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Dr Zeeshan Pervez is a Professor of Computer Science at the University of the West of Scotland (UWS). He is a Senior Member of IEEE, an ACM Distinguished Speaker, a Member of the ISO and BSI working groups for the FM sector, a Fellow of Higher Education Academy (UK), and a Full Member of the EPSRC Peer Review College (UK). He has over 14 years of research and industry experience in addressing technological and societal challenges and designing and developing enterprise-ready solutions.
Web 2.0 helped user-generated platforms to spread widely. Unfortunately, it also allowed for cyberbullying to spread. Cyberbullying has negative effects that could lead to cases of depression and low self-esteem. It has become crucial to develop tools for automated cyberbullying detection. The research on developing these tools has been growing over the last decade, especially with the recent advances in machine learning and natural language processing. Given the large body of work on this topic, it is vital to critically review the literature on cyberbullying within the context of these latest advances. In this paper, we survey the automated detection of cyberbullying. Our survey sheds light on some challenges and limitations for the field. The challenges range from defining cyberbullying, data collection, and feature representation to model selection, training, and evaluation. We also provide some suggestions for improving the task of cyberbullying detection. In addition to the survey, we propose to improve the task of cyberbullying detection by addressing some of the raised limitations: 1) Using recent contextual language models like BERT for the detection of cyberbullying; 2) Using slang-based word embeddings to generate better representations of the cyberbullying-related datasets. Our results show that BERT outperforms state-of-the-art cyberbullying detection models and deep learning models. The results also show that deep learning models initialized with slang-based word embeddings outperform deep learning models initialized with traditional word embeddings.
Fatma Elsafoury; Stamos Katsigiannis; Zeeshan Pervez; Naeem Ramzan. When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection. IEEE Access 2021, 9, 103541 -103563.
AMA StyleFatma Elsafoury, Stamos Katsigiannis, Zeeshan Pervez, Naeem Ramzan. When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection. IEEE Access. 2021; 9 ():103541-103563.
Chicago/Turabian StyleFatma Elsafoury; Stamos Katsigiannis; Zeeshan Pervez; Naeem Ramzan. 2021. "When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection." IEEE Access 9, no. : 103541-103563.
The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps.
Shehzad Ahmed; Tahera Kalsoom; Naeem Ramzan; Zeeshan Pervez; Muhammad Azmat; Bassam Zeb; Masood Ur Rehman. Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review. Sensors 2021, 21, 4158 .
AMA StyleShehzad Ahmed, Tahera Kalsoom, Naeem Ramzan, Zeeshan Pervez, Muhammad Azmat, Bassam Zeb, Masood Ur Rehman. Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review. Sensors. 2021; 21 (12):4158.
Chicago/Turabian StyleShehzad Ahmed; Tahera Kalsoom; Naeem Ramzan; Zeeshan Pervez; Muhammad Azmat; Bassam Zeb; Masood Ur Rehman. 2021. "Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review." Sensors 21, no. 12: 4158.
Industry 4.0 (I4.0) technologies have been highlighted in recent literature as enablers of servitisation. Simultaneously, businesses are advised to implement a circular economy (CE) to bring new opportunities. However, it is pertinent to mention that little attention has been given to assess the role of I4.0 in adopting the CE and servitisation in a fully integrated manner. This research fills this gap by developing a conceptual framework through a systematic literature review of 139 studies investigating the relationship between the I4.0, CE, and servitisation. This study identifies the impact of these variables on a firm’s operational and financial performance (revenue stream, growth, and profitability). Our research findings advocate that adopting I4.0 technologies to the business and manufacturing model enables sustainability, energy and resource efficiency while enhancing performance and offering innovative products through smart services. Thus, firms must systematically adopt I4.0 technologies to support a CE model that creates value through servitisation. This study identifies the research gaps that are unexplored for practitioners and future researchers while providing insight into the role of I4.0 in implementing CE in the servitisation business model.
Sehrish Atif; Shehzad Ahmed; Muhammad Wasim; Bassam Zeb; Zeeshan Pervez; Lorraine Quinn. Towards a Conceptual Development of Industry 4.0, Servitisation, and Circular Economy: A Systematic Literature Review. Sustainability 2021, 13, 6501 .
AMA StyleSehrish Atif, Shehzad Ahmed, Muhammad Wasim, Bassam Zeb, Zeeshan Pervez, Lorraine Quinn. Towards a Conceptual Development of Industry 4.0, Servitisation, and Circular Economy: A Systematic Literature Review. Sustainability. 2021; 13 (11):6501.
Chicago/Turabian StyleSehrish Atif; Shehzad Ahmed; Muhammad Wasim; Bassam Zeb; Zeeshan Pervez; Lorraine Quinn. 2021. "Towards a Conceptual Development of Industry 4.0, Servitisation, and Circular Economy: A Systematic Literature Review." Sustainability 13, no. 11: 6501.
In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.
Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Wajahat Ali Khan; Asad Masood Khattak; Bashir Hayat. Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability 2021, 13, 1522 .
AMA StyleRaja Majid Ali Ujjan, Zeeshan Pervez, Keshav Dahal, Wajahat Ali Khan, Asad Masood Khattak, Bashir Hayat. Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability. 2021; 13 (3):1522.
Chicago/Turabian StyleRaja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Wajahat Ali Khan; Asad Masood Khattak; Bashir Hayat. 2021. "Entropy Based Features Distribution for Anti-DDoS Model in SDN." Sustainability 13, no. 3: 1522.
The Internet-of-Things (IoT) has formed a whole new layer of the world built on internet, reaching every connected devices, actuators and sensors. Many organizations utilize IoT data streams for research and development purposes. To make value out of these data streams, the data handling party must ensure the privacy of the individuals. The most common approach to provide privacy preservation is anonymization. IoT data provides varied data streams due to the nature of the individual’s preference and versatile devices pool. The conventional single tuple expiration driven sliding window method is not adequate to provide efficient anonymization. Furthermore, minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose X-BAND algorithm that utilizes the new expiration-band mechanism for handling varied data streams to achieve efficient anonymization, and we introduce weighted distance function for X-BAND to reduce missingness of published data. Our experiment on real datasets shows that X-BAND is effective and efficient compared to famous conventional anonymization algorithm FADS. X-BAND demonstrated 5% to 11% and 1% to 3% less information loss on real dataset Adult and PM2.5 respectively while performing similar on clustering, comparable to re-using suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.
Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal. $X-BAND$ : Expiration Band for Anonymizing Varied Data Streams. IEEE Internet of Things Journal 2019, 7, 1438 -1450.
AMA StyleAnkhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal. $X-BAND$ : Expiration Band for Anonymizing Varied Data Streams. IEEE Internet of Things Journal. 2019; 7 (2):1438-1450.
Chicago/Turabian StyleAnkhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal. 2019. "$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams." IEEE Internet of Things Journal 7, no. 2: 1438-1450.
Smart cities aim to provide smart governance with the emphasis on gaining high transparency and trust in public services and enabling citizen participation in decision making processes. This means on the one hand data generated from urban transactions need to be open and trustworthy. On the other hand, security and privacy of public data needs to be handled at different administrative and geographical levels. In this paper, we investigate the pivotal role of blockchain in providing privacy, self‐verification, authentication, and authorization of participatory transactions in open governance. We also investigate up to what extent edge computing can contribute toward management of permissioned sharing at specific administrative levels and enhance privacy and provide an economic approach for resource utilization in a distributed environment. We introduce a novel architecture that is based on distributed hybrid ledger and edge computing model. The architecture provides refined and secure management of data generated and processed in different geographical and administrative units of a city. We implemented a proof of concept of the architecture and applied it on a carefully designed use case, ie, citizen participation in administrative decisions through consensus. This use case highlights the need to keep and process citizen participation data at local level by deploying district chaincodes and only share consensus results through permissioned chaincodes. The results reveal that proposed architecture is scalable and provide secure and privacy protected environment for citizen participatory applications. Our performance test results are promising and show that under control conditions, the average registration time for a citizen transaction is about 42 ms, whilst the validation and result compilation of 100 concurrent citizens' transactions took about 2.4 seconds.
Zaheer Khan; Abdul Ghafoor Abbasi; Zeeshan Pervez. Blockchain and edge computing–based architecture for participatory smart city applications. Concurrency and Computation: Practice and Experience 2019, 32, 1 .
AMA StyleZaheer Khan, Abdul Ghafoor Abbasi, Zeeshan Pervez. Blockchain and edge computing–based architecture for participatory smart city applications. Concurrency and Computation: Practice and Experience. 2019; 32 (12):1.
Chicago/Turabian StyleZaheer Khan; Abdul Ghafoor Abbasi; Zeeshan Pervez. 2019. "Blockchain and edge computing–based architecture for participatory smart city applications." Concurrency and Computation: Practice and Experience 32, no. 12: 1.
Distributed Denial of Service (DDoS) is one of the most rampant attacks in the modern Internet of Things (IoT) network infrastructures. Security plays a very vital role for an ever-growing heterogeneous network of IoT nodes, which are directly connected to each other. Due to the preliminary stage of Software Defined Networking (SDN), in the IoT network, sampling based measurement approaches currently results in low-accuracy, higher memory consumption, higher-overhead in processing and network, and low attack-detection. To deal with these aforementioned issues, this paper proposes sFlow and adaptive polling based sampling with Snort Intrusion Detection System (IDS) and deep learning based model, which helps to lower down the various types of prevalent DDoS attacks inside the IoT network. The flexible decoupling property of SDN enables us to program network devices for required parameters without utilizing third-party propriety based hardware or software. Firstly, in data-plane, to lower down processing and network overhead of switches, we deployed sFlow and adaptive polling based sampling individually. Secondly, in control-plane, to optimize detection accuracy, we deployed Snort IDS collaboratively with Stacked Autoencoders (SAE) deep learning model. Furthermore, after applying performance metrics on collected traffic streams, we quantitatively investigate trade off among attack detection accuracy and resources overhead. The evaluation of the proposed system demonstrates higher detection accuracy with 95% of True Positive rate with less than 4% of False Positive rate within sFlow based implementation compared to adaptive polling.
Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Ali Kashif Bashir; Rao Mumtaz; J. González. Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN. Future Generation Computer Systems 2019, 111, 763 -779.
AMA StyleRaja Majid Ali Ujjan, Zeeshan Pervez, Keshav Dahal, Ali Kashif Bashir, Rao Mumtaz, J. González. Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN. Future Generation Computer Systems. 2019; 111 ():763-779.
Chicago/Turabian StyleRaja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Ali Kashif Bashir; Rao Mumtaz; J. González. 2019. "Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN." Future Generation Computer Systems 111, no. : 763-779.
Ana Serrano Mamolar; Pablo Salva-Garcia; Enrique Chirivella-Perez; Zeeshan Pervez; Jose Maria Alcaraz Calero; Qi Wang. Autonomic protection of multi-tenant 5G mobile networks against UDP flooding DDoS attacks. Journal of Network and Computer Applications 2019, 145, 1 .
AMA StyleAna Serrano Mamolar, Pablo Salva-Garcia, Enrique Chirivella-Perez, Zeeshan Pervez, Jose Maria Alcaraz Calero, Qi Wang. Autonomic protection of multi-tenant 5G mobile networks against UDP flooding DDoS attacks. Journal of Network and Computer Applications. 2019; 145 ():1.
Chicago/Turabian StyleAna Serrano Mamolar; Pablo Salva-Garcia; Enrique Chirivella-Perez; Zeeshan Pervez; Jose Maria Alcaraz Calero; Qi Wang. 2019. "Autonomic protection of multi-tenant 5G mobile networks against UDP flooding DDoS attacks." Journal of Network and Computer Applications 145, no. : 1.
The IoT and its applications are the inseparable part of modern world. IoT is expanding into every corner of the world where internet is available. IoT data streams are utilized by many organizations for research and business. To benefit from these data streams, the data handling party must secure the individuals’ privacy. The most common privacy preservation approach is data anonymization. However, IoT data provides missing data streams due to the varying device pool and preferences of individuals and unpredicted devices’ malfunctions of IoT. Minimization of missingess and information loss is very important for anonymizing of missing data streams. To achieve this, we introduce IncrementalPBM (Incremental Partitioning Based Marginalization) for anonymizing missig data streams. IncrementalPBM utilizes time based sliding window for missing data stream anonymization, and it aims to control the number of QIDs for anonymization while increasing the number of tuples for anonymization. Our experiment on real dataset showed IncrementalPBM is effective and efficient for anonymizing missing data streams compared to existing missing data stream anonymization algorithm. IncrementalPBM showed significant improvement; 5% to 9% less information loss, 4500 to 6000 more number of re-use anonymization while showing comparable clustering, suppression and runtime.
Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal. Partitioning based incremental marginalization algorithm for anonymizing missing data streams. 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) 2019, 1 -7.
AMA StyleAnkhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal. Partitioning based incremental marginalization algorithm for anonymizing missing data streams. 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). 2019; ():1-7.
Chicago/Turabian StyleAnkhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal. 2019. "Partitioning based incremental marginalization algorithm for anonymizing missing data streams." 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) , no. : 1-7.
The packet loss and power consumption are the main issues considered once congestion occurs in any network, such as the Internet of Things (IoT) with a huge number of sensors and applications. Since IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is not initially designed for high stream traffic load, this restricts the application domain of RPL in several IoT scenarios such as burst traffic scenarios. The performance of RPL suffers in a network with burst traffic load, which leads to reducing the lifetime of the network and causing traffic congestion among the neighbour nodes. Therefore, to address this issue, we proposed a Burst and Congestion-Aware Metric for RPL called BCA-RPL, which calculates the rank, considering the number of packets. Also, the proposed mechanism includes congestion avoiding and load balancing techniques by switching the best parent selection to avoid the congested area. Our scheme is built and compared to the original RPL routing protocol for low power and lossy network with OF0 (OF0-RPL). Simulation results based on Cooja simulator shows BCA-RPL performs better than the original RPL-OF0 routing protocol in terms of packet loss, power consumption and packet delivery ratio (PDR) under burst traffic load. The BCA-RPL significantly improves the network where it decreases the packet loss around 50% and power consumption to an acceptable level with an improvement on the PDR of the IoT network.
Hussien Saleh Altwassi; Zeeshan Pervez; Keshav Dahal. A Burst and Congestion-Aware Routing Metric for RPL Protocol in IoT Network. 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) 2019, 1 -6.
AMA StyleHussien Saleh Altwassi, Zeeshan Pervez, Keshav Dahal. A Burst and Congestion-Aware Routing Metric for RPL Protocol in IoT Network. 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). 2019; ():1-6.
Chicago/Turabian StyleHussien Saleh Altwassi; Zeeshan Pervez; Keshav Dahal. 2019. "A Burst and Congestion-Aware Routing Metric for RPL Protocol in IoT Network." 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) , no. : 1-6.
Identity management (IdM) is a method used to determine user identities. The centralized aspect of IdM introduces a serious concern with the growing value of personal information, as well as with the General Data Protection Regulation (GDPR). The problem with currently-deployed systems and their dominating approach, with identity providers (IdP) and single-point services, is that a third party is in charge of maintaining and controlling the personal data. The main challenge to manage data securely lies in trusting humans and institutes who are responsible for controlling the entire activity. Identities are not owned by the rightful owners or the user him/herself, but by the mentioned providers. With the rise of blockchain technology, self-sovereign identities are in place utilizing decentralization; unfortunately, the flaws still exist. In this research, we propose DNS-IdM, a smart contract-based identity management system that enables users to maintain their identities associated with certain attributes, accomplishing the self-sovereign concept. DNS-IdM has promising outcomes in terms of security and privacy. Due to the decentralized nature, DNS-IdM is able to avoid not only the conventional security threats, but also the limitations of the current decentralized identity management systems.
Jamila Alsayed Kassem; Sarwar Sayeed; Hector Marco-Gisbert; Zeeshan Pervez; Keshav Dahal. DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network. Applied Sciences 2019, 9, 2953 .
AMA StyleJamila Alsayed Kassem, Sarwar Sayeed, Hector Marco-Gisbert, Zeeshan Pervez, Keshav Dahal. DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network. Applied Sciences. 2019; 9 (15):2953.
Chicago/Turabian StyleJamila Alsayed Kassem; Sarwar Sayeed; Hector Marco-Gisbert; Zeeshan Pervez; Keshav Dahal. 2019. "DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network." Applied Sciences 9, no. 15: 2953.
Demand for a reliable and adaptive intelligence generalization system has become an essential task to both the WCS's developers and its numerous services providers. Since WCS's spectrum is naturally known to be unstable, time-dependent and currently not only scarce in capacity but heavily congested and the impacts of its various services and its rapidly evolving applications are constantly making the system to be extremely complex. However as proposed by Mitola in 1999, Cognitive Radio (CR) has been developed with such intelligence capabilities and through it, the present-day WCS's spectrum complexity can be effectively managed, at the same time increases its scarce and the highly varying spectrum utilization particularly in complicated WCS's environments. To address this, CR system through its intelligence mechanism, which is also known as the Cognitive Engine (CE) enforces such adaptive intelligence functionalities to dynamically adjust its input parameters, observing its surrounding environment and ultimately makes its decision to meet the WCS's desired objective. This paper proposes the hybridization of two different Artificial Intelligence systems to design and implement an adaptive intelligence system for CR systems to predict the WCS's required objective.
Martins Olaleye; Keshav Dahal; Zeeshan Pervez. A Hybrid Intelligence-Based Cognitive Engine. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 2019, 258 -262.
AMA StyleMartins Olaleye, Keshav Dahal, Zeeshan Pervez. A Hybrid Intelligence-Based Cognitive Engine. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2019; ():258-262.
Chicago/Turabian StyleMartins Olaleye; Keshav Dahal; Zeeshan Pervez. 2019. "A Hybrid Intelligence-Based Cognitive Engine." 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) , no. : 258-262.
In the last decade, e-commerce has been grown rapidly and become a familiar tool of shopping for many people. However, some people still have concerns while making online purchases due to its uncertain attributes. In fact, there are many online consumers have suffered from monetary loose problem due to some reasons which the lack of the trust in e-commerce is one of them. Therefore, there is a great demand for a mechanism that helps to evaluate the trust throughout the online transactions. One of them is the existing mechanism of the trust management which is used in some e-commerce websites (e.g. eBay). Such a mechanism evaluates the trust by computing a trust value of any seller only based on the previous rating of the past transactions. Therefore, the trust value is only able to show the general status of the trust without taking into the account the new transaction. Consequently, there is a great possibility for the frauds to be committed by some of the malicious people. For example, some of them can easily build a good reputation by making many transactions by selling cheap products with good qualities and start to commit frauds by selling more expensive products. This kind of frauds is named by [1] as the value imbalance problem. Therefore, there is a great demand for a trust evaluation mechanism which consider the new transaction as well as the past transactions. In this paper, we propose a new method which considers three dimensions that play important roles in any online transaction to help the buyers to detect the frauds. This method measures the similarity between the new transaction and the past transactions in the products types dimension, the number of the products sold dimension and the transactions amounts dimension.
Nasser Alsharif; Keshav Dahal; Zeeshan Pervez; Pradorn Sureephong. Multi-Dimensional E-commerce Trust Evaluation Method. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) 2018, 1 -7.
AMA StyleNasser Alsharif, Keshav Dahal, Zeeshan Pervez, Pradorn Sureephong. Multi-Dimensional E-commerce Trust Evaluation Method. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). 2018; ():1-7.
Chicago/Turabian StyleNasser Alsharif; Keshav Dahal; Zeeshan Pervez; Pradorn Sureephong. 2018. "Multi-Dimensional E-commerce Trust Evaluation Method." 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) , no. : 1-7.
Spontaneous response after any kind of disaster is essential to rescue victims at the disaster-affected regions. Efficient and effective relief logistics scheduling is crucial to reduce the disaster impact on the people in the affected areas. Such scheduling plan remains challenging in the field of relief logistics and related study areas because of the constraints such as time, cost, priorities and limited resources. Also, the nature of requirements of victims changes dynamically that makes the scheduling task more challenging. This leads to formulate a distinctive relief logistics scheduling covering disaster regions requirement and priorities. With the limited availability of the vehicles, the scheduling needs to be planned in iterative manner with the multiple time slots based on the availability of the vehicles. A greedy heuristic with priority based search generates the optimal set of scheduling sequences in the iterative time-slot scenarios. Covering priority aspect gives an effective alternative for the rational scheduling of the relief logistics. In this paper, we focus especially on an optimal weighted priority relief logistics scheduling model based on different priorities with least dependency on the choice of scheduling sequence. The resulting model is appropriate for relief logistics scheduling in the early phase of disaster response. The simulated result shows that the weighted priority relief logistics scheduling model generates comparatively better scheduling schedule in comparison with sequences generated by applying the priorities individually.
Bhupesh Kumar Mishra; Tek Narayan Adhikari; Keshav Dahal; Zeeshan Pervez. Priority-Index Based Multi-Priority Relief Logistics Scheduling with Greedy Heuristic Search. 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) 2018, 1 -8.
AMA StyleBhupesh Kumar Mishra, Tek Narayan Adhikari, Keshav Dahal, Zeeshan Pervez. Priority-Index Based Multi-Priority Relief Logistics Scheduling with Greedy Heuristic Search. 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). 2018; ():1-8.
Chicago/Turabian StyleBhupesh Kumar Mishra; Tek Narayan Adhikari; Keshav Dahal; Zeeshan Pervez. 2018. "Priority-Index Based Multi-Priority Relief Logistics Scheduling with Greedy Heuristic Search." 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) , no. : 1-8.
In Low Power and Lossy Networks (LLNs) sensor nodes are deployed in various traffic load conditions such as, regular and heavy traffic load. The adoption of Internet-of-Things enabled devices in the form of wearables and ubiquitous sensors and actuators has demanded LLNs to handle burst traffic load, which is an event required by myriad IoT devices in a shared LLN. In the large events, burst traffic load requires a new radical approach of load balancing, this scenario causes congestion increases and packet drops relatively when frequent traffic burst load rises in comparison with regular and heavy loads. In this paper, we introduced a new efficient load balance mechanism for traffic congestion in IPv6 Routing Protocol for Low Power and Lossy Network (RPL). To measure the communication quality and optimize the lifetime of the network, we have chosen packet delivery ratio (PDR) and power consumption (PC) as our metrics. We proposed a traffic-aware metric that utilizes ETX and parent count metrics (ETXPC), where communication quality for LLNs with RPL routing protocol are playing an important role in traffic engineering. In addition, we provided analytical results to quantify the impact of Minimum Rank with Hysteresis Objective on Function (MRHOF) and Objective Function zero (OF0) to the packet delivery, reliability and power consumption in LLNs. The simulation results pragmatically show that the proposed load balancing approach has increased packet delivery ratio with less power consumption.
Hussien Saleh Altwassi; Zeeshan Pervez; Keshav Dahal; Baraq Ghaleb. The RPL Load Balancing in IoT Network with Burst Traffic Scenarios. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) 2018, 1 -7.
AMA StyleHussien Saleh Altwassi, Zeeshan Pervez, Keshav Dahal, Baraq Ghaleb. The RPL Load Balancing in IoT Network with Burst Traffic Scenarios. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). 2018; ():1-7.
Chicago/Turabian StyleHussien Saleh Altwassi; Zeeshan Pervez; Keshav Dahal; Baraq Ghaleb. 2018. "The RPL Load Balancing in IoT Network with Burst Traffic Scenarios." 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) , no. : 1-7.
Currently, there is a lack of tools for real validation of 5G scenarios. The increasing traffic demand of 5G networks is pushing network operators to find new cost-efficient solutions. The selected solution is a multi-tenancy approach that, together with user mobility will impose some architectural changes. This approach increases service dynamism making it necessary to have tools that provide these new capabilities to be able to validate each development. This work presents a novel experimentation framework for the emulation of 5G scenarios providing them with real-time user mobility and multi-tenancy. The functionality of this novel framework has been validated through different experiments.
Ana Serrano Mamolar; Zeeshan Pervez; Jose Maria Alcaraz Calero. An Experimentation Framework for Mobile Multi- Tenant 5G Networks Integrated with CORE Network Emulator. 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) 2018, 1 -8.
AMA StyleAna Serrano Mamolar, Zeeshan Pervez, Jose Maria Alcaraz Calero. An Experimentation Framework for Mobile Multi- Tenant 5G Networks Integrated with CORE Network Emulator. 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT). 2018; ():1-8.
Chicago/Turabian StyleAna Serrano Mamolar; Zeeshan Pervez; Jose Maria Alcaraz Calero. 2018. "An Experimentation Framework for Mobile Multi- Tenant 5G Networks Integrated with CORE Network Emulator." 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) , no. : 1-8.
Currently, there is no any effective security solution which can detect cyber-attacks against 5G networks where multitenancy and user mobility are some unique characteristics that impose significant challenges over such security solutions. This paper focuses on addressing a transversal detection system to be able to protect at the same time, infrastructures, tenants and 5G users in both edge and core network segments of the 5G multi-tenant infrastructures. A novel approach which significantly extends the capabilities of a commonly used IDS, to accurately identify attacking nodes in a 5G network, regardless of multiple network traffic encapsulations, has been proposed in this paper. The proposed approach is suitable to be deployed in almost all 5G network segments including the Mobile Edge Computing. Both architectural design and data models are described in this contribution. Empirical experiments have been carried out a realistic 5G multi-tenant infrastructures to intensively validate the design of the proposed approach regarding scalability and flexibility.
Ana Serrano Mamolar; Zeeshan Pervez; Jose Maria Alcaraz Calero; Asad Masood Khattak. Towards the transversal detection of DDoS network attacks in 5G multi-tenant overlay networks. Computers & Security 2018, 79, 132 -147.
AMA StyleAna Serrano Mamolar, Zeeshan Pervez, Jose Maria Alcaraz Calero, Asad Masood Khattak. Towards the transversal detection of DDoS network attacks in 5G multi-tenant overlay networks. Computers & Security. 2018; 79 ():132-147.
Chicago/Turabian StyleAna Serrano Mamolar; Zeeshan Pervez; Jose Maria Alcaraz Calero; Asad Masood Khattak. 2018. "Towards the transversal detection of DDoS network attacks in 5G multi-tenant overlay networks." Computers & Security 79, no. : 132-147.
The Internet-of-Things (IoT) produces and transmits enormous amounts of data. Extracting valuable information from this enormous volume of data has become an important consideration for businesses and research. However, extracting information from this data without providing privacy protection puts individuals at risk. Data has to be sanitized before use, and anonymization provides solution to this problem. Since, IoT is a collection of numerous different devices, data streams from these devices tend to vary over time thus creating varied data streams. However, implementing traditional data stream anonymization approaches only provide privacy protection for data streams that have predefined and fixed attributes. Therefore, conventional methods cannot directly work on varied data streams. In this work, we propose K-VARP (K-anonymity for VARied data stream via Partitioning) to publish varied data streams. K-VARP reads the tuple and assigns them to partitions based on description, and all tuples must be anonymized before expiring. It tries to anonymize expiring tuple within a partition if its partition is eligible to produce a K-anonymous cluster. Otherwise, partition merging is applied. In K-VARP we propose a new merging criterion called R-likeness to measure similarity distance between tuple and partitions. Moreover, flexible re-using and imputation free-publication is implied in K-VARP to achieve better anonymization quality and performance. Our experiments on a real datasets show that K-VARP is efficient and effective compared to existing algorithms. K-VARP demonstrated approximately three to nine and ten to twenty percent less information loss on two real datasets, while forming a similar number of clusters within a comparable computation time.
Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal; Steve Eager. K-VARP: K-anonymity for varied data streams via partitioning. Information Sciences 2018, 467, 238 -255.
AMA StyleAnkhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal, Steve Eager. K-VARP: K-anonymity for varied data streams via partitioning. Information Sciences. 2018; 467 ():238-255.
Chicago/Turabian StyleAnkhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal; Steve Eager. 2018. "K-VARP: K-anonymity for varied data streams via partitioning." Information Sciences 467, no. : 238-255.
Health Monitoring apps for smartphones have the potential to improve quality of life and decrease the cost of health services. However, they have failed to live up to expectation in the context of respiratory disease. This is in part due to poor objective measurements of symptoms such as cough. Real-time cough detection using smartphones faces two main challenges namely, the necessity of dealing with noisy input signals, and the need of the algorithms to be computationally efficient, since a high battery consumption would prevent patients from using them. This paper proposes a robust and efficient smartphone-based cough detection system able to keep the phone battery consumption below 25% (16% if only the detector is considered) during 24 h use. The proposed system efficiently calculates local image moments over audio spectrograms to feed an optimized classifier for final cough detection. Our system achieves 88.94% sensitivity and 98.64% specificity in noisy environments with a 5500× speed-up and 4× battery saving compared to the baseline implementation. Power consumption is also reduced by a minimum factor of 6 compared to existing optimized systems in the literature.
Carlos Hoyos-Barceló; Jesús Monge-Álvarez; Zeeshan Pervez; Luis M. San-José-Revuelta; Pablo Casaseca-De-La-Higuera. Efficient computation of image moments for robust cough detection using smartphones. Computers in Biology and Medicine 2018, 100, 176 -185.
AMA StyleCarlos Hoyos-Barceló, Jesús Monge-Álvarez, Zeeshan Pervez, Luis M. San-José-Revuelta, Pablo Casaseca-De-La-Higuera. Efficient computation of image moments for robust cough detection using smartphones. Computers in Biology and Medicine. 2018; 100 ():176-185.
Chicago/Turabian StyleCarlos Hoyos-Barceló; Jesús Monge-Álvarez; Zeeshan Pervez; Luis M. San-José-Revuelta; Pablo Casaseca-De-La-Higuera. 2018. "Efficient computation of image moments for robust cough detection using smartphones." Computers in Biology and Medicine 100, no. : 176-185.
Ideally, Wireless Communication System (WCS) and its various services are expected to operate effectively without any restrictions be it in space, time or communication locations. However, this is not absolutely possible in real-time, simply because the WCSs environmental space called Spectrum is currently found to be limited, heavily congested and continuously dynamic in nature. To address this problem, Cognitive Radio (CR) system has been proposed as the innovative technology solution. In this paper, Fuzzy Logic (FL) based approach has been proposed, designed and implemented as an adaptive prediction algorithm for the CR. The results obtained from the simulation shows that the proposed prediction algorithm was found to be faster with reduced computational complexity and offer quality improvement to the WCSs in terms of its overall throughput prediction accuracy.
Martins Olaleye; Keshav Dahal; Zeeshan Pervez. A Fuzzy-based Throughput Prediction For Wireless Communication Systems. 2018 Innovations in Intelligent Systems and Applications (INISTA) 2018, 1 -7.
AMA StyleMartins Olaleye, Keshav Dahal, Zeeshan Pervez. A Fuzzy-based Throughput Prediction For Wireless Communication Systems. 2018 Innovations in Intelligent Systems and Applications (INISTA). 2018; ():1-7.
Chicago/Turabian StyleMartins Olaleye; Keshav Dahal; Zeeshan Pervez. 2018. "A Fuzzy-based Throughput Prediction For Wireless Communication Systems." 2018 Innovations in Intelligent Systems and Applications (INISTA) , no. : 1-7.