of network layer features, (e.g., PDR, delay, hops and routing overhead). Fundamentally, issues such as “When do start up transients end?” and “Do all Monte- Carlo runs indeed arrive at the same steady-state distributions?” have not been well explored. This work explores these issues through using the DYMO routing protocol and the OMNeT++ simulation framework as exemplars. By applying distribution free Kolmogorov-Smirnov goodness-of-fit tests it is shown that, for network-layer features: a) MANET start-up transients can persist far longer than previously reported, b) transient durations can vary significantly from feature to feature and with varying node velocities, and c) Monte-Carlo runs of a given MANET scenario can produce distinct behavioral modes. It is then discussed whether these issues are likely inherent to MANETs and their routing protocols or an artifact of OMNeT++.

eCommerce has rapidly turned into a trillion dollar a year industry. Now an integral part of modern economies, it is continuing to expand, especially in the form of M- commerce. Numerous solutions have been proposed to secure consumer-level eCommerce and M-commerce transactions. The recent shift toward chip-and-PIN cards in some jurisdictions, and similar technologies that require pre-transaction customer authorization, has begun to shift the legal liability for security breaches from the financial institutions onto the customers themselves. Because it is relatively easy to acquire someone's PIN (e.g., through shoulder surfing, cameras placed in the environment, touch sensitive overlays, or compromised debit or credit card terminals), a core issue is that customers are given no formal means by which they can prove their involvement (or lack thereof) in a given transaction. To make matters worse, the supposition becomes that they were careless with their PIN and, hence, by the card holder agreement, hold financial responsibility for the transaction(s). This work addresses said problem by developing a secure and efficient (<; 5 second) consumer-level eCommerce/M-Commerce transaction protocol that supports non-repudiation for the customer, merchant, and financial institution. Hence, post-transaction, each participant holds sufficient information to prove what the others did (or did not) do. To our knowledge this is the first transaction protocol to support such full 3-way non-repudiation.

In computing clouds, it is desirable to avoid wasting resources as a result of under-utilization and to avoid lengthy response times as a result of over-utilization. In this paper, we propose a new approach for dynamic autonomous resource management in computing clouds. The main contribution of this work is two-fold. First, we adopt a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center. Second, the Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method. Simulation results show that the proposed approach is promising in terms of scalability, feasibility and flexibility.

Wireless channel characterization is a known methodology for the generation of secret keys, where the secrecy of the key depends on the spatial-temporal correlation properties of the channel which themselves arise due to the channel's physical constraints. Spatial correlations can be suitably addressed simply by moving from narrow band channels to ultra-wide band (UWB) channels, but this does not address temporal correlations, (i.e., the likelihood that two successive key generation processes will generate the same, or nearly the same, key). This work shows that the worst-case of the temporal correlation problem, (i.e., when an eavesdropper has perfect knowledge of all past channel characterizations) is effectively addressed by applying channel prediction to the available ensemble of channel characterisation events and removing the predictable ensembles. It is shown that this proposed prediction approach increases the security of the key generation process while simultaneously reduce the available amount of information for the key generation. Moreover, real-world experimental data is also applied to confirm that a linear prediction suffices in this case.

Theoretical models of ultrawideband (UWB) radio channels indicate that pairs of UWB radio transceivers measure their common radio channel with a high degree of agreement and third parties are not be able to accurately estimate the state of the common channels. These properties allow generation of secret keys to support secure communications from UWB channels measurements. In this paper, the results of UWB propagation studies are presented that validate the required properties to support secret key generation in a typical indoor environment. Key generation algorithms are employed on the measured data and key lengths on the order of thousands of bits are obtained capable of supporting most popular cryptographic systems. The paper also reports measurements of the spatial and temporal correlation of the UWB channel from which the relative privacy of the secret keys can be determined as well as the rate new secret keys may be generated.

This paper aims to give an empirical evaluation and analysis on ultra-wideband (UWB) channel properties, which differ quite significantly from their narrowband counterpart. Time domain measurements are performed to investigate the property of channel reciprocity, spatial correlation and body shadow effect. First of all, UWB reciprocity is observed under both baseband and radio frequency (RF) transmission, and it is observed to be distance independent and frequency independent. In addition, the spatial correlation fluctuates with distance in a two dimensional grid, and an investigation of multiple antenna spacing is provided. Finally, body shadow effect is investigated by studying the signal strength affected by placement of human body obstruction on the transmission path.

It has been shown that the radio channel impulse response for a pair of legitimate Ultra-wide band (UWB) transceivers can be used to generate secret keys for secure communications. Past proposed secret key generation algorithms under-exploited the available number of secret key bits from the radio channel. This paper proposes a new efficient method for generation of the shared key where the transceivers use LDPC decoders to resolve the differences in their channel impulse response measurements caused by measurement noise. To ensure secret key agreement, a method of public discussion between the two users is performed using the syndrome from Hamming (7,3) binary codes. An algorithm is proposed to check the equality of generated keys for both legitimate users, and ensure error-free secure communication. The security of this algorithm has been verified by AVISPA. Comparisons are performed with previous work on secret key generation and it has been shown that this algorithm reliably generates longer secret keys in standard UWB radio channels.

The standard INFOSEC alert clustering algorithms use a predetermined fixed time threshold for specifying the maximum duration over which new INFOSEC alerts can be added to existing alert clusters. Since these alert clusters are the basis of further alert correlation processing, an important question is can this time threshold be set robustly, in the sense that an optimal value can be found that is independent of the attackers' actions. In this work the necessary conditions for such a robust threshold to be determined are derived. That these conditions cannot be assumed to hold for operational real world deployments of alert clustering systems in large scale networks is then discussed.

In this work an optimal algorithm is presented for bounding dependencies in coarsely quantized systems for the purposes of fault detection. Coarse quantization implies the need to analyze signals for which the standard /spl Delta//sup 2//12 approximation for quantization noise cannot be applied. This creates significant difficulties when it comes to retrofitting analytic fault detection and identification (FDI) approaches to existing large-scale engineering plants that have coarsely quantized status data, particularly in situations when no system models exist and/or it is cost-prohibitive to replace the exist status data sampling sub-systems. No methodology has been reported in the literature for optimal fault detection for the case of coarsely quantized signals. This work presents such an optimal approach. The theoretical results are operational validated by applying the algorithm to perform fault detection on one year's data obtained from a real-world large-scale engineering plant.