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Logs of SQL queries are useful for building the system design, upgrading, and checking which SQL queries are running on certain applications. These SQL queries provide us useful information and knowledge about the system operations. The existing works use SQL query logs to find patterns when the underlying data and database schema is not available. For this purpose, a knowledge-base in the form of an ontology is created which is then mined for knowledge extraction. In this paper, we have proposed an approach to create and evolve an ontology from logs of SQL queries. Furthermore, when these SQL queries are transformed into the ontology, they loose their original form/shape i.e., we do not have original SQL queries. Therefore, we have further proposed a strategy to recover these SQL queries in their original form. Experiments on real world datasets demonstrate the effectiveness of the proposed approach.
Awais Yousaf; Asad Masood Khattak; Kifayat Ullah Khan. Ontology Evolution Using Recoverable SQL Logs. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 509 -517.
AMA StyleAwais Yousaf, Asad Masood Khattak, Kifayat Ullah Khan. Ontology Evolution Using Recoverable SQL Logs. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():509-517.
Chicago/Turabian StyleAwais Yousaf; Asad Masood Khattak; Kifayat Ullah Khan. 2021. "Ontology Evolution Using Recoverable SQL Logs." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 509-517.
Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Experimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).
Asad Khattak; Muhammad Zubair Asghar; Zain Ishaq; Waqas Haider Bangyal; Ibrahim A Hameed. Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews. Egyptian Informatics Journal 2021, 1 .
AMA StyleAsad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, Ibrahim A Hameed. Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews. Egyptian Informatics Journal. 2021; ():1.
Chicago/Turabian StyleAsad Khattak; Muhammad Zubair Asghar; Zain Ishaq; Waqas Haider Bangyal; Ibrahim A Hameed. 2021. "Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews." Egyptian Informatics Journal , no. : 1.
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.
The constant development of interrelated computing devices and the emergence of new network technologies have caused a dramatic growth in the number of Internet of Things (IoT) devices. It has brought great convenience to people’s lives where its applications have been leveraged to revolutionize everyday objects connected in different life aspects such as smart home, healthcare, transportation, environment, agriculture, and military. This interconnectivity of IoT objects takes place through networks on centralized cloud infrastructure that is not constrained to national or jurisdictional boundaries. It is crucial to maintain security, robustness, and trustless authentication to guarantee secure exchange of critical user data among IoT objects. Consequently, blockchain technology has recently emerged as a tenable solution to offer such prominent features. Blockchain’s secure decentralization can overcome security, authentication, and maintenance limitations of current IoT ecosystem. In this paper we conduct a comprehensive literature review to address recent security and privacy challenges related to IoT where they are categorized according to IoT layered architecture: perception, network, and application layer. Further, we investigate blockchain technology as a key pillar to overcome many of IoT security and privacy problems. Additionally, we explore the blockchain technology and its added values when combined with other new technologies as machine learning especially in intrusion detection systems. Moreover, we highlight challenges and privacy issues resulted due to integration of blockchain in IoT applications. Finally, we propose a framework of IoT security and privacy requirements via blockchain technology. Our main contribution is to exhaust the literature to highlight the recent IoT security and privacy issues and how blockchain can be utilized to overcome these issues, nevertheless; we address challenges and open security issues that blockchain may impose on the current IoT systems. Research findings formulate a rigid foundation upon which an efficient and secure adoption of IoT and blockchain is highlighted accordingly.
Omar Alfandi; Salam Khanji; Liza Ahmad; Asad Khattak. A survey on boosting IoT security and privacy through blockchain. Cluster Computing 2020, 24, 37 -55.
AMA StyleOmar Alfandi, Salam Khanji, Liza Ahmad, Asad Khattak. A survey on boosting IoT security and privacy through blockchain. Cluster Computing. 2020; 24 (1):37-55.
Chicago/Turabian StyleOmar Alfandi; Salam Khanji; Liza Ahmad; Asad Khattak. 2020. "A survey on boosting IoT security and privacy through blockchain." Cluster Computing 24, no. 1: 37-55.
The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods.
Asad Khattak; Anam Habib; Muhammad Zubair Asghar; Fazli Subhan; Imran Razzak; Ammara Habib. Applying deep neural networks for user intention identification. Soft Computing 2020, 25, 2191 -2220.
AMA StyleAsad Khattak, Anam Habib, Muhammad Zubair Asghar, Fazli Subhan, Imran Razzak, Ammara Habib. Applying deep neural networks for user intention identification. Soft Computing. 2020; 25 (3):2191-2220.
Chicago/Turabian StyleAsad Khattak; Anam Habib; Muhammad Zubair Asghar; Fazli Subhan; Imran Razzak; Ammara Habib. 2020. "Applying deep neural networks for user intention identification." Soft Computing 25, no. 3: 2191-2220.
Fahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. 2020, 1 -36.
AMA StyleFahad Ahmed Satti, Taqdir Ali, Jamil Hussain, Wajahat Ali Khan, Asad Masood Khattak, Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. . 2020; ():1-36.
Chicago/Turabian StyleFahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. 2020. "Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability." , no. : 1-36.
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.
Fahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. Computing 2020, 102, 2409 -2444.
AMA StyleFahad Ahmed Satti, Taqdir Ali, Jamil Hussain, Wajahat Ali Khan, Asad Masood Khattak, Sungyoung Lee. Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability. Computing. 2020; 102 (11):2409-2444.
Chicago/Turabian StyleFahad Ahmed Satti; Taqdir Ali; Jamil Hussain; Wajahat Ali Khan; Asad Masood Khattak; Sungyoung Lee. 2020. "Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability." Computing 102, no. 11: 2409-2444.
The prediction of judicial decisions based on historical datasets in the legal domain is a challenging task. To answer the question about how the court will render a decision in a particular case has remained an important issue. Prior studies conducted on the prediction of judicial case decisions have datasets with limited size by experimenting less efficient set of predictors variables applied to different machine learning classifiers. In this work, we investigate and apply more efficient sets of predictors variables with a machine learning classifier over a large size legal dataset for court judgment prediction. Experimental results are encouraging and depict that incorporation of feature selection technique has significantly improved the performance of predictive classifier.
Anwar Ullah; Muhammad Zubair Asghar; Anam Habib; Saiqa Aleem; Fazal Masud Kundi; Asad Masood Khattak. Optimizing the Efficiency of Machine Learning Techniques. Communications in Computer and Information Science 2020, 553 -567.
AMA StyleAnwar Ullah, Muhammad Zubair Asghar, Anam Habib, Saiqa Aleem, Fazal Masud Kundi, Asad Masood Khattak. Optimizing the Efficiency of Machine Learning Techniques. Communications in Computer and Information Science. 2020; ():553-567.
Chicago/Turabian StyleAnwar Ullah; Muhammad Zubair Asghar; Anam Habib; Saiqa Aleem; Fazal Masud Kundi; Asad Masood Khattak. 2020. "Optimizing the Efficiency of Machine Learning Techniques." Communications in Computer and Information Science , no. : 553-567.
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.
Over the past two decades, the subject of extension of the lifetime of Wireless Sensor Networks (WSN) based on the Internet of Things (IoT) has been thoroughly investigated by researcher. As WSN, and its new form based on IoT, are increasingly being deployed in time-critical applications, users require certain network lifetime guarantees to satisfy application requirements. Few research efforts in the past have focused on guaranteeing the IoT-based network lifetime. Most such efforts pay little or no attention to other network performance indicators such as sensing coverage and network connectivity. To address this challenge, this work proposes a new centralized approach that analyzes a network’s energy consumption to optimize node duty cycles. In the proposed scheme, the sink node periodically assigns an active/sleep role to each node for the next network cycle by coupling the residual energy, total active time, and possible coverage area to guarantee their lifetimes. We show through extensive simulation that the proposed guaranteed lifetime protocol achieves less average end-to-end delay and better packet delivery ratio when compared to its best rival protocols formulated in past research, i.e., the CERACC, A-Mac, and Coverage Preserving protocols.
Babar Shah; Ali Abbas; Gohar Ali; Farkhund Iqbal; Asad Masood Khattak; Omar Alfandi; Ki-Il Kim. Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints. Ad Hoc Networks 2020, 104, 102158 .
AMA StyleBabar Shah, Ali Abbas, Gohar Ali, Farkhund Iqbal, Asad Masood Khattak, Omar Alfandi, Ki-Il Kim. Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints. Ad Hoc Networks. 2020; 104 ():102158.
Chicago/Turabian StyleBabar Shah; Ali Abbas; Gohar Ali; Farkhund Iqbal; Asad Masood Khattak; Omar Alfandi; Ki-Il Kim. 2020. "Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints." Ad Hoc Networks 104, no. : 102158.
Wireless communication and computation technologies are becoming increasingly complex and dynamic due to the sophisticated and ubiquitous Internet of things (IoT) applications. Therefore, future wireless networks and computation solutions must be able to handle these challenges and dynamic user requirements for the success of IoT systems. Recently, learning strategies (particularly deep learning and reinforcement learning) are explored immensely to deal with the complexity and dynamic nature of communication and computation technologies for IoT systems, mainly because of their power to predict and efficient data analysis. Learning strategies can significantly enhance the performance of IoT systems at different stages, including at IoT node level, local communication, long-range communication, edge gateway, cloud platform, and corporate data centers. This paper presents a comprehensive overview of learning strategies for IoT systems. We categorize learning paradigms for communication and computing technologies in IoT systems into reinforcement learning, Bayesian algorithms, stochastic learning, and miscellaneous. We then present research in IoT with the integration of learning strategies from the optimization perspective where the optimization objectives are categorized into maximization and minimization along with corresponding applications. Learning strategies are discussed to illustrate how these strategies can enhance the performance of IoT applications. We also identify the key performance indicators (KPIs) used to evaluate the performance of IoT systems and discuss learning algorithms for these KPIs. Lastly, we provide future research directions to further enhance IoT systems using learning strategies
Waleed Ejaz; Mehak Basharat; Salman Saadat; Asad Masood Khattak; Muhammad Naeem; Alagan Anpalagan. Learning paradigms for communication and computing technologies in IoT systems. Computer Communications 2020, 153, 11 -25.
AMA StyleWaleed Ejaz, Mehak Basharat, Salman Saadat, Asad Masood Khattak, Muhammad Naeem, Alagan Anpalagan. Learning paradigms for communication and computing technologies in IoT systems. Computer Communications. 2020; 153 ():11-25.
Chicago/Turabian StyleWaleed Ejaz; Mehak Basharat; Salman Saadat; Asad Masood Khattak; Muhammad Naeem; Alagan Anpalagan. 2020. "Learning paradigms for communication and computing technologies in IoT systems." Computer Communications 153, no. : 11-25.
In this paper, we analyze pilot contamination (PC) attacks on a multi-cell massive multiple-input multiple-output (MIMO) network with correlated pilots. We obtain correlated pilots using a user capacity-achieving pilot sequence design. This design relies on an algorithm which designs correlated pilot sequences based on signal-to-interference-plus-noise ratio (SINR) requirements for all the legitimate users. The pilot design is capable of achieving the SINR requirements for all users even in the presence of PC. However, this design has some intrinsic limitations and vulnerabilities, such as a known pilot sequence and the non-zero cross-correlation among different pilot sequences. We reveal that such vulnerabilities may be exploited by an active attacker to increase PC in the network. Motivated by this, we analyze the correlated pilot design for vulnerabilities that can be exploited by an active attacker. Based on this analysis, we develop an effective active attack strategy in the massive MIMO network with correlated pilot sequences. Our examinations reveal that the user capacity region of the network is significantly reduced in the presence of the active attack. Importantly, the SINR requirements for the worst-affected users may not be satisfied even with an infinite number of antennas at the base station.
Noman Akbar; Shihao Yan; Asad Masood Khattak; Nan Yang. On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks. 2020, 1 .
AMA StyleNoman Akbar, Shihao Yan, Asad Masood Khattak, Nan Yang. On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks. . 2020; ():1.
Chicago/Turabian StyleNoman Akbar; Shihao Yan; Asad Masood Khattak; Nan Yang. 2020. "On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks." , no. : 1.
Label noises exist in many applications, and their presence can degrade learning performance. Researchers usually use filters to identify and eliminate them prior to training. The ensemble learning based filter (EnFilter) is the most widely used filter. According to the voting mechanism, EnFilter is mainly divided into two types: single-voting based (SVFilter) and multiple-voting based (MVFilter). In general, MVFilter is more often preferred because multiple-voting could address the intrinsic limitations of single-voting. However, the most important unsolved issue in MVFilter is how to determine the optimal decision point (ODP). Conceptually, the decision point is a threshold value, which determines the noise detection performance. To maximize the performance of MVFilter, we propose a novel approach to compute the optimal decision point. Our approach is data driven and cost sensitive, which determines the ODP based on the given noisy training dataset and noise misrecognition cost matrix. The core idea of our approach is to estimate the mislabeled data probability distributions, based on which the expected cost of each possible decision point could be inferred. Experimental results on a set of benchmark datasets illustrate the utility of our proposed approach.
Donghai Guan; Maqbool Hussain; Weiwei Yuan; Asad Masood Khattak; Muhammad Fahim; Wajahat Ali Khan. Enhanced Label Noise Filtering with Multiple Voting. Applied Sciences 2019, 9, 5031 .
AMA StyleDonghai Guan, Maqbool Hussain, Weiwei Yuan, Asad Masood Khattak, Muhammad Fahim, Wajahat Ali Khan. Enhanced Label Noise Filtering with Multiple Voting. Applied Sciences. 2019; 9 (23):5031.
Chicago/Turabian StyleDonghai Guan; Maqbool Hussain; Weiwei Yuan; Asad Masood Khattak; Muhammad Fahim; Wajahat Ali Khan. 2019. "Enhanced Label Noise Filtering with Multiple Voting." Applied Sciences 9, no. 23: 5031.
Advanced Metering Infrastructure (AMI) is the aggregation of smart meters, communications networks, and data management systems that are tailored to meet the efficient integration of renewable energy resources. The more complex features and soundless functionalities the AMI is enhanced with, the more cyber security concerns are raised and must be taken into consideration. It is imperative to assure consumer’s privacy and security to guarantee the proliferation of rolling out smart metering infrastructure. This research paper analyzes AMI from security perspectives; it discusses the possible vulnerabilities associated with different attack surfaces in the smart meter, their security and threat implications, and finally it recommends proper security controls and countermeasures. The research findings draw the foundation upon which robust security by design approach is geared for the deployment of the AMI in the future.
Asad Masood Khattak; Salam Ismail Khanji; Wajahat Ali Khan. Smart Meter Security: Vulnerabilities, Threat Impacts, and Countermeasures. Advances in Intelligent Systems and Computing 2019, 554 -562.
AMA StyleAsad Masood Khattak, Salam Ismail Khanji, Wajahat Ali Khan. Smart Meter Security: Vulnerabilities, Threat Impacts, and Countermeasures. Advances in Intelligent Systems and Computing. 2019; ():554-562.
Chicago/Turabian StyleAsad Masood Khattak; Salam Ismail Khanji; Wajahat Ali Khan. 2019. "Smart Meter Security: Vulnerabilities, Threat Impacts, and Countermeasures." Advances in Intelligent Systems and Computing , no. : 554-562.
Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification.
Imad Eddine Ibrahim Bekkouch; Youssef Youssry; Rustam Gafarov; Adil Khan; Asad Masood Khattak. Triplet Loss Network for Unsupervised Domain Adaptation. Algorithms 2019, 12, 96 .
AMA StyleImad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan, Asad Masood Khattak. Triplet Loss Network for Unsupervised Domain Adaptation. Algorithms. 2019; 12 (5):96.
Chicago/Turabian StyleImad Eddine Ibrahim Bekkouch; Youssef Youssry; Rustam Gafarov; Adil Khan; Asad Masood Khattak. 2019. "Triplet Loss Network for Unsupervised Domain Adaptation." Algorithms 12, no. 5: 96.
User behavior prediction with low-dimensional vectors generated by user network embedding models has been verified to be efficient and reliable in real applications. However, most user network embedding models utilize homogeneous properties to represent users, such as attributes or user network structure. Though some works try to combine two kinds of properties, the existing works are still not enough to leverage the rich semantics of users. In this paper, we propose a novel heterogeneous information preserving user network embedding model, which is named HINE, for user behavior classification in user network. HINE applies attributes, user network connection, user network structure, and user behavior label information for user representation in user network embedding. The embedded vectors considering these multi-type properties of users contribute to better user behavior classification performances. Experiments verified the superior performances of the proposed approach on real-world complex user network dataset.
Weiwei Yuan; Kangya He; Guangjie Han; Donghai Guan; Asad Masood Khattak. User behavior prediction via heterogeneous information preserving network embedding. Future Generation Computer Systems 2018, 92, 52 -58.
AMA StyleWeiwei Yuan, Kangya He, Guangjie Han, Donghai Guan, Asad Masood Khattak. User behavior prediction via heterogeneous information preserving network embedding. Future Generation Computer Systems. 2018; 92 ():52-58.
Chicago/Turabian StyleWeiwei Yuan; Kangya He; Guangjie Han; Donghai Guan; Asad Masood Khattak. 2018. "User behavior prediction via heterogeneous information preserving network embedding." Future Generation Computer Systems 92, no. : 52-58.
Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).
Adnan Amin; Babar Shah; Asad Masood Khattak; Fernando Joaquim Lopes Moreira; Gohar Ali; Alvaro Rocha; Sajid Anwar. Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods. International Journal of Information Management 2018, 46, 304 -319.
AMA StyleAdnan Amin, Babar Shah, Asad Masood Khattak, Fernando Joaquim Lopes Moreira, Gohar Ali, Alvaro Rocha, Sajid Anwar. Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods. International Journal of Information Management. 2018; 46 ():304-319.
Chicago/Turabian StyleAdnan Amin; Babar Shah; Asad Masood Khattak; Fernando Joaquim Lopes Moreira; Gohar Ali; Alvaro Rocha; Sajid Anwar. 2018. "Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods." International Journal of Information Management 46, no. : 304-319.
Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.
Adnan Amin; Babar Shah; Asad Masood Khattak; Thar Baker; Hamood Ur Rahman Durani; Sajid Anwar. Just-in-time Customer Churn Prediction: With and Without Data Transformation. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -6.
AMA StyleAdnan Amin, Babar Shah, Asad Masood Khattak, Thar Baker, Hamood Ur Rahman Durani, Sajid Anwar. Just-in-time Customer Churn Prediction: With and Without Data Transformation. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-6.
Chicago/Turabian StyleAdnan Amin; Babar Shah; Asad Masood Khattak; Thar Baker; Hamood Ur Rahman Durani; Sajid Anwar. 2018. "Just-in-time Customer Churn Prediction: With and Without Data Transformation." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-6.
Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active.
Muhammad Fahim; Thar Baker; Asad Masood Khattak; Babar Shah; Saiqa Aleem; Francis Chow. Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone. Sensors 2018, 18, 874 .
AMA StyleMuhammad Fahim, Thar Baker, Asad Masood Khattak, Babar Shah, Saiqa Aleem, Francis Chow. Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone. Sensors. 2018; 18 (3):874.
Chicago/Turabian StyleMuhammad Fahim; Thar Baker; Asad Masood Khattak; Babar Shah; Saiqa Aleem; Francis Chow. 2018. "Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone." Sensors 18, no. 3: 874.