Showing papers by "Ata Ullah published in 2020"

Advances in Position Based Routing Towards ITS Enabled FoG-Oriented VANET–A Survey

Abstract: With the rapid growth in connected vehicles and related innovative applications, it is getting keen interest among the researchers. Vehicular ad-hoc networks (VANETs) comprise of interconnected vehicles with sensing capabilities to exchange traffic, weather and emergency information. Intelligent transportation system (ITS) supports better coordination among vehicles and in providing reliable services. In VANET, topology is dynamic due to the high mobility of vehicles, therefore, the existing topology-based schemes are not suitable. In this paper, we have explored position based routing (PBR) protocols for VANETs by presenting a taxonomy. The existing survey papers have focused on PBR schemes but we have further focused on considering PBR for the city environment along with connectivity aware routing schemes. Moreover, linear programming, genetic algorithms, and regression-based schemes are also included. To further evaluate the strengths and weaknesses of PBR schemes, a categorical evaluation of different architectures, path strategies, and carry-forward strategies are presented. Currently, no architecture is presented for FoG-oriented VANET using parked vehicles as guards for anchor points or junctions. To fill this aspiring demand, we have presented a FoG-oriented VANET architecture that can support the PBR by utilizing road junctions for path selection. It also involves the vehicles in the parking area for packet transmission. Further, we have proposed to use parked vehicles near junction as an option for selecting guarding vehicle along with ITS as well. It reduces the probability of extensive carry forward-based communication due to the absence of guarding nodes. The records at parked vehicles can be upgraded at neighboring parked vehicles as well during the beaconing or exchange of data messages. The architecture supports better packet delivery ratios, end-to-end delay, transmission time, and communication cost. Moreover, opportunities and challenges of the proposed architecture are also explored to attract researchers toward this area of research.

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Abstract: With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN

Abstract: With the rapid increase in number of sensing devices for healthcare, researchers are getting growing interest due to wide application support and new challenging scenarios. Internet of Things (IoT) comprises of huge number of connected devices with a variety of sensing support especially in medical health parameters sensing. In these scenarios, secure data collection and transmission to centralized servers is quite challenging to protect against several attacks for illegal data access. Existing solutions suffer from storage, communication and energy overheads. To resolve these issues, this paper presents a FoG assisted scheme for healthcare data aggregation in a secure and efficient manner.. We have involved the peer-to-peer communication between healthcare sensing devices and wearables to share secret data with an aggregating node that can share data with FoG server. In this scenario, an aggregator may be away from FoG server and cannot transmit data directly. However, it can share the encrypted data with the neighboring aggregator to transmit data to FoG server by appending in its current aggregated data. FoG server can extract the required values from the data and can save in the local repository that can be further updated later in cloud repositories. For these functionalities, we have presented two algorithms for message receiving at aggregator and message extraction at FoG server. Moreover, compression mechanism is also presented to further reduce the communication costs. We have performed simulations using TCL and C files in NS2.35 to generate trace files and then executed AWK scripts to extract results. Results prove the supremacy of proposed scheme over existing schemes in terms of storage, communication, transmission ratio, energy consumption and resilience.

Systematic Literature Review on Energy Efficient Routing Schemes in WSN – A Survey

Abstract: In wireless sensor networks (WSN), routing is quite challenging area of research where packets are forwarded through multiple nodes to the base station. The packet being sent over the network should be shared in an energy efficient manner. It also considers the residual power of battery to enhance the network life time. Existing energy efficient routing solutions and surveys are presented but still there is a need for Systematic Literature Review (SLR) to identify the valid problems. This paper performs SLR for energy efficiency routing with 172 papers at initial stage. Next, 50 papers are shortlisted after filtration based on quality valuation and selection criteria by ensuring relevance with energy efficiency. Initially, we present literature that includes schemes for threshold sensitive, adaptive periodic threshold sensitive, power efficient, hybrid energy efficient distribution and low energy adaptive mechanisms. Result of systematic review reveals that consumption of energy is the most fundamental issue in WSN however, is not noticed by the researchers and practitioners where as it can contribute for the improvement of the energy efficiency. It also elaborates the weaknesses of the existing approaches which make them inappropriate for energy efficient routing in WSN.

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

Abstract: Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.

Performance analysis of machine learning classifiers for non-technical loss detection

Abstract: Power companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest, K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

Physical unclonable function: architectures, applications and challenges for dependable security

Abstract: Physical unclonable function (PUF) is hardware-specific security primitive for providing cryptographic functionalities that are applicable for secure communication among the embedded devices. The physical structure of PUF is considered to be easy to manufacture but hard or impossible to replicate due to variations in its manufacturing process. However, a large community of analytics believes hardware-based PUF has paved the way for its realisation in providing dependable security. In this study, the authors have thoroughly explored the architecture, applications, requirements, and challenges of PUF that provide security solutions. For presenting the literature, they have designed a taxonomy where PUFs are divided under two main categories, including non-silicon and silicon-based PUF. Currently, there is no comprehensive survey that highlights the comparison and usability of memory-based and analogue/mixed-signal based PUF that are considered to be suitable as compared to counterparts. In a similar vein, they have presented the network-specific application scenarios in wireless sensor network, wireless body area network and Internet of Things and then identified the strong, weak and controlled PUF in a categorical manner. Moreover, they have presented a number of prospective limitations that are identified in PUF structures and then identified the open research challenges to meet the desired security levels.

Fog-Assisted Secure Data Exchange for Examination and Testing in E-learning System

Abstract: E-learning systems are getting growing interest due to their wide applicability in distance education. A huge amount of data is shared among students, teachers, examiners that should be exchanged in a confidential manner. In literature, a number of related clustering-based schemes are explored that consider security but still there is a need for dependable secure schemes. This paper explores a Secure E-learning System (SES) for sharing examinations related materials by ensuring protection against various security attacks. Exam materials include tests, quizzes, question papers, answer sheets, and aptitude tests. In the first phase, we present a secure authentication mechanism for students and teachers with a trusted server or a fog server. Next, we present a Session Key Establishment Protocol (SKEP) to setup keys for a specified time period such as a class, seminar or exam. We have also maintained the level of trust and authentication level to regularly check the legitimacy of the students. A security analysis is performed to highlight the pros and cons of security schemes to ensure reliable security for e-learning systems. We have setup a testbed using web-services in ASP.net and C# on windows Azure cloud for an e-learning scenario. Results demonstrate the effectiveness of the proposed SES in terms of reducing number of untrusted students, exams exposed, student interaction time, authentication level, reputation and trust levels for students.

Secure and efficient data transfer using spreading and assimilation in MANET

Abstract: Mobile ad hoc Network (MANET) is a cluster of moveable devices connected through a wireless medium to design network with rapidly changing topologies due to mobility. MANETs are applicable in variety of innovative application scenarios where smart devices exchange data among each other. In this case, security of data is the major concern to provide dependable solution to users. This article presents a secure mechanism for data transfer where sender splits the data into fragments and receiver gets the actual data by assimilating the data fragments. We have presented an Enhanced Secured Lempel‐Ziv‐Welch (ES‐LZW) algorithm that provides cryptographic operations for secure data transfer. In proposed model, we have utilized the disjoint paths to transfer the data fragments from sender side and assimilate these fragments at receiver to get the original data. The messages containing data fragments are compressed and encrypted as well. Our scheme ensures confidentiality, integrity, efficient memory utilization, and resilience against node compromising attacks. We have validated our work through extensive simulations in NS‐2.35 using TCL and C language. Results prove that our scheme reduces memory consumption along with less encryption and decryption cost as compared to blowfish especially when plaintext has more repetitive data. We have also analyzed the impact of creating data fragments, fraction of communication compromised, and probability to compromise the data fragments by subverting intermediaries.

Dual server based security system for multimedia Services in Next Generation Networks

Abstract: Next Generation Networks (NGN) provide multimedia services to a large set of users by maintaining concurrent sessions for multimedia services using IP multimedia sub-system (IMS). SIP protocol are used for building, keeping and detaching session with clients. The main problem is that intruders can launch SIP flooding attacks that cause a bottleneck at IMS entities. In this paper, we have presented a Dual Server based Intrusion Detection and Prevention System (DS-IDPS) to guard against INVITE flooding attack. We have proposed two-level security for VoLTE environment by involving two servers titled helper and main servers to handle spoofing and attack detection respectively. We have defined three thresholds at main server by utilizing CUSUM algorithm to generate alarms by detecting intrusion. By attaching DS-IDPS at the middle of client and server, our system filters every request. We have developed a test bed using OpenIMS to develop IMS entities and launch attacks by malicious nodes and detect the intrusion in the system. It reduces CPU load and memory stack from P--CSCF. Results prove that DS-IDPS produces lesser false alarms and detect more number of malicious or bogus requests. Our proposed approach dominates as compared to preliminaries in terms of memory utilization, response time, malicious requests recognition ratios and CPU load consumption.

Multi-Cyclic Requirement Engineering for Educational and Industrial Models in Software Development

Abstract: In software development, requirement elicitation (RE) is quite challenging in software industry to meet the customer requirements and for successful project delivery. In educational projects, requirements are taken both in natural and formal languages. In software industry, commercially viable methods are adopted as per demands of variety of novel software architectures and practices. This paper covers perspectives of teachers, students and industrial requirement engineers in RE as per their level of understanding and practices. We have proposed bi–signaling requirement engineering model (BSRE) to engineer the requirement process. Secondly, Requirement Engineering Design Model is proposed based on the Multi-cyclic approach to overcome the gap between client and related stakeholders. We address the challenges of existing methodologies for RE elicitation, by introducing two new methods. First, the bi-signaling RE that connects requirements engineer to the stakeholders through the management of logged issues, while the requirement engineering design model iteratively elicit requirements from multiple stakeholders in multiple cycles.