The performance of large-scale Long-Range Wide Area Network (LoRaWAN) deployments is typically evaluated using conventional link metrics such as distance, transmit power, and spreading factor. However, the role of environmental variables has remained largely unexplored, despite their potential to significantly alter signal quality. In this paper, we introduce aclimate-aware modeling frameworkthat quantifies and predicts how temperature and humidity affect the signal-to-noise ratio (SNR) across heterogeneous IoT deployment scenarios. Using a city-scale dataset from more than 30,000 sensors, the proposed framework integrates statistical characterization (PDF/CDF and outage modeling) with a predictive learning component based on gradient-boosted decision trees, forming a unified modeling pipeline that links environmental variability, SNR distributional behavior, and predictive response. The framework is evaluated across three representative topologies—Free, Covered, and Underground—providing a structured understanding of environment-conditioned reliability in LoRaWAN networks. Our results show that temperature and humidity induce measurable shifts in SNR distributions, and that incorporating them into the predictive model yields consistent accuracy gains, improving test-set performance by up to Δr2= 0.445 over signal-only baselines (depending on topology and SF). Beyond prediction accuracy, the model’s outputs can be directly integrated into Adaptive Data Rate (ADR) management and network planning, demonstrating its applicability for system-level IoT optimization. These findings demonstrate that climate-aware modeling is not only relevant but essential for reliable Internet of Things (IoT) connectivity, offering new directions for adaptive LoRaWAN network planning and deployment in diverse urban environments.

In the smart grid, data communication between smart meters and utility servers should be authentic, private, have integrity while being accessible. To mitigate the risks of potential attacks, securing these two-way communications is crucial. Equally important is maintaining near real-time communication and avoiding significant delays when extra security levels are involved. Existing research on smart grids has not simultaneously tackled the issues of security, communication speed, and network scalability. In this work, we propose a novel delay-optimized blockchain solution for securing cryptographic communication between consumers and the utility in a smart grid. Our solution, based on EOS smart contracts, Edge computing, asymmetric Cryptographic functions, and Group signatures ($E E C G$), treats data communication as transactions that are asymmetrically encrypted and signed in groups before being stored on the EOS blockchain, ensuring confidentiality, privacy, availability, and low cost. The use of edge computing reduces the computational burden of smart meters, increases transaction speed, enhances data privacy, and improves scalability. Furthermore, an optimization problem for associating smart meters with edge nodes is formulated to minimize data exchange and processing delays over the blockchain, facilitating near real-time secure data access.

Long Range Wide Area Network (LoRaWAN) has emerged as a key enabler for Internet of Things (IoT) applications in smart cities, offering long-range connectivity with low power consumption. This study evaluates the performance of a large-scale operational LoRaWAN network deployed in Pafos, Cyprus, consisting of over 30,000 smart water meters. Leveraging twelve months of real-world data, we apply regression-based machine learning models (Decision Trees and XGBoost) to predict Estimated Signal Power (ESP), enabling enhanced network reliability and efficiency. Unlike prior studies that primarily rely on simulations or small-scale testbeds, this work is the first to analyze large-scale real-world LoRaWAN data, uncovering key environmental factors influencing signal propagation. Furthermore, we introduce a novel software tool that integrates machine learning-driven ESP predictions, allowing for automated network optimization. Unlike conventional models, this tool dynamically refines signal power predictions based on empirical data, making it a valuable asset for smart city planning and large-scale IoT deployments. To the best of our knowledge, this study represents the first comprehensive analysis focused on the features influencing and predicting ESP. The results demonstrate that our approach effectively tailors propagation predictions to urban environments, achieving high predictive accuracy across diverse conditions. This work contributes to the optimization of large-scale IoT deployments, paving the way for scalable and reliable smart city applications.

Stronger defenses against adversarial attacks are subsequently broken by a more advanced defense aware attack. We propose a stronger defense to achieve state-of the-art detection performance on both standard and defense-aware attacks. To this end, we propose a new architecture, called the Residual Vision Transformer (RVT) for attack based-image classification. RVT introduces the Residual Networks to Vision Transformers (ViT) to improve ViT in performance and efficiency and yield the best of both designs. The classic architecture of ViT is mainly modified by: (i) a hierarchy of Transformers containing a new residual token embedding, and (ii) a residual Transformer block leveraging a residual projection. Moreover, the positional encoding, a crucial component in existing vision transformers, can be safely removed in the RVT model, simplifying the design for higher resolution vision tasks. In this paper, the RVT model is evaluated for its robustness against adversarial attacks and is found to perform great in detecting adversarial images. Extensive experiments conducted on CIFAR-10 and SVHN datasets, show that our proposed deep learning algorithm significantly outperforms the state-of-the-art performance over other Vision Transformers and ResNets. RVT is first pre-trained on larger datasets (e.g. ImageNet-22k) and then fine-tuned to downstream tasks achieving a top-1 accuracy of 89.3% proving that the RVT model is a promising approach for image classification offering improved performance, efficiency, and robustness against adversarial attacks. To make the results reproducible, the code, the used data and details of the experimental setup are made available online at ">https://github.com/Robotoks/RVT .

Smart grids are suscceptible to security vulnerabilities of cyber-physical systems due to the heterogeneity of their interconnected components. There are high risks associated with potential attacks targeting the two-way communication between the smart meters and the utility servers. It is vital to ensure that data communicated between consumers and the utility is not tampered with and is authentic, private, and available. Conventional security measures in traditional communication and network systems fail to secure the data communication aspect in the complex network that composes the advanced metering infrastructure (AMI). In this work, we propose ECC: a novel prevention approach based on Ethereum smart contracts, asymmetric cryptographic functions, and cloud services for securing the two-way communication between smart meters and utility servers. Ethereum blockchain is utilized as a building block where communicated data is treated as transactions encrypted and stored in a distributed fashion to ensure data availability, confidentiality, and privacy. We also augment the Ethereum architecture with cloud services to extend the number of allowable transactions, ensure the availability of the electricity data, and reduce the cost associated with Ethereum transactions. The conducted experiments illustrate the efficacy of ECC in terms of the achieved security properties. This paper shows that the Ethereum Blockchain coupled with Cloud services can improve the efficiency of a system solely based on the Ethereum Blockchain.

Learning response generation models constitute the main component of building open-domain dialogue systems. However, training open-domain response generation models requires large amounts of labeled data and pre-trained language generation models that are often nonexistent for low-resource languages. In this article, we propose a framework for training open-domain response generation models in low-resource settings. We consider Dialectal Arabic (DA) as a working example. The framework starts by warm-starting a transformer-based encoder-decoder with pre-trained language model parameters. Next, the resultant encoder-decoder model is adapted to DA by employing self-supervised pre-training on large-scale unlabeled data in the desired dialect. Finally, the model is fine-tuned on a very small labeled dataset for open-domain response generation. The results show significant performance improvements on three spoken Arabic dialects after adopting the framework’s three stages, highlighted by higher BLEU and lower Perplexity scores compared with multiple baseline models. Specifically, our models are capable of generating fluent responses in multiple dialects with an average human-evaluated fluency score above 4. Our data is made publicly available.

Dialogue generation is the automatic generation of a text response, given a user’s input. Dialogue generation for low-resource languages has been a challenging tasks for researchers. However, the advancements in deep learning models have made developing conversational agents that perform the tasks of dialogue generation not only possible, but also effective and helpful in many applications spanning a variety of domains. Nevertheless, work on conversational bots for low-resource languages such as the Arabic language is still limited due to various challenges, including the language structure, vocabulary, and the scarcity of its data resources. Meta-learning has been introduced before in the natural language processing (NLP) realm and showed significant improvements in many tasks; however, it has rarely been used in natural language generation (NLG) tasks and never in Arabic NLG. In this work, we propose a meta-learning approach for Arabic dialogue generation for fast adaptation on low-resource domains, namely, Arabic. We start by using existing pre-trained models; we then meta-learn the initial parameters on high-resource dataset before finetuning the parameters on the target tasks. We prove that the proposed model that employs meta-learning techniques improves generalization and enables fast adaptation of the transformer model on low-resource NLG tasks. We report gains in the BLEU-4 and improvements in Semantic textual Similarity (STS) metrics when compared to the existing state-of-the-art approach. We also do a further study on the effectiveness of the meta-learning algorithms on the response generation of the models.

With the advances in Natural Language Processing (NLP), the industry has been moving towards human-directed artificial intelligence (AI) solutions. Recently, chatbots and automated news generation have captured a lot of attention. The goal is to automatically generate readable text from tabular data or web data commonly represented in Resource Description Framework (RDF) format. The problem can then be formulated as Data-to-text (D2T) generation from structured non-linguistic data into human-readable natural language. Despite the significant work done for the English language, no efforts are being directed towards low-resource languages like the Arabic language. This work promotes the development of the first RDF data-to-text (D2T) generation system for the Arabic language while trying to address the low-resource limitation. We develop several models for the Arabic D2T task using transfer learning from large language models (LLM) such as AraBERT, AraGPT2, and mT5. These models include a baseline Bi-LSTM Sequence-to-Sequence (Seq2Seq) model, as well as encoder-decoder transformers like BERT2BERT, BERT2GPT, and T5. We then provide a detailed comparative study highlighting the strengths and limitations of these methods setting the stage for further advancement in the field. We also introduce a new Arabic dataset (AraWebNLG) that can be used for new model development in the field. To ensure a comprehensive evaluation, general-purpose automated metrics (BLEU and Perplexity scores) are used as well as task-specific human evaluation metrics related to the accuracy of the content selection and fluency of the generated text. The results highlight the importance of pre-training on a large corpus of Arabic data and show that transfer learning from AraBERT gives the best performance. Text-to-text pre-training using mT5 achieves second best performance results even with multilingual weights.

The high need of real-time vehicular applications for self-driving cars to maintain service availability and reachability, and to process huge amount of generated data within a small amount of time, rise the need to improve the vehicular network infrastructure. Fog computing has been introduced to reduce the amount of data sent to cloud by bringing processing power near the edge and reducing latency. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Reinforcement Learning, Kubeadm Clustering, Docker Containerization, Istio service mesh, and micro-services technologies. We propose in our scheme (1) a service mesh architecture that manages communication between multiple micro services across different clusters and tackle inter-service communication, (2) a Reinforcement Learning model deployed on RSUs to predict on-demand placement of microservices. Experiments and simulations show that our method is more efficient in deploying microservices using the reinforcement learning model than other current strategies in the literature. Given that only needed microservices are deployed in limited resource cluster, mesh network shows an improvement in the deployment time and inter-service communication across clusters.

Smart grids are being continually adopted as a replacement of the traditional power grid systems to ensure safe, efficient, and cost-effective power distribution. The smart grid is a heterogeneous communication network made up of various devices such as smart meters, automation, and emerging technologies interacting with each other. As a result, the smart grid inherits most of the security vulnerabilities of cyber systems, putting the smart grid at risk of cyber-attacks. To secure the communication between smart grid entities, namely the smart meters and the utility, we propose in this paper a communication infrastructure built on top of a blockchain network, specifically Ethereum. All two-way communication between the smart meters and the utility is assumed to be transactions governed by smart contracts. Smart contracts are designed in such a way to ensure that each smart meter is authentic and each smart meter reading is reported securely and privately. We present a simulation of a sample smart grid and report all the costs incurred from building such a grid. The simulations illustrate the feasibility and security of the proposed architecture. They also point to weaknesses that must be addressed, such as scalability and cost.

In this paper, we address the problem of recognizing the semantic human activities through the analysis of large dataset collected from users’ sensor-based smartphones. Our approach is unique in terms of covering a large number of activities that users could possibly engage in, and considering the multi-level-based classification model. Our model has three properties that never seemed to be addressed by existing approaches dealing with the same problem. These are: (1) comprehensiveness — in terms of the activity set, (2) accuracy — in terms of the activity classification, and (3) applicability — in terms of flexibility in being applied in real-life settings. Current approaches do not tackle all these properties. When tested on realistic dataset, our multi-level-based model achieved promising results despite the large number of activities being considered. When compared to similar approaches, our approach achieved comparable results in terms of accuracy and outperformed them in terms of the activity types, environment and settings covered, comprehensiveness, and applicability.

Observing the headway in vehicular industry, new applications are developed demanding more resources. For instance, real-time vehicular applications require fast processing of the vast amount of generated data by vehicles in order to maintain service availability and reachability while driving. Fog devices are capable of bringing cloud intelligence near the edge, making them a suitable candidate to process vehicular requests. However, their location, processing power, and technology used to host and update services affect their availability and performance while considering the mobility patterns of vehicles. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Kubeadm Clustering, Docker Containerization, and micro-services technologies. In this context, we propose an efficient resource and context aware approach for deploying containerized micro-services on on-demand fogs called Vehicular-OBUs-As-On-Demand-Fogs. Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. An Evolutionary Memetic Algorithm is elaborated to solve our vehicular container placement problem. Experiments and simulations demonstrate the relevance and efficiency of our approach compared to other recent techniques in the literature.

The integration of sensors and units in vehicle manufacturing is constantly increasing the data volume generated by vehicles. This requires to support services to handle these data and provide a continuous response to application requests such as collision warnings, lane changing, traffic information, routing information, multimedia streaming, and many others. Add to that the need for achieving the required quality of service is of immense importance. Fog devices deployed along the road are used as a solution to service vehicles. Vehicular ad hoc network (VANET) environment have the property of dynamically changing its cluster connectivity because of the different mobility patterns, which makes it a challenge for vehicular users to access their services. Many preliminary works proposed the use of Software Defined Wireless Network (SDWN) in VANET. In this paper, we study and analyze the use of SDWN in vehicular fog computing environment taking into consideration the suspected high delay between the SDWN controller that is located on the cloud and its switches, the high traffic coming to the controller, the high vehicle speed, and the range of RSU coverage. We have used Mininet-Wifi to implement a vehicular fog computing architecture and simulate different scenarios. Obtained results showed that using SDWN in a VANET environment might not be the best solution in many cases.

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

When downloading an application by a smartphone user, the application asks the user to accept a set of permissions allowing it to access sensitive information on the phone. It is mostly unclear how, when, and why such data will be used. openPDS is a framework that was proposed to limit such privacy invasion. Although openPDS protects the user's raw data, sensitive personal information, such as location trace, can still be inferred by the service provider by analyzing the accumulated answers. In this paper, we aim to append openPDS with a module that prevents the service provider from reconstructing the trace of users. This module abides with the QoS requirements necessitated by the provider, and defined as the tolerance of the application to inaccurate answers. Our approach was tested on 10 users whose locations traces were recorded for 10 months. Results show that no user trace was successfully reconstructed even when high QoS levels were required.

With the rise the of the Internet of Things (IoT) the Routing Protocol for Low Power and Lossy Networks (RPL) gained a lot of interest in the research community mainly for its flexibility to cope with different network topologies and its ability to offer features like Auto-Configuration, Self-Healing, Loop avoidance and detection, etc. Based on certain routing metrics, RPL's Objective Function (OF) assigns ranks to the nodes in the network then selects and optimizes the routes. This paper overviews the most used objective functions then proposes a modification on the Minimum Rank with Hysteresis Objective Function (MRHOF) which takes into consideration two metrics instead of one, to get more reliable and optimized routes.

Long Term Evolution (LTE) was developed by 3GPP to cope with the increasing demand for better Quality of service (QoS) and the emergence of bandwidth-consuming multimedia applications. Today's data transmission networks face extreme challenges in providing high data rate and low latency. Scheduling paradigms such as Round Robin, Best Channel Quality Condition and Proportional Fair are commonly adopted in current LTE downlink scheduling algorithms, but they are far from optimal for satisfying latency requirements. In this paper, we first survey the state of the art downlink scheduling algorithms in LTE and identify their main challenges. We then formulate the LTE downlink scheduling problem as an optimization problem in order to meet the flow deadlines, then incorporate the formulation within the surveyed scheduling algorithms, to produce better performance. We consider strict deadlines for different types of packets with the goal of maximizing resource distribution. Additionally, in our formulation the buffer state for each user is taken into consideration in order to minimize the packet loss. We evaluate the proposed formulation using LTE-Sim and study its positive impact on the existing LTE downlink scheduling algorithms; the performance in terms of QoS, packet loss and fairness is improved throughout all evaluations.

Accurate sentiment analysis models encode the sentiment of words and their combinations to predict the overall sentiment of a sentence. This task becomes challenging when applied to morphologically rich languages (MRL). In this article, we evaluate the use of deep learning advances, namely the Recursive Neural Tensor Networks (RNTN), for sentiment analysis in Arabic as a case study of MRLs. While Arabic may not be considered the only representative of all MRLs, the challenges faced and proposed solutions in Arabic are common to many other MRLs. We identify, illustrate, and address MRL-related challenges and show how RNTN is affected by the morphological richness and orthographic ambiguity of the Arabic language. To address the challenges with sentiment extraction from text in MRL, we propose to explore different orthographic features as well as different morphological features at multiple levels of abstraction ranging from raw words to roots. A key requirement for RNTN is the availability of a sentiment treebank; a collection of syntactic parse trees annotated for sentiment at all levels of constituency and that currently only exists in English. Therefore, our contribution also includes the creation of the first Arabic Sentiment Treebank (A r S en TB) that is morphologically and orthographically enriched. Experimental results show that, compared to the basic RNTN proposed for English, our solution achieves significant improvements up to 8% absolute at the phrase level and 10.8% absolute at the sentence level, measured by average F1 score. It also outperforms well-known classifiers including Support Vector Machines, Recursive Auto Encoders, and Long Short-Term Memory by 7.6%, 3.2%, and 1.6% absolute respectively, all models being trained with similar morphological considerations.

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A R ecursive Deep Learning Model for O pinion M ining in A rabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.

In LTE, tracking Areas (TAs) are used to group cells where each cell is assigned to one TA and each user equipment (UE) registers with one TA. A TA may be connected to more than one mobility management entity (MME), which records the current TAs of its UEs. The home subscriber server (HSS) records the current MME for each UE. When a UE moves to a new TA, a TA update procedure is triggered to update the MME in case of an intra-MME move and the HSS in case of an inter-MME move. When the UE is called, the paging procedure is triggered to determine the UE's current cell, by broadcasting a paging message in all cells of the UE's current TA. The smaller the TAs are, the smaller the number of cells needed to be paged but more often TA updates. On the other hand with a larger TA, the paging cost increases and the TAU signaling decreases. Hence designing TAs is a problem as it affects both paging and TAU signalling cost. An initial optimal TA configuration cannot guarantee a low signaling overhead because the UEs mobility might alter their distribution. Therefore, the TAs need to be reconfigured to account for the new distribution of UEs. In this paper, we use the Integer Programming Model to solve the TAs reconfiguration problem in LTE networks. We then evaluate the performance of the proposed approach and compare it to the tabu search and the genetic algorithm based solutions to study how far from optimality those solutions are.

SIP is amongst the most popular Voice over IP signaling protocols. Its deployment in live scenarios showed its vulnerability to flooding attacks. In this paper, we present a SIP flooding attack detection mechanism that dynamically detects SIP flooding attacks and correlates in real time the temporal characteristics of SIP reliable mechanism and the number of received INVITE requests. Experimental results show that the proposed mechanism is able to detect SIP flooding rapidly and does not suffer from false alarms. When compared to other similar approaches in literature, the proposed approach outperformed the other approaches in terms of detections speed and accuracy.

This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.

Huge amounts of data and personal information are being sent to and retrieved from web applications on daily basis. Every application has its own confidentiality and integrity policies. Violating these policies can have broad negative impact on the involved company's financial status, while enforcing them is very hard even for the developers with good security background. In this paper, we propose a framework that enforces security-by-construction in web applications. Minimal developer effort is required, in a sense that the developer only needs to annotate database attributes by a security class. The web application code is then converted into an intermediary representation, called Extended Program Dependence Graph (EPDG). Using the EPDG, the provided annotations are propagated to the application code and run against generic security enforcement rules that were carefully designed to detect insecure information flows as early as they occur. As a result, any violation in the data's confidentiality or integrity policies is reported. As a proof of concept, two PHP web applications, Hotel Reservation and Auction, were used for testing and validation. The proposed system was able to catch all the existing insecure information flows at their source. Moreover and to highlight the simplicity of the suggested approaches vs. Existing approaches, two professional web developers assessed the annotation tasks needed in the presented case studies and provided a very positive feedback on the simplicity of the annotation task.

The popularity of Mobile Ad Hoc networks (MANETs) continues to increase due to the technological advances in wireless radios and wireless networks. Given the unpredictability of the wireless medium, one of the major challenges in MANETs remains to be the efficiency of the underlying routing protocol. The Optimized Link State Routing Protocol (OLSR) is one of the most popular routing protocols being deployed in MANETs. OLSR uses multipoint relay mechanism (MPR) to maintain the topology information at each node. Instead of using blind broadcasting where each node sends a control message to all its neighbors, and consequently every neighbor forwards the message to its neighbors, MPR selects a small set of nodes to do the broadcasting such that all network nodes are covered. In this paper, we propose improving further the performance of OLSR by introducing the concept of Mobile Agent (MA) when maintaining the MANET topology information, resulting in MA-based OLSR (MOLSR). In MOLSR, MA will substitute MPR and will rely on message unicast instead of broadcast, a major improvement to current OLSR in terms of traffic overhead. In MOLSR, every node creates and launches a MA that intelligently travels the network and returns with the full topology. In case of an MA loss, MOLSR automatically recovers the MA and makes sure it covers the whole network. Extensive simulation results show that MOLSR outperforms OLSR in various areas, namely the total control messages, network utilization, and reliable message transfer.

A peer-to-peer (P2P) system is known for its scalability and dynamic nature, where nodes can join and leave the system easily and at any time. These networks are susceptible to malicious behavior such as nodes dropping messages and misleading other nodes. P2P routing protocols are not immune against such incidents. Additionally, most secure routing protocols in the literature suffer from false negatives. In this paper, we propose to use the K-nearest neighbor (K-nn) algorithm in order to reduce false negatives in P2P secure routing protocols. We incorporate the proposed algorithm in a chord based trust aware P2P routing protocol, and evaluate its performance using the PeerSim simulator. Preliminary simulation results demonstrate that the proposed algorithm reduces the rate of false negatives without impacting the malicious node detection rate.

Abstract Port scanning is one of the most popular reconnaissance techniques that many attackers use to profile running services on a potential target before launching an attack. Many port scanning detection mechanisms have been suggested in literature. To test the proposed detection approaches, researchers use data sets that are available online or simulate their own. However, the available data sets do not provide complete logs and are usually outdated. Furthermore, the simulated data sets provide logs that do not resemble real‒life scenarios. These deficiencies in the available data sets highly affect the performance of testing the intrusion detection systems (IDSs) and result in poor evaluations. Meanwhile, very little work has been done on generating port scanning benchmarks that researchers can use to test their detection methods. In this work, we suggest a simulation framework using OMNeT++ to generate benchmarks that resemble real‒life traffic. We approach the problem by dividing it into three modules: (1) topology creation; (2) good traffic generation; and (3) bad traffic generation, each of which are made realistic, similar to deployed and usable networks. The benchmark is then tested using Snort and MalwareAnalysis. The tested IDSs were not able to catch many of the generated port scanning attacks, specifically the slow and distributed ones. We also measured the attack detection efficiency of the IDSs under different loads of background activities. Hence, the proposed framework and the annotated benchmarks will provide researchers and industry with an effective way of testing the power of IDSs' port scanning detection modules. Copyright © 2014 John Wiley & Sons, Ltd.

A peer-to-peer (P2P) system is known by its scalability and dynamic nature where nodes can join and leave the system easily and anytime. These networks are susceptible to malicious behaviors such as nodes dropping messages and misleading requesting nodes. P2P routing protocols are not immune against these misbehaviors. Therefore, detecting and dealing with malicious nodes will certainly lead to more reliable and secure system. In this paper, we propose a trust aware system for P2P routing protocols. The proposed system analyzes constantly the behaviors of all nodes to determine their trust-worthiness then classify them accordingly isolating the ones deemed malicious. It tracks the nodes' reputation based on evaluation reports from the nodes themselves. The credibility of nodes that are inaccurately evaluating other nodes is also monitored, thus, malicious evaluations would not affect other nodes' reputation. We have integrated the proposed approach with several P2P routing protocols and evaluated their performance through simulations measuring parameters such as request delivery ratio, malicious detection, and false negatives. Results show that the proposed approach improves significantly the performance of P2P routing protocols.

The goal of this brief is to present a unique top-down design methodology for developing energy-aware algorithms based on energy profiling. The key idea revolves around identifying and measuring components of code with high energy consumption. There are two major contributions of this brief: 1) a method for identifying components with high energy consumption in compute-intensive applications. To this end, we target operations called kernels, which are frequently used operations in the algorithm; 2) a method for estimating software energy for the identified software components, in particular for kernels and load/store operations. The energy evaluation method involves isolated code with assembly injection. Furthermore, to ensure reliable results, we use physical energy measurements conducted on specially instrumented circuit boards to provide actual and not just simulated measurements. To evaluate the proposed methods, we conducted two case studies using data mining algorithms: K-nearest neighbors and linear regression. The results highlight the contributions of kernels and memory energy to total energy.

LTE uplink frequency scheduling algorithms have neglected the user equipment's (UE) QoS requirements, relying only on the time domain to provide such requirements when creating the allocation matrix for the next transmission time interval. Two time domain paradigms exist for creating the resource allocation matrix: channel-dependent and proportional fairness. The channel dependent paradigm considers mainly the channel quality of UEs, allowing for users with high channel quality to get assigned most resources. The proportional fairness paradigm allocates resources to users based on the ratio of their channel condition over their lifelong service rate, allowing for users with low channel conditions to get some resources, but fewer than those with better channel conditions. Even though the proportional fairness paradigm's main focus is to achieve high system throughput without starving any user, it does not account for QoS requirements in many scenarios especially when UEs with high priority data pending for transmission have worst channel conditions than those with lower priority data. In this paper we propose a QoS-aware resource allocation paradigm for LTE uplink scheduling that gives more advantage to UEs having high priority data, while not starving other users. The proposed approach is scalable and mobility aware where the dynamic nature of the network is taken into account while devising the algorithm. When simulated using NS3, the proposed algorithm produced very promising results and outperformed the state-of-the-art approaches presented in literature.

Port scanning is one of the most popular reconnaissance techniques that many attackers use to profile running services on a potential target before launching an attack. Many port scanning detection mechanisms have been suggested in literature. However, very little work has been done on generating port scanning benchmarks that researchers can use to test their detection methods. In this paper, we suggest a simulation framework using OMNeT++ to generate benchmarks that resemble real-life traffic. We approach the problem by dividing it into three modules (topology creation, good traffic generation, bad traffic generation), each of which we make realistic, similar to deployed and usable networks. Hence the resultant benchmark is annotated and made public.

The proliferation of powerful smart devices is revolutionizing mobile computing systems. A particular set of applications that is gaining wide interest is recommender systems. Recommender systems provide their users with recommendations on variety of personal and relevant items or activities. They can play a significant role in today's life whether in E-commerce or for daily decisions that we need to make. We introduce a hybrid approach for solving the problem of finding the ratings of unrated items in a user-item ranking matrix through a weighted combination of user-based and item-based collaborative filtering. The proposed technique provides improvements in addressing two major challenges of recommender systems: accuracy of recommender systems and sparsity of data by simultaneously incorporating users' correlations and items ones. The evaluation of the system shows superiority of the solution compared to stand-alone user-based collaborative filtering or item-based collaborative filtering.

A wireless sensor network (WSN) is a collection of tiny and limited-capability sensor nodes that report their sensed data to a data collector, referred to as a sink node. WSNs are used in many applications, but are challenged by memory and energy constraints. To address these issues, solutions have been proposed on different levels including the topological level where multiple sinks can be used in the network to reduce the number of hops between a sensor and its sink node. Topological level solutions are very crucial in time-sensitive applications where the maximum worst case delay incurred by a message to get from a sensor to the corresponding sink should be minimal or at least less than a certain value. In turn, the maximum worst case delay can be minimized by choosing near optimal locations of the sinks. Consequently the network lifetime will be extended since the energy consumed by the sensor nodes will be reduced. In this paper, we propose an efficient and robust approach based on Particle Swarm Optimization (PSO) heuristic to solve the multiple sink placement problem; more specifically we use Discrete PSO (DPSO) with local search (LS). We start by formulating the problem then discretizing it and finally applying PSO while introducing local search to the inner workings of the algorithm. When compared to Genetic Algorithm-based Sink Placement (GASP), which is considered the state-of-the-art in solving the multiple sink placement problem, our approach improved the results in most scenarios while requiring less runtime.

Improvements over recent years in the performance of Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers have encouraged the modeling of complex engineering problems as ILP. An example is the Clustering Problem in Mobile Ad-Hoc Networks (MANETs). The Clustering Problem in MANETs consists of selecting the most suitable nodes of a given MANET topology as clusterheads, and ensuring that regular nodes are connected to clusterheads such that the lifetime of the network is maximized. This paper proposes enhanced ILP formulations for the Clustering Problem, through the enablement of multi-hop connections and intra-cluster communication, and assesses the performance of state-of-the art generic ILP and SAT solvers in solving the enhanced formulations.

ABSTRACT Attacks have been targeting secure socket layer (SSL) from the time it was created especially because of its utmost importance in securing Web transactions. These attacks are either attacks exploiting vulnerabilities in the SSL protocol itself, or attacks exploiting vulnerabilities in the services that SSL uses, such as certificates and web browsers. While the attacks on SSL itself have been successful, at least in the context of academics or other research, attacks on the services that SSL uses have been successfully exploited in an actual commercial setting; the fact that makes these kinds of attacks extremely dangerous. In this paper, we give a brief overview of the attacks conducted on the implementation of SSL and we analyze in more details the recent attacks that exploit the services SSL uses. Most of these attacks are considered Man in the Middle (MitM) attacks. In particular, we explore the most recent five attacks targeting SSL: SSL sniffing, MD5 collide certificate, SSL striping, SSL Null prefix and online certificate status protocol (OCSP) attack. We discuss the origins of each attack and explain the typical environment that allows for such attacks to occur. We then highlight the implementation phase where we implemented some of the attacks and were able to catch logins, passwords, and any data transmitted between two parties. In addition, we implemented using, Java, our own parsers and decoders to extract the useful data from the captured files and decode them if needed. Since most of the discussed attacks target browsers and the way they manage certificates, we conducted an extensive evaluation on the rate of success of the SSL attacks when various browsers are used. The browsers that were considered are Internet explorer (IE), Mozilla Firefox, Opera, Safari, and Chrome. The alarming results show that all analyzed attacks except for SSL Sniffing can be performed on almost all browsers. Copyright © 2011 John Wiley & Sons, Ltd.

Port scanning is the most popular reconnaissance technique attackers use to discover services they can break into. Port scanning detection has received a lot of attention by researchers. However a slow port scan attack can deceive most of the existing Intrusion Detection Systems (IDS). In this paper, we present a new, simple, and efficient method for detecting slow port scans. Our proposed method is mainly composed of two phases: (1) a feature collection phase that analyzes network traffic and extracts the features needed to classify a certain IP as malicious or not. (2) A classification phase that divides the IPs, based on the collected features, into three groups: normal IPs, suspicious IPs and scanner IPs. The IPs our approach classify as suspicious are kept for the next (K) time windows for further examination to decide whether they represent scanners or legitimate users. Hence, this approach is different than the traditional approach used by IDSs that classifies IPs as either legitimate or scanners, and thus producing a high number of false positives and false negatives. A small Local Area Network was put together to test our proposed method. The experiments show the effectiveness of our proposed method in correctly identifying malicious scanners when both normal and slow port scan were performed using the three most common TCP port scanning techniques. Moreover, our method detects malicious scanners that are otherwise not detected using well known IDSs such as Snort.

The increasing demand for mobile devices and high performance computing has made energy consumption a main issue in computer technology. Mobile devices require extended battery life, but the available technology still puts limits on the need for recharging the devices. High performance computing has a high price tag on energy for compute-intensive applications such as data mining. As a result, optimizations at various layers of the computer platform are becoming necessary to minimize energy usage or extend the time before a battery needs to be recharged. This paper focuses on back-propagation neural network algorithm, one of the popular compute-intensive data mining algorithms. The goal is to present a design methodology for developing an energy aware algorithm. The key idea revolves around identifying operations called kernels, which are frequently used in the algorithm, and that can be implemented in hardware. Optimizing these kernels for performance or energy would then lead to a major impact in these areas. These kernels are analyzed for their impact on the overall application energy using energy-based asymptotic analysis. The methodology then considers additional optimizations not related to kernels, but are specific to the back-propagation algorithm. Suggestions are provided to improve the performance and reduce energy consumption. Experiments show that there are significant potentials in energy reduction through the use of alternative lower energy kernels or through custom optimizations with tradeoffs in the accuracy of the results.

Abstract Wired and wireless networks are being attacked and hacked on continuous basis. One of the critical pieces of information the attacker needs to know is the open ports on the victim's machine, thus the attacker does what is called port scanning. Port scanning is considered one of the dangerous attacks that intrusion detection tries to detect. Snort, a famous network intrusion detection system (NIDS), detects a port scanning attack by combining and analyzing various traffic parameters. Because these parameters cannot be easily combined using a mathematical formula, fuzzy logic can be used to combine them; fuzzy logic can also reduce the number of false alarms. This paper presents a novel approach, based on fuzzy logic, to detect port scanning attacks. A fuzzy logic controller is designed and integrated with Snort in order to enhance the functionality of port scanning detection. Experiments are carried out in both wired and wireless networks. The results show that applying fuzzy logic adds to the accuracy of determining bad traffic. Moreover, it gives a level of degree for each type of port scanning attack. Copyright © 2010 John Wiley & Sons, Ltd.

Artificial intelligence and wireless technologies have progressed rapidly in the last decade driven by technology evolution and new emerging applications. The automotive industry has the opportunity to benefit from progress in these fields. In this paper, we propose an extensible software framework that can be implemented on all vehicles. It comprehends the capabilities supporting wireless communication and analytics for collaborative intelligence among vehicles and data mining solutions. The architecture has four key modules for: interface to on-board controls, external wireless communication, artificial intelligence, and an internal virtual bus. The design is implemented as a framework so that new applications can be developed with minimal additions to achieve high end solutions. We demonstrate the effectiveness of the architecture with vehicle safety applications, and illustrate the use of collaborative intelligence. A potential implementation is proposed with multi-threads lending itself to parallel programming and high performance computing for real-time response.

This paper describes a method of implementing two factor authentication using mobile phones. The proposed method guarantees that authenticating to services, such as online banking or ATM machines, is done in a very secure manner. The proposed system involves using a mobile phone as a software token for one time password generation. The generated one time password is valid for only a short user-defined period of time and is generated by factors that are unique to both, the user and the mobile device itself. Additionally, an SMS-based mechanism is implemented as both a backup mechanism for retrieving the password and as a possible mean of synchronization. The proposed method has been implemented and tested. Initial results show the success of the proposed method.

Minimal configuration and quick deployment of ad hoc networks make it suitable for numerous applications such as emergency situations, border monitoring, and military missions, etc. For such ad hoc networks to fulfill their mission in a timely manner, they should be able to establish a connection between nodes and to maintain this connection until the communication halts. Establishing a connection is achieved by using a routing protocol, and maintaining it is achieved by having a resilient fault tolerant network. In this paper, we propose a network design scheme that incorporates these features. We first propose a special network topology that is unique in terms of how nodes are interconnected. After constructing the initial topology, we propose a distributed routing protocol that allows any two sites to communicate by traversing at most 2 nodes regardless of the network size. We conducted both simulation study and theoretical analysis; the results show that the proposed scheme is resilient to network dynamics and has high quality as well as efficient routing.

BACKGROUND: Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines. RESULTS: To assess the ability of the proposed method to recognize the difference between "interacted" and "non-interacted" proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction. CONCLUSION: Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.

Abstract In the communication sector, the optimal objective is to equate quality and cost. The technologies that best serve these objectives are Wireless Access Technologies since they are easily deployed and capable of reaching and serving customers everywhere in a cost effective way. In this paper, we examine the communication options, and account for a country's geography to propose optimal WiMax planning keeping in mind the security concerns that are inherent in wireless communication. To perform WiMax radio network planning, we use a network simulation tool from ATDI called ICS Telecom. Our approach offers all users a minimum bandwidth of 1.4 Mbps as well as a coverage that exceeds 90% for all indoor users in the area under study. We optimize the network by iteratively minimizing the number of base stations required, and equivalently minimizing the cost, while maximizing the coverage for the subscribers. We also analyze the impact of security on the performance of WiMax. More specifically, we use well‐known simulation software called Qualnet to simulate a WiMax environment under different security protocols and encryption scenarios. We then analyze the results to determine the impact of the added security features on the data rates between the base station(s) subscribers. The results of our proposed WiMax planning approach and the conducted security experiments showed that efficient deployment and coverage plans could be achieved for big cities as well as for rural areas. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The performance of wireless local area networks (WLANs) is based on the performance of the corresponding access points (APs). Nowadays, network engineers tend to manually assign data channels (frequencies) for each AP. They only use channels 1, 6, and 11 because no interference exists between these channels. But it will be far more efficient if all 11 channels are used. Therefore, the channel allocation problem becomes a major challenge when deploying WLANs. In this paper, we assume that the location of each AP is known. Our objective is to optimally assign a frequency for each AP such that the throughput is maximized and the interference between the various APs is minimized. We also consider a realistic scenario where the APs are not in line of sight of each other, but on the other hand there are different barriers that separate them. We formulate the problem using integer linear programming (ILP) in order to obtain the optimal frequency assignment (OFA). Then, we propose two efficient heuristic algorithms to achieve the same results. Finally, we evaluate the performance of all techniques and make a comparison between them. Copyright © 2008 John Wiley & Sons, Ltd.

ARP cache poisoning is considered to be one of the easiest and dangerous attacks in local area networks. This paper proposes a solution to the ARP poisoning problem by extending the current ARP protocol implementation. Instead of the traditional stateless ARP cache, we use a stateful ARP cache in order to manage and secure the ARP cache. We also use a novel Fuzzy Logic approach to differentiate between normal and malicious ARP replies. The Fuzzy Logic controller uses a dynamically populated data base that adapts to network changes. The limits of the current approaches are discussed and analyzed.

Nowadays data networks represent the most common communication environment for transfer of data, voice or image. Such popularity led network users to becoming more vulnerable to network attacks and intrusions. Data link layer attacks, ex. ARP poisoning, is considered to be one of these dangerous attacks. ARP poisoning attack is a technique used to attack an Ethernet network. It may allow an attacker to sniff network traffic or stop the traffic altogether. In this paper, we use a fuzzy logic controller to thwart data link layer attacks in Ethernet networks (ARP poisoning). Each host in the network is assigned certain dynamic characteristics. Then a fuzzy logic controller is used to combine these characteristics keeping in mind the synergy between them. The output of the controller decides if the host is trusted or not. Moreover, we use a stateful ARP cache, instead of the traditional stateless ARP cache.

Virtual backbone based routing is a promising approach for enhancing the routing efficiency in wireless ad hoc networks. To establish communication in the network, the virtual backbone nodes have to be connected. Connected dominating sets (CDS) are the earliest structures proposed as candidates for virtual backbones in ad hoc networks. In this paper, we propose a fast distributed and efficient algorithm to find a connected dominating set (DE-CDS) in wireless ad hoc networks. DE-CDS has a message and time complexity of O(n) and O(Delta2), where n is the number of nodes in the network and Delta is the maximum node degree. According to our knowledge, DE-CDS achieves the best message and time complexity combinations among the previously suggested approaches. Moreover, DE-CDS constructs a reliable virtual backbone that takes into account (1) node's limited energy, (2) node's mobility, and (3) node's traffic pattern.

A mobile Ad-Hoc network (MANET) is a collection of autonomous arbitrarily located wireless mobile hosts, in which an infrastructure is absent. In this paper we propose a fuzzy-based hierarchical energy efficient routing scheme (FEER) for large scale mobile ad-hoc networks that aims to maximize the network's lifetime. Each node in the network is characterized by its residual energy, traffic, and mobility. We develop a fuzzy logic controller that combines these parameters, keeping in mind the synergy between them. The value obtained, indicates the importance of a node and it is used in network formation and maintenance. We compare our approach to another energy efficient hierarchical protocol based on the dominating set (DS) idea. Our simulation shows that our design out performs the DS approach in prolonging the network lifetime.

Due to the growing interest in mobile wireless Ad-Hoc networks' (MANETs) applications, researchers have proposed many routing protocols that differ in their objective. Energy efficiency and scalability are two of the most important objectives. In our previous work, we proposed a fuzzy based hierarchical energy efficient routing protocol (FEER) for large scale MANETs that aims to maximize the network's lifetime and increase its scalability. The problem has two parts: the clustering part and the routing part. In the first part, we cluster the network into two levels of hierarchy (cluster heads and normal nodes), connect the cluster heads (backbone) with each other, and connect the normal nodes to the cluster heads while maximizing the network lifetime. In the second part, we design energy efficient routing that uses the hierarchical structure. We call the first part, the energy efficient clustering problem (EEC). In this paper, we formulate three variations of EEC as integer linear programming (ILP) problems. We first consider a network with a fully connected backbone (EEC-FCB). Then, we relax the fully connected constraint and consider a network with a connected backbone (EEC-CB), not necessarily fully connected. Finally, we consider a more reliable network (EEC-R) by electing a backup cluster head for each cluster.