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Cloud Computing (CC) is a promising technology due to its pervasive features, such as online storage, high scalability, and seamless accessibility, in that it plays an important role in reduction of the capital cost and workforce, which attracts organizations to conduct their businesses and financial activities over the cloud. Even though CC is a great innovation in the aspect of computing with ease of access, it also has some drawbacks. With the increase of cloud usage, security issues are proportional to the increase. To address these, there has been much work done in this domain, whereas research work considering the growing constrained applications provided by the Internet of Things (IoT) and smart city networks are still lacking. In this survey, we provide a comprehensive security analysis of CC-enabled IoT and present state-of-the-art in the research area. Finally, future research work and possible areas of implementation and consideration are given to discuss open issues.
Abeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics 2021, 10, 1811 .
AMA StyleAbeer Tahirkheli, Muhammad Shiraz, Bashir Hayat, Muhammad Idrees, Ahthasham Sajid, Rahat Ullah, Nasir Ayub, Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics. 2021; 10 (15):1811.
Chicago/Turabian StyleAbeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. 2021. "A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges." Electronics 10, no. 15: 1811.
Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in security-sensitive applications such as question answering and machine translation in the aspects of sustainability. In this study, we demonstrate that NRE models are inherently vulnerable to adversarially crafted text that contains imperceptible modifications of the original but can mislead the target NRE model. Specifically, we propose a novel sustainable term frequency-inverse document frequency (TFIDF) based black-box adversarial attack to evaluate the robustness of state-of-the-art CNN, CGN, LSTM, and BERT-based models on two benchmark RE datasets. Compared with white-box adversarial attacks, black-box attacks impose further constraints on the query budget; thus, efficient black-box attacks remain an open problem. By applying TFIDF to the correctly classified sentences of each class label in the test set, the proposed query-efficient method achieves a reduction of up to 70% in the number of queries to the target model for identifying important text items. Based on these items, we design both character- and word-level perturbations to generate adversarial examples. The proposed attack successfully reduces the accuracy of six representative models from an average F1 score of 80% to below 20%. The generated adversarial examples were evaluated by humans and are considered semantically similar. Moreover, we discuss defense strategies that mitigate such attacks, and the potential countermeasures that could be deployed in order to improve sustainability of the proposed scheme.
Ijaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability 2021, 13, 5892 .
AMA StyleIjaz Haq, Zahid Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee, Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability. 2021; 13 (11):5892.
Chicago/Turabian StyleIjaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. 2021. "Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks." Sustainability 13, no. 11: 5892.
Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.
Asif Khan; Huaping Zhang; Nada Boudjellal; Arshad Ahmad; Jianyun Shang; Lin Dai; Bashir Hayat. Election Prediction on Twitter: A Systematic Mapping Study. Complexity 2021, 2021, 1 -27.
AMA StyleAsif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat. Election Prediction on Twitter: A Systematic Mapping Study. Complexity. 2021; 2021 ():1-27.
Chicago/Turabian StyleAsif Khan; Huaping Zhang; Nada Boudjellal; Arshad Ahmad; Jianyun Shang; Lin Dai; Bashir Hayat. 2021. "Election Prediction on Twitter: A Systematic Mapping Study." Complexity 2021, no. : 1-27.
In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.
Muhammad Atif Butt; Asad Masood Khattak; Sarmad Shafique; Bashir Hayat; Saima Abid; Ki-Il Kim; Muhammad Waqas Ayub; Ahthasham Sajid; Awais Adnan. Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems. Complexity 2021, 2021, 1 -11.
AMA StyleMuhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki-Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan. Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems. Complexity. 2021; 2021 ():1-11.
Chicago/Turabian StyleMuhammad Atif Butt; Asad Masood Khattak; Sarmad Shafique; Bashir Hayat; Saima Abid; Ki-Il Kim; Muhammad Waqas Ayub; Ahthasham Sajid; Awais Adnan. 2021. "Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems." Complexity 2021, no. : 1-11.
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.
Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.
Asad Masood Khattak; Rabia Batool; Fahad Ahmed Satti; Jamil Hussain; Wajahat Ali Khan; Adil Mehmood Khan; Bashir Hayat. Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation. Complexity 2020, 2020, 1 -11.
AMA StyleAsad Masood Khattak, Rabia Batool, Fahad Ahmed Satti, Jamil Hussain, Wajahat Ali Khan, Adil Mehmood Khan, Bashir Hayat. Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation. Complexity. 2020; 2020 ():1-11.
Chicago/Turabian StyleAsad Masood Khattak; Rabia Batool; Fahad Ahmed Satti; Jamil Hussain; Wajahat Ali Khan; Adil Mehmood Khan; Bashir Hayat. 2020. "Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation." Complexity 2020, no. : 1-11.
Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.
Shahzad Ashraf; Omar Alfandi; Arshad Ahmad; Asad Masood Khattak; Bashir Hayat; Kyong Hoon Kim; Ayaz Ullah. Bodacious-Instance Coverage Mechanism for Wireless Sensor Network. Wireless Communications and Mobile Computing 2020, 2020, 1 -11.
AMA StyleShahzad Ashraf, Omar Alfandi, Arshad Ahmad, Asad Masood Khattak, Bashir Hayat, Kyong Hoon Kim, Ayaz Ullah. Bodacious-Instance Coverage Mechanism for Wireless Sensor Network. Wireless Communications and Mobile Computing. 2020; 2020 ():1-11.
Chicago/Turabian StyleShahzad Ashraf; Omar Alfandi; Arshad Ahmad; Asad Masood Khattak; Bashir Hayat; Kyong Hoon Kim; Ayaz Ullah. 2020. "Bodacious-Instance Coverage Mechanism for Wireless Sensor Network." Wireless Communications and Mobile Computing 2020, no. : 1-11.
The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.
Muhammad Dawood Khan; Zahid Ullah; Arshad Ahmad; Bashir Hayat; Ahmad Almogren; Kyong Hoon Kim; Muhammad Ilyas; Muhammad Ali. Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. Sensors 2020, 20, 6267 .
AMA StyleMuhammad Dawood Khan, Zahid Ullah, Arshad Ahmad, Bashir Hayat, Ahmad Almogren, Kyong Hoon Kim, Muhammad Ilyas, Muhammad Ali. Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. Sensors. 2020; 20 (21):6267.
Chicago/Turabian StyleMuhammad Dawood Khan; Zahid Ullah; Arshad Ahmad; Bashir Hayat; Ahmad Almogren; Kyong Hoon Kim; Muhammad Ilyas; Muhammad Ali. 2020. "Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN." Sensors 20, no. 21: 6267.
Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used efficiently to improve the lifetime of a network. In this paper, to support smart city vision, a terrestrial based “Away Cluster Head with Adaptive Clustering Habit” (ACH) 2 is examined in the specified three dimensional (3-D) region inside the water. Three different cases are considered, which are: single sink at the water surface, multiple sinks at water surface,, and sinks at both water surface and inside water. “Underwater (ACH) 2 ” (U-(ACH) 2 ) is evaluated in each case. We have used depth in our proposed U-(ACH) 2 to examine the performance of (ACH) 2 in the ocean environment. Moreover, a comparative analysis is performed with state of the art routing protocols, including: Depth-based Routing (DBR) and Energy Efficient Depth-based Routing (EEDBR) protocol. Among all of the scenarios followed by case 1 and case 3, the number of packets sent and received at sink node are maximum using DEEC-(ACH) 2 protocol. The packets drop ratio using TEEN-(ACH) 2 protocol is less when compared to other algorithms in all scenarios. Whereas, for dead nodes DEEC-(ACH) 2 , LEACH-(ACH) 2 , and SEP-(ACH) 2 protocols’ performance is different for every considered scenario. The simulation results shows that the proposed protocols outperform the existing ones.
Nighat Usman; Omar Alfandi; Saeeda Usman; Asad Masood Khattak; Muhammad Awais; Bashir Hayat; Ahthasham Sajid. An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities. Sensors 2020, 20, 4116 .
AMA StyleNighat Usman, Omar Alfandi, Saeeda Usman, Asad Masood Khattak, Muhammad Awais, Bashir Hayat, Ahthasham Sajid. An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities. Sensors. 2020; 20 (15):4116.
Chicago/Turabian StyleNighat Usman; Omar Alfandi; Saeeda Usman; Asad Masood Khattak; Muhammad Awais; Bashir Hayat; Ahthasham Sajid. 2020. "An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities." Sensors 20, no. 15: 4116.
Cloud computing has received a lot of attention from both researcher and developer in last decade due to its unique structure of providing services to the user. As the digitalization of world, heterogeneous devices, and with the emergence of Internet of Things (IoT), these IoT devices produce different type of data with distinct frequency, which require real‐time and latency sensitive services. This provides great challenge to cloud computing framework. Fog computing is a new framework to accompaniment cloud platform and is proposed to extend services to the edge of the network. In fog computing, the entire user's tasks are offloaded to distributed fog nodes to the edge of network to avoid delay sensitivity. We select fog computing network dwell different set of fog nodes to provide required services to the users. Allocation of defined resource to the users in order to achieve optimal result is a big challenge. Therefore, we propose dynamic resource allocation strategy for cloud, fog node, and users. In the framework, we first formulate the ranks of fog node using TOPSIS to identify most suitable fog node for the incoming request. Simultaneously logistic regression calculates the load of individual fog node and updates the result to send back to the broker for next decision. Simulation results demonstrate that the proposed scheme undoubtedly improves the performance and give accuracy of 98.25%.
Hayat Bashir; Seonah Lee; Kyong Hoon Kim. Resource allocation through logistic regression and multicriteria decision making method in IoT fog computing. Transactions on Emerging Telecommunications Technologies 2019, e3824 .
AMA StyleHayat Bashir, Seonah Lee, Kyong Hoon Kim. Resource allocation through logistic regression and multicriteria decision making method in IoT fog computing. Transactions on Emerging Telecommunications Technologies. 2019; ():e3824.
Chicago/Turabian StyleHayat Bashir; Seonah Lee; Kyong Hoon Kim. 2019. "Resource allocation through logistic regression and multicriteria decision making method in IoT fog computing." Transactions on Emerging Telecommunications Technologies , no. : e3824.