Abidalrahman Moh'd

AES-512: 512-bit Advanced Encryption Standard algorithm design and evaluation

This paper presents an FPGA architecture for a new version of the Advanced Encryption Standard (AES) algorithm. The efficient hardware that implements the algorithm is also proposed. The new algorithm (AES-512) uses input block size and key size of 512-bits which makes it more resistant to cryptanalysis with tolerated area increase. AES-512 will be suitable for applications with high security and throughput requirements and with less chip area constrains such as multimedia and satellite communication systems. An FPGA architectural for AES-512 was developed using VHDL, and synthesized using Virtix-6 and Virtex-7 chips. AES-512 show tremendous throughput increase of 230% when compared with the implementation of the original AES-128.

C-Sec: Energy efficient link layer encryption protocol for Wireless Sensor Networks

In this paper, we introduce Compact-Security (C-Sec), an energy efficient link layer encryption protocol for Wireless Sensor Networks (WSNs). The protocol minimizes energy consumption by eliminating the need for transmitting all header and trailer fields related to security, while keeping security functions and services intact. Such fields include message authentication code, freshness counter, and source address. Our work relies on merging security related data with the essential headers of the next packet. This will dramatically reduce security related communication overhead. In addition, it includes a unique security feature that does not exist in any of the current protocols: hiding the packet header information. C-Sec is implemented using Very high speed integrated circuit Hardware Description Language (VHDL). Experimental results using synthesis for Spartan-6 low-power FPGA demonstrates that the proposed protocol outperforms related work in terms of computational time and energy consumption, in addition to the large savings in communication energy and bandwidth.

SN-SEC: a secure wireless sensor platform with hardware cryptographic primitives

Security was not considered when current wireless sensor nodes were designed. As a result, providing high level of security on current WSNs platforms is unattainable, especially against attacks based on key resolving and node compromise. In this paper, we scrutinize the security holes in current WSNs platforms and compare the main approaches to implementing their cryptographic primitives in terms of security, time, and energy efficiency. To secure these holes and provide more efficiency, we propose SN-SEC, a 32-bit RISC secure wireless sensor platform with hardware cryptographic primitives. The choice of cryptographic primitives for SN-SEC is based on their compatibility with the constrained nature of WSNs and their security. SN-SEC is implemented using very high-speed integrated circuit hardware description language. Experimental results using synthesis for Spartan-6 low-power FPGA show that the proposed design has a very reasonable computational time and energy consumption compared to well-known WSN processers.

Toward understanding how users respond to rumours in social media

As the spread of rumours has been increasing every day in online social networks (OSNs), it is important to analyze and understand this phenomenon. Damage caused by the spread of rumours is difficult to handle without a full understanding of the dynamics behind it. One of the central steps of understanding rumour spread is to analyze who spread rumours online, why, and how. In this research, we focus on the steps who and why by describing, implementing, and evaluating an approach that studies whether or not a group of users is actively involved in rumour discussions, and assesses rumour-spreading personality types in OSNs. We implement this general approach using Reddit data, and demonstrate its use by determining which users engage with a recurring rumour, and analyzing their comments using qualitative methods. We find that we can reliably classify users into one of three categories: (1) “Generally support a false rumour”, (2) “Generally refute a false rumour”, or (3) “Generally joke about a false rumour”. Combining text mining techniques, such as text classification, sentiment analysis, and social network analysis, we aim to identify and classify those rumour-spreading user categories automatically and provide a more holistic view of rumour spread in OSNs.

A semi-automated approach to transforming database schemas into ontology language

Transforming database schemas into an ontology language opens the door wide to the many advantages offered by the Semantic Web. Industries in particular can benefit from intelligent systems (e.g., decision-support systems) arising from such transformations. In this paper, we propose a semi-automated algorithm to transform data to the ontology language, OWL, while taking advantage of the actual data stored in a database schema. These data are used to discover hidden patterns in a database schema with a minimal level of human involvement. Such an approach also ensures improved mappings for relatively loosely structured database schema. The evaluation results on a simple DBLP database schema show improved effectiveness of such transformations.

Efficient Computation of Co-occurrence Based Word Relatedness

Measuring document relatedness using unsupervised co-occurrence based word relatedness methods is a processing-time and memory consuming task. This paper introduces the application of compact data structures for efficient computation of word relatedness based on corpus statistics. The data structure is used to efficiently lookup: (1) the corpus statistics for the Common Word Relatedness Approach, (2) the pairwise word relatedness for the Algorithm Specific Word Relatedness Approach. These two approaches significantly accelerate the processing time of word relatedness methods and reduce the space cost of storing co-occurrence statistics in memory, making text mining tasks like classification and clustering based on word relatedness practical.

A dual-mode energy efficient encryption protocol for wireless sensor networks

This paper presents a novel link-layer encryption protocol for wireless sensor networks. The protocol design aims to reduce energy consumption by reducing security related communication overhead. This is done by merging security related data of consecutive packets. The merging (or combining packets) based on simple mathematical operations helps to reduce energy consumption by eliminating the requirement to send security related fields in headers and trailers. We name our protocol as the Compact Security Protocol referred to as C-Sec. In addition to energy savings, the C-Sec protocol also includes a unique security feature of hiding the packet header information. This feature makes it more difficult to trace the flow of wireless communication, and helps to minimize the cost of defending against replay attacks. We performed rigorous testing of the C-Sec protocol and compared it with well-known protocols including TinySec, MiniSec, SNEP and Zigbee. Our performance evaluation demonstrates that the C-Sec protocol outperforms other protocols in terms of energy savings. We also evaluated our protocol with respect to other performance metrics including queuing delay and error probability.

Active High-Recall Information Retrieval from Domain-Specific Text Corpora based on Query Documents

In this paper, we propose a high recall active document retrieval system for a class of applications involving query documents, as opposed to key terms, and domain-specific document corpora. The output of the model is a list of documents retrieved based on the domain expert feedback collected during training. A modified version of Bag of Word (BoW) representation and a semantic ranking module, based on Google n-grams, are used in the model. The core of the system is a binary document classification model which is trained through a continuous active learning strategy. In general, finding or constructing training data for this type of problem is very difficult due to either confidentiality of the data, or the need for domain expert time to label data. Our experimental results on the retrieval of Call For Papers based on a manuscript demonstrate the efficacy of the system to address this application and its performance compared to other candidate models.

Integrating Global Attention for Pairwise Text Comparison

Attention guides computation to focus on important parts of the input data. For pairwise input, existing attention approaches tend to bias towards trivial repetitions (e.g. punctuations and stop words) between two texts, and thus failed to contribute reasonable guidance to model predictions. As a remedy, we suggest taking into account the corpus-level information via global-aware attention. In this paper, we propose an attention mechanism that makes use of intratext, inter-text and global contextual information. We undertake an ablation study on paraphrase identification, and demonstrate that the proposed attention mechanism can obviate the downsides of trivial repetitions and provide interpretable word weightings.

Post-Processing OCR Text using Web-Scale Corpora

We introduce a (semi-)automatic OCR post-processing system that utilizes web-scale linguistic corpora in providing high-quality correction. This paper is a comprehensive system overview with the focus on the computational procedures, applied linguistic analysis, and processing optimization.