Fatma A. Omara

A generalized architecture of quantum secure direct communication for N disjointed users with authentication.

In this paper, we generalize a secured direct communication process between N users with partial and full cooperation of quantum server. So, N − 1 disjointed users u1, u2, …, uN−1 can transmit a secret message of classical bits to a remote user uN by utilizing the property of dense coding and Pauli unitary transformations. The authentication process between the quantum server and the users are validated by EPR entangled pair and CNOT gate. Afterwards, the remained EPR will generate shared GHZ states which are used for directly transmitting the secret message. The partial cooperation process indicates that N − 1 users can transmit a secret message directly to a remote user uN through a quantum channel. Furthermore, N − 1 users and a remote user uN can communicate without an established quantum channel among them by a full cooperation process. The security analysis of authentication and communication processes against many types of attacks proved that the attacker cannot gain any information during intercepting either authentication or communication processes. Hence, the security of transmitted message among N users is ensured as the attacker introduces an error probability irrespective of the sequence of measurement.

A Case Study for Deploying Applications on Heterogeneous PaaS Platforms

Cloud Platform-as-a-Service (PaaS) model provides developers with the ability to deploy and manage their applications remotely through the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS management and deployment, each PaaS provider has its own specific APIs (e.g., Google AppEngine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure). Therefore, deploying applications on heterogeneous PaaS platforms is considered one of the challenges that make some developers worry about using PaaS services. Such challenge can be solved by providing a standard or a generic API that overcomes PaaS API heterogeneity. The aim of this paper is to report on our effort to use and extend a generic API, namely the COAPS API, which supports deployment and management on Cloud Foundry and OpenShift. According to the work in this paper, an extension of the COAPS API is provided to include the deployment on Google AppEngine as a case study to demonstrate COAPS API generality.

Predictionźmechanisms forźmonitoringźstate of cloud resourcesźusing Markov chain model

Cloud computing allows for sharing computing resources, such as CPU, application platforms, and services. Monitoring these resources would benefit from an accurate prediction model that significantly reduces the network overhead caused by unnecessary push and pull messages. However, accurate prediction of the computing resources is considered hard due to the dynamic nature of cloud computing. In this paper, two monitoring mechanisms have been developed: the first is based on a Continuous Time Markov Chain (CTMC) model and the second is based on a Discrete Time Markov Chain (DTMC) model. It is found that The CTMC-based mechanism outperformed the DTMC-based mechanism. Also, the CTMC-based mechanism outperformed the Grid Resource Information Retrieval (GRIR) mechanism, which does not employ prediction, and a prediction-based mechanism, which uses Markov Chains to predict the time interval of monitoring mobile grid resources, in monitoring cloud resources.

Genetic-Based Task Scheduling Algorithm in Cloud Computing Environment

Nowadays, Cloud computing is widely used in companies and enterprises. However, there are some challenges in using Cloud computing. The main challenge is resource management, where Cloud computing provides IT resources (e.g., CPU, Memory, Network, Storage, etc.) based on virtualization concept and pay-as-you-go principle. The management of these resources has been a topic of much research. In this paper, a task scheduling algorithm based on Genetic Algorithm (GA) has been introduced for allocating and executing an application’s tasks. The aim of this proposed algorithm is to minimize the completion time and cost of tasks, and maximize resource utilization. The performance of this proposed algorithm has been evaluated using CloudSim toolkit.

Effective virtual machine configuration for cloud environment

Cloud computing is most widely increasing platform for task execution. On the other hands, virtual machine configuration plays as a critical role in the cloud computing performance. It is responsible to select the best suitable configuration for task execution by taking into consideration some static and dynamic factors of tasks. The aim of this paper is to study the effective of homogeneity and heterogeneity of the virtual machine configuration for assigning the tasks and satisfying the optimum time and cost. Our study has been evaluated over CloudSim toolkit under VM scheduling policies on virtual machine configuration.

OverCovert: Using Stack-Overflow Software Vulnerability to Create a Covert Channel

Abstract-Attackers exploit software vulnerabilities, such as stack overflow, heap overflow, and format string errors, to break into victim machines and implant backdoors to maintain access. They typically use obfuscation techniques, such as encryption and covert channels, to hide their command-and-control traffic and avoid detection. In this paper, we show how a vulnerable program can be used to create a covert channel that allows an entity (e.g., an attacker) to stealthily send information to another remote entity (e.g., a backdoor). The proposed covert channel, for which we coin the term OverCovert, is based on the common return-to-libc stack-overflow attack and the address space layout randomization defense. We implemented a proof-of-concept of OverCovert under Linux and evaluated its throughput sending files of different formats. We also propose and analyze techniques to improve channel undetectability and throughput.

Exploiting coarse-grained reused-based opportunities in Big Data multi-query optimization

Abstract Multi-query optimization in Big Data becomes a promising research direction due to the popularity of massive data analytical systems (e.g., MapReduce and Flink). The multi-query is translated into jobs. These jobs are routinely submitted with similar tasks to the underling Big Data analytical systems. These similar tasks are considered complicated and computation overhead. Therefore, there are some existing techniques that have been proposed for exploiting sharing tasks in Big Data multi-query optimization (e.g., MRShare and Relaxed MRShare). These techniques are heavily tailored relaxed optimizing factors of fine-grained reused-based opportunities. In accordance with Big Data multi-query optimization, the existing fine-grained techniques are only concerned with equal tuples size and uniform data distribution. These issues are not applicable to the real-world distributed applications which depend on coarse-grained reused-based opportunities, such as non-equal tuples size and non-uniform data distribution. These two issues receive more-attention in Big Data multi-query optimization, to minimize the data read from or written back to Big Data infrastructures (e.g., Hadoop). In this paper, Multi-Query Optimization using Tuple Size and Histogram (MOTH) system has been proposed to consider the granularity of the reused-based opportunities. The proposed MOTH system exploits the coarse-grained of the fully and partially reused-based opportunities among queries with considering non-equal tuples size and non-uniform data distribution to avoid repeated computations. According to the proposed MOTH system, a combined technique has been introduced for estimating the coarse-grained reused-based opportunities horizontally and vertically. The horizontal estimation of non-equal tuples size has been done by extracting metadata in column-level, while the vertical estimation of non-uniform data distribution is concerned with using pre-computed histogram in row-level. In addition, the MOTH system estimates the coarse-grained reused-based opportunities with considering slow storage (i.e., limited physical resources or fewer allocated virtualized resources) to produce the accurate estimation of the reused results costs. Then, a cost-based heuristic algorithm has been introduced to select the best reused-based opportunity and generate an efficient multi-query execution plan. Because the partial reused-based opportunities have been considered, extra computations are needed to retrieve the non-derived results. Also, a partial reused-based optimizer has been tailored and added to the proposed MOTH system to reformulate the generated multi-query plan to improve the shared partial queries. According to the experimental results of the proposed MOTH system using TPC-H benchmark, it is found that multi-query execution time has been reduced by considering the granularity of the reused results.

A key-agreement protocol based on the stack-overflow software vulnerability

Exploiting software vulnerabilities, such as stack overflow, heap overflow, and format string exploits, enables attackers to break into victim machines. Moreover, attackers tend to use obfuscation techniques, such as encryption, to evade intrusion detection systems. In this paper, we show that a common stack-overflow attack, namely the return-to-libc attack, coupled with a common defense, namely the Address Space Layout Randomization (ASLR), together allow for constructing a key-agreement protocol that allows two entities (e.g., a Trojan and a controller) to agree on a shared key, whereas the shared key can then be used to encrypt further communication. We have developed a prototype of our key-agreement protocol to evaluate its feasibility and performance. Our results show that both time and message overhead of our protocol are linear in key length. Although our key-agreement protocol can be used by attackers for malicious purposes, it has low computation overhead, making it a candidate for adoption in CPU-constrained platforms.

Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms

The cloud platform-as-a-service (PaaS) model provides developers with the ability to deploy and manage their applications remotely in the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS deployment and management; each PaaS provider [e.g., Google AppEngine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure] has its own proprietary APIs. This lack of standardisation adds a layer of complexity to application deployment and migration between heterogeneous PaaS platforms because of API incompatibility. A standard (generic) PaaS deployment API overcomes the previously mentioned PaaS API heterogeneity. A generic open-source API, namely the COAPS API, has been proposed to support deployment and management of applications on CF and OS PaaS platforms. This work implements COAPS deployment API to include the GAE PaaS. Whereas both CF and OS PaaS platforms use the same application packaging, deploying the same application on GAE requires application repackaging. We evaluated our work using a case study in which the same application is automatically deployed on CF and GAE.

An Improved Image Steganography Method Based on LSB Technique with Random Pixel Selection

with the rapid advance in digital network, information technology, digital libraries, and particularly World Wide Web services, many kinds of information could be retrieved any time. Thus, the security issue has become one of the most significant problems for distributing new information. It is necessary to protect this information while passing over insecure channels. Steganography introduces a strongly approach to hide the secret data in an appropriate media carriers such as images, audio files, text files, and video files. In this paper, a new image steganography method based on spatial domain is proposed. According to the proposed method, the secret message is embedded randomly in the pixel location of the cover image using Pseudo Random Number Generator (PRNG) of each pixel value of the cover image instead of embedding sequentially in the pixels of the cover image. This randomization is expected to increase the security of the system. The proposed method works with two layers (Blue and Green), as (2-1-2) layer, and the byte of the message will be embedded in three pixels only in this form (3-2-3). From the experimental results, it has found that the proposed method achieves a very high Maximum Hiding Capacity (MHC), and higher visual quality as indicated by the Peak Signal-to- Noise Ratio (PSNR).