A Methodology for Engineering Collaborative and ad-hoc Mobile Applications using SyD Middleware

Today’s web applications are more collaborative and utilize standard and ubiquitous Internet protocols. We have earlier developed System on Mobile Devices (SyD) middleware to rapidly develop and deploy collaborative applications over heterogeneous and possibly mobile devices hosting web objects. In this paper, we present the software engineering methodology for developing SyD-enabled web applications and illustrate it through a case study on two representative applications: (i) a calendar of meeting application, which is a collaborative application and (ii) a travel application which is an ad-hoc collaborative application. SyD-enabled web objects allow us to create a collaborative application rapidly with limited coding effort. In this case study, the modular software architecture allowed us to hide the inherent heterogeneity among devices, data stores, and networks by presenting a uniform and persistent object view of mobile objects interacting through XML/SOAP requests and responses. The performance results we obtained show that the application scales well as we increase the group size and adapts well within the constraints of mobile devices

Demonstrating the Threat of Hardware Trojans in Wireless Sensor Networks

As the demand for cheaper electronic devices has increased, the location of manufacturing foundries has changed, sometimes to untrusted places in foreign countries. Some of these locations have limited oversight of the manufacturing of complicated and sensitive electronic components including integrated circuits (ICs). The integrated circuits are key component in all current electronic devices and can be modified to be malicious or to monitor the functions of their applications. These malicious modifications on the ICs are called hardware trojans (HWTs). HWTs an be designed to quietly monitor, to actively send out sensitive information, or to destroy their host device completely. The idea of hardware trojans in Wireless Sensor Networks (WSNs) has not been investigated before; thus, our goal is to demonstrate the potential threat that hardware trojans pose for sensor networks. This is important to study, given that in WSNs hundreds of sensors are deployed and in most cases left unattended, which gives the opportunity to an attacker to trigger a HWT on the sensors. For our investigation, we used TelosB sensors that have been used for some WSN applications. An attacker in a network can, for example, take advantage of the SPI bus that is used by the radio to eavesdrop messages and even disrupt communications completely. Currently, security breaches through software is given great importance in the WSN academic and research community. Our research shows that the same level of importance must be given to attacks through hardware to ensure a trusted and secure network

Simulations and Algorithms on Reconfigurable Meshes With Pipelined Optical Buses.

Recently, many models using reconfigurable optically pipelined buses have been proposed in the literature. A system with an optically pipelined bus uses optical waveguides, with unidirectional propagation and predictable delays, instead of electrical buses to transfer information among processors. These two properties enable synchronized concurrent access to an optical bus in a pipelined fashion. Combined with the abilities of the bus structure to broadcast and multicast, this architecture suits many communication-intensive applications. We establish the equivalence of three such one-dimensional optical models, namely the LARPBS, LPB, and POB. This implies an automatic translation of algorithms (without loss of speed or efficiency) among these models. In particular, since the LPB is the same as an LARPBS without the ability to segment its buses, their equivalence establishes reconfigurable delays (rather than segmenting ability) as the key to the power of optically pipelined models. We also present simulations for a number of two-dimensional optical models and establish that they possess the same complexity, so that any of these models can simulate a step of one of the other models in constant time with a polynomial increase in size. Specifically, we determine the complexity of three two-dimensional optical models (the PR-Mesh, APPBS, and AROB) to be the same as the well known LR-Mesh and the cycle-free LR-Mesh. We develop algorithms for the LARPBS and PR-Mesh that are more efficient than existing algorithms in part by exploiting the pipelining, segmenting, and multicasting characteristics of these models. We also consider the implications of certain physical constraints placed on the system by restricting the distance over which two processors are able to communicate. All algorithms developed for these models assume that a healthy system is available. We present some fundamental algorithms that are able to tolerate up to N/2 faults on an N-processor LARPBS. We then extend these results to apply to other algorithms in the areas of image processing and matrix operations

Improving Feature Selection Techniques for Machine Learning

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases

Attacking and securing beacon-enabled 802.15.4 networks

The IEEE 802.15.4 standard has attracted timecritical applications in wireless sensor networks because of its beacon-enabled mode and guaranteed timeslots (GTSs). However, the GTS management scheme’s security mechanisms still leave the 802.15.4 medium access control vulnerable to attacks. Further, the existing techniques in the literature for securing 802.15.4 networks either focus on nonbeacon-enabled 802.15.4 networks or cannot defend against insider attacks for beacon-enabled 802.15.4 networks. In this paper, we illustrate this by demonstrating attacks on the availability and integrity of the beaconenabled 802.15.4 network. To confirm the validity of the attacks, we implement the attacks using Tmote Sky motes for wireless sensor nodes, where the malicious node is deployed as an inside attacker. We show that the malicious node can freely exploit information retrieved from the beacon frames to compromise the integrity and availability of the network. To defend against these attacks, we present BCN-Sec, a protocol that ensures the integrity of data and control frames in beacon-enabled 802.15.4 networks. We implement BCN-Sec, and show its efficacy during various attacks

Classifying the Clique-Width of H-Free Bipartite Graphs

Let G be a bipartite graph, and let H be a bipartite graph with a fixed bipartition (B H ,W H ). We consider three different, natural ways of forbidding H as an induced subgraph in G. First, G is H-free if it does not contain H as an induced subgraph. Second, G is strongly H-free if G is H-free or else has no bipartition (B G ,W G ) with B H  ⊆ B G and W H  ⊆ W G . Third, G is weakly H-free if G is H-free or else has at least one bipartition (B G ,W G ) with TeX or TeX. Lozin and Volz characterized all bipartite graphs H for which the class of strongly H-free bipartite graphs has bounded clique-width. We extend their result by giving complete classifications for the other two variants of H-freeness

Towards an Experimental Testbed to Study Cyber Worm Behaviors in Large Scale Networks

A worm is a malicious agent that propagates across networks of devices creating negative impacts on the devices it is able to reach and infect. Currently, there is very limited information in cybersecurity research regarding worm behavior across real networks of devices, particularly in large scale networks (e.g. campus networks, office networks, IoT etc.). This paper positions an experimental testbed that can be used for studying worm behaviors in large scale networks. In particular, this research aims to setup an infrastructure to empirically study worm generation, propagation, attacks, policies and antidote (intervention) mechanisms through a unified experimental testbed. As a preliminary step towards this goal, this paper presents a case study of an empirical study of the behavior of a worm that attacks through IP address routing in a campus network. Through a 10 node set up where Raspberry Pis are used to emulate a user device in the campus network, we show how a simple worm that uses an exhaustive sequential and/or random selection of IP can lead to infecting devices in ways which can be challenging to track in reality. We also infer that through extensive experimentation it could be possible to develop prediction models for the attack patterns, based on the behavior patterns observed in the experiments

Upper Bound Analysis and Routing in Optical Benes Networks

Multistage Interconnection Networks (MIN) are popular in switching and communication applications. It has been used in telecommunication and parallel computing systems for many years. The new challenge facing optical MIN is crosstalk, which is caused by coupling two signals within a switching element. Crosstalk is not too big an issue in the Electrical Domain, but due to the stringent Bit Error Rate (BER) constraint, it is a big major concern in the Optical Domain. In this research dissertation, we will study the blocking probability in the optical network and we will study the deterministic conditions for strictly non-blocking Vertical Stacked Optical Benes Networks (VSOBN) with and without worst-case scenarios. We will establish the upper bound on blocking probability of Vertical Stacked Optical Benes Networks with respect to the number of planes used when the non-blocking requirement is not met. We will then study routing in WDM Benes networks and propose a new routing algorithm so that the number of wavelengths can be reduced. Since routing in WDM optical network is an NP-hard problem, many heuristic algorithms are designed by many researchers to perform this routing. We will also develop a genetic algorithm, simulated annealing algorithm and ant colony technique and apply these AI algorithms to route the connections in WDM Benes network

Distributed and asynchronous data collection in cognitive radio networks with fairness consideration

Abstract-As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay