Phishing, or web spoofing, is a growing problem: the Anti-Phishing Working Group (APWG) received almost 14,000 unique phishing reports in August 2005, a 56% jump over the number of reports in December 2004 [3]. For financial institutions, phishing is a particularly insidious problem, since trust forms the foundation for customer relationships, and phishing attacks undermine confidence in an institution. Phishing attacks succeed by exploiting a user's inability to distinguish legitimate sites from spoofed sites. Most prior research focuses on assisting the user in making this distinction; however, users must make the right security decision every time. Unfortunately, humans are ill-suited for performing the security checks necessary for secure site identification, and a single mistake may result in a total compromise of the user's online account. Fundamentally, users should be authenticated using information that they cannot readily reveal to malicious parties. Placing less reliance on the user during the authentication process will enhance security and eliminate many forms of fraud. We propose using a trusted device to perform mutual authentication that eliminates reliance on perfect user behavior, thwarts Man-in-the-Middle attacks after setup, and protects a user's account even in the presence of keyloggers and most forms of spyware. We demonstrate the practicality of our system with a prototype implementation.

Phoolproof Phishing Prevention

Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.

A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction

Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays by Maya B. Gokhale and Paul S. Graham Springer, 2005, 238 pp. ISBN-13 978-0387-26105-8, $87.20 Reconfigurable Computing Accelerating Computation with Field-Programmable Gate Arrays is an expository and easy to digest book. The authors are recognized leaders with many years of experience on the field of reconfigurable computing. The book is written so that non-specialists can understand the principles, techniques and algorithms. Each chapter has many excellent references for interested readers. It surveys methods, algorithms, programming languages and applications targeted to reconfigurable computing. Automatic generation of parallel code from a sequential program on conventional micro-processor architectures remains an open problem. Nevertheless, a wide range of computationally intensive applications have benefited from many tools developed to tackle such a problem. For RC, it is even a much harder problem (perhaps 10x and up) and intense research is being devoted to make RC a common-place practical tool. The aim of the authors is threefold. First, guide the readers to know current issues on HLL for RC. Second, help the readers understand the intricate process of algorithmic-to-hardware compilation. And third, show that, even though this process is painful, if the application is suitable for RC the gains in performance are huge. The book is divided into two parts. The first part contains four chapters about reconfigurable computing and languages. Chapter 1 presents an introduction of RC, contrasting conventional fixed instruction microprocessors with RC architectures. This chapter also contains comprehensive reference material for further reading. Chapter 2 introduces reconfigurable logic devices by explaining the basic architecture and configuration of FPGAs. Chapter 3 deals with RC systems by discussing how parallel processing is achieved on reconfigurable computers and also gives a survey of RC systems today. Then, in chapter 4, languages, compilation, debugging and their related manual vs. automatic issues are discussed. The second part of the book comprises five chapters about applications of RC. Chapter 5 and 6 discuss digital signal and image processing applications. Chapter 7 covers the application of RC to secure network communications. The aim of Chapter 8 is to discuss some important bioinformatics applications for which RC is a good candidate, their algorithmic problems and hardware implementations. Finally, Chapter 9 covers two applications of reconfigurable supercomputers. The first one is a simulation of radiative heat transfer and the second one models large urban road traffic. This book is neither a technical nor a text book, but in the opinion of this reviewer, it is an excellent state-of-the-art review of RC and would be a worthwhile acquisition by anyone seriously considering speeding up a specific application. On the downside, it is somewhat disappointing that the book does not contain more information about HLL tools that could be used to help close the gap between traditional HPC community and the raw computing power of RC. Edusmildo Orozco, Department of Computer Science, University Of Puerto Rico at Rio Piedras.

Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays

Using wireless sensor networks (WSNs) in delay and reliability critical environments is highly desired due to their unique advantages such as low cost, ease of deployment, and redundancy. However, successful deployment of resource-limited WSNs in such applications requires strict quality-of-service (QoS) provisioning techniques to meet the desired latency and reliability targets of those applications. Implementation of QoS techniques in WSNs is significantly challenging since WSNs have been initially devised for low-data-rate non-real-time applications. Therefore, WSN designers and developers resort to different cross-layer interaction and optimization techniques to provision QoS in WSNs. In this paper, we present a survey on the state of the art of cross-layer QoS approaches in wireless terrestrial sensor networks to achieve delay and reliability bounds in critical applications. Our paper provides a unique classification of cross-layer QoS approaches in WSNs that allows surveying a large amount of studies with utmost clarity. Furthermore, we highlight the main challenges of implementing QoS protocols in WSNs and present an overview of QoS-aware WSN applications.

A Survey on Cross-Layer Quality-of-Service Approaches in WSNs for Delay and Reliability-Aware Applications

Recent advances in wireless technologies, micro-electro-mechanical systems, and embedded systems enable the popular usage of the Internet of Things (IoT) and wireless sensor networks (WSNs) in many important applications, such as smart buildings, security, target tracking, industrial automation, and so on. Typically, a WSN consists of a large number of tiny, low-cost sensor nodes that are limited in terms of their capabilities of computation, communication, memory, and power. In WSNs, energy-efficient algorithms are of paramount importance for a long lasting high throughput network. MAC protocol plays a prominent role in extending the life-time of WSNs. MAC protocols provide various schemes on how multiple nodes access a common wireless medium. To achieve a longer lifetime for the nodes and the networks, MAC protocols need to be energy-efficient and reduce the sources of energy wastage. Energy conservation in sensor nodes is generally achieved by duty cycling the radios, and it is the MAC layer protocol that controls when to switch on and off the radio. In this paper, we discuss the essential properties of MAC protocols, the MAC for IoT and the common causes of energy consumptions. Thereafter, we categorize the MAC layer protocols and discuss several protocols under each category in depth, emphasizing their strengths and weaknesses, and giving a detailed comparison of MAC protocols. Finally, we conclude the survey with the insights on future research directions.

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A Comprehensive Study of IoT and WSN MAC Protocols: Research Issues, Challenges and Opportunities

Any complex application can be realized as a graph of dependent tasks, and scheduling these tasks onto a limited number of computational resources while satisfying their dependencies is a well-known NP-complete optimization problem. For microprocessor systems, several algorithms have been proposed that can efficiently find suboptimal schedules. A solution for dynamically reconfigurable hardware (DRHW), however, is more complicated, as the time and complexity of reconfiguration has to be scheduled as well. The reconfiguration overhead in these systems is significant, and quickly becomes a crucial factor in real-world applications. In this paper, a meta-heuristic method known as Feasibility Assured TSP-likened Scheduling (FATS) is proposed in which the scheduling problem is translated into a construction graph, similar to Travelling Sales Person (TSP) problem, such that it would be able to benefit from the advantages of Ant Colony Optimization (ACO) algorithm. Moreover, by exploiting such a construction graph, precedence constraints and system limitations are satisfied beforehand, the feasibility of solutions is assured, while avoiding the costly solution repair operations. To demonstrate the performance of the proposed method, it was tested on several synthetic and real-world benchmark task graphs and the results were compared with a selection of classic and state-of-the-art algorithms. A comprehensive set of experiments was performed to evaluate the method in terms of efficiency, execution time, scalability and reliability. In brief, the results of experiments on benchmarks showed that on average FATS outperforms HPSO-GA and BGA by 8.4 % and 12.2 % respectively in terms of the quality of the solutions, and its run-time is far less than the state-of-the-art algorithms. Also, on synthetic graphs the makespan improvements of the solutions generated by FATS and GA over the List scheduler are on average 11.2 % and 6.8 % better respectively; and from the execution-time point of view, our method is 27.37 % faster than GA. Moreover, the results confirm that the proposed method is scalable for large task graphs and its reliability is superior to other compared algorithms.

An efficient ACO-based algorithm for scheduling tasks onto dynamically reconfigurable hardware using TSP-likened construction graph

Tracking moving objects with Wireless Sensor Networks (WSNs) has received much concern because of its practical applications in nowadays world. Tracking accuracy is deeply dependant upon tracking time interval of the tracking network. This paper proposes a new adaptive power optimization scheme for target tracking sensor networks. This scheme includes a Fast Dynamic Adaptive Prediction-based Tracking (FDAPT) algorithm to dynamically modify the tracking time interval such that it minimizes the power consumption while keeping the required accuracy. The simulation results show that using the proposed scheme the network has a good performance with the added advantage of reducing the power consumption significantly when compared to previous non-adaptive tracking schemes. Moreover, simulation results show that the performance of the proposed scheme is better than one of existing adaptive methods with respect to power consumption (up to 10%) and tracking accuracy (up to 4%).

A new adaptive power optimization scheme for target tracking Wireless Sensor Networks

Reconfiguration overhead is an important obstacle that limits the performance of on-line scheduling algorithms in reconfigurable computing systems and increases the overall execution time. Configuration reusing (task reusing) can decrease reconfiguration overhead considerably, particularly in periodic applications. In this paper, we present a new approach for on-line scheduling and placement in which configuration reusing is considered as a main characteristic in order to reduce reconfiguration overhead and decrease total execution time of the tasks. A large variety of experiments have been conducted on the proposed algorithm. Obtained results show considerable improvement in overall execution time of the tasks.

A New approach in on-line task scheduling for reconfigurable computing systems

Science ecosystem has been evolved during modern human life. Teaching, learning, education, and doing research connecting elements of this ecosystem include students, professors, administrators and researchers from academia and industry. Different learning systems have been developed in different countries results in various science ecosystems that interact with each other. While the main goals are the same and there are many collaborations between them, but their reactions are different during the world paradigm shifts such as ICTs, IoTs and the Internet revolution and recently emerged Covid-19 Pandemic. In this paper we are going to study and share some experiences of different education systems. This may help developed and developing courtiers to tune current or determine new strategies, especially when the world faces with the crisis of Covid-19 Pandemic. We will discuss about the digital transformation in academic society and innovative ecosystems in the world beyond Covid19-Pandemic by using 7PS model and the 5th wave theory.

Digital Transformation in Academic Society and Innovative Ecosystems in the World beyond Covid19-Pandemic with Using 7PS Model for IoT

Congestion-aware routing algorithms improve the network performance by selecting less congested paths between source and destination routers. In this paper, a Global Congestion-Aware Routing Algorithm (G-CARA) is proposed for traffic management in three-dimensional Networks-on-Chip (3D NoCs). The proposed method forwards data packets efficiently toward regions with lower traffic to avoid network congestion. This is done by two steps. At first, the algorithm collects global congestion information in each layer of the network, and then, the algorithm utilizes the incoming information to specify an optimal path for sending a packet based on dimension-order routing. Global congestion values are propagated by embedded traffic information in header flit of packets. Furthermore, turn models are used to guarantee deadlock-freedom in proposed routing algorithm. Experimental results show that G-CARA can achieve an improvement in both saturation point and average latency within the range of 14%–22% and 42%–61% on average, respectively.

Hadi Shahriar Shahhoseini

Papers

An efficient ACO-based algorithm for scheduling tasks onto dynamically reconfigurable hardware using TSP-likened construction graph

A new adaptive power optimization scheme for target tracking Wireless Sensor Networks

A New approach in on-line task scheduling for reconfigurable computing systems

Digital Transformation in Academic Society and Innovative Ecosystems in the World beyond Covid19-Pandemic with Using 7PS Model for IoT

G-CARA: A Global Congestion-Aware Routing Algorithm for traffic management in 3D networks-on-chip