Rabia Latif
@psu.edu.sa
Assistant Professor, College of Computer and Information Sciences
Prince Sultan University
RESEARCH INTERESTS
Information Security
CyberSecurity
Wireless Security
Cloud Computing Security
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Tajwar Mehmood, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik, and Rabia Latif
PeerJ
This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-world cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in non-Gaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.
Rabia Latif, Bello Musa Yakubu, and Tanzila Saba
Springer Science and Business Media LLC
AbstractDue to the significance of trust in Social Internet of Things (SIoT)-based smart marketplaces, several research have focused on trust-related challenges. Trust is necessary for a smooth connection, secure systems, and dependable services during trade operations. Recent SIoT-based trust assessment approaches attempt to solve smart marketplace trust evaluation difficulties by using a variety of direct and indirect trust evaluation techniques and other local trust rating procedures. Nevertheless, these methodologies render trust assessment very sensitive to seller dishonesty, and a dishonest seller may influence local trust scores and at the same time pose a significant trust related threats in the system. In this article, a MarketTrust model is introduced, which is a blockchain-based method for assessing trust in an IoT-based smart marketplace. It has three parts: familiarity, personal interactions, and public perception. A conceptual model, assessment technique, and a global trust evaluation system for merging the three components of a trust value are presented and discussed. Several experiments were conducted to assess the model's security, viability, and efficacy. According to results, the MarketTrust model scored a 21.99% higher trust score and a 47.698% lower average latency than both benchmark models. Therefore, this illustrates that using the proposed framework, a potential buyer can efficiently choose a competent and trustworthy resource seller in a smart marketplace and significantly reduce malicious behavior.
Zoya, Seemab Latif, Rabia Latif, Hammad Majeed, and Nor Shahida Mohd Jamail
Association for Computing Machinery (ACM)
Text pre-processing is a crucial step in Natural Language Processing (NLP) applications, particularly for handling informal and noisy content on social media. Word-level tokenization plays a vital role in text pre-processing by removing stop words, filtering irrelevant characters, and retaining relevant tokens. These tokens are essential for constructing meaningful n-grams within advanced NLP frameworks used for data modeling. However, tokenization in low-resource languages like Urdu presents challenges due to language complexity and limited resources. Conventional space-based methods and direct application of language-specific tools often result in erroneous tokens in Urdu Language Processing (ULP). This hinders language models from effectively learning language-specific and domain-specific tokens, leading to sub-optimal results for downstream tasks such as aspect mining, topic modeling, and Named Entity Recognition (NER). To address this issue for Urdu, we have proposed a data pre-processing technique that detects outliers using the Inter-Quartile-Range (IQR) method and proposed normalization algorithms for creating useful lexicons in conjunction with existing technologies. We have collected approximately 50 million Urdu tweets using the Twitter API and conducted the performance analysis of existing language-specific tokenizers (Urduhack and Space-based tokenizer). Dataset variants were created based on the language-specific tokenizers, and we performed statistical analysis tests and visualization techniques to compare tokenization results before and after applying the proposed outlier detection and normalization method. Our findings highlighted the noticeable improvement in token size distributions, handling of informal language tokens, and misspelled and lengthy tokens. The Urduhack tokenizer combined with the proposed outlier detection and normalization yielded tokens with the best-fitted distribution in ULP. Its effectiveness has been evaluated through the task of topic modeling using Non-negative Matrix Factorization (NMF) and Latent Dirichlet allocation (LDA). The results demonstrated new and distinct topics using unigram features while achieving highly coherent topics when utilizing bigram features. For the traditional space-based method, the results consistently demonstrated improved coherence and precision scores. However, the NMF topic modeling with bigram features outperformed LDA topic modeling with bigram features.
Ayesha Altaf, Faiza Iqbal, Rabia Latif, Bello Musa Yakubu, Seemab Latif, and Hamza Samiullah
SAGE Publications
Blockchain technology is at the peak of hype and contemporary research area across the world. It is a distributed ledger that keeps records of transactions with verifiable and immutable structures and continuously grows with the new block of transactions. Blockchain provides better transparency, enhanced security and privacy, and true traceability over the traditional approaches. Due to its advanced and secure features, it is being used in various fields such as trade finance, digital transactions, the Internet of Things (IoT), the healthcare industry, and energy sector. Blockchain technology has a great impact in all its applied fields with the prominent features of privacy and reliability of data and transactions. This survey intends to present the architecture of blockchain, its potential applications, and practices in different domains other than cryptocurrency along with the platform of blockchain. This paper will explore some potential benefits of blockchain and some future directions and open challenges that are expected to come in future research.
Kashan Ahmed, Ayesha Altaf, Nor Shahida Mohd Jamail, Faiza Iqbal, and Rabia Latif
MDPI AG
Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of anomalies. Current methods for achieving this are insufficient for overcoming the following obstacles: (1) Log processing is performed in a separate component apart from log mining. (2) In modern software systems, log format evolution is ongoing. It is hard to detect latent technical issues using simple monitoring techniques in a non-intrusive manner. Within the scope of this paper, we present a reliable and consistent method for the detection and localization of anomalies in interleaved unstructured logs in order to address the aforementioned drawbacks. This research examines Log Sequential Anomalies (LSA) for potential performance issues. In this study, IDs are used to group log messages, and ID relation graphs are constructed between distributed components. In addition to that, we offer a data-driven online log parser that does not require any parameters. By utilizing a novel log parser, the bundled log messages undergo a transformation process involving both semantic and temporal embedding. In order to identify instance–granularity anomalies, this study makes use of a heuristic searching technique and an attention-based Bi-LSTM model. The effectiveness, efficiency, and robustness of the paper are supported by the research that was performed on real-world datasets as well as on synthetic datasets. The neural network improves the F1 score by five percent, which is greater than other cutting-edge models.
Momna Dar, Faiza Iqbal, Rabia Latif, Ayesha Altaf, and Nor Shahida Mohd Jamail
MDPI AG
Spam communications from spam ads and social media platforms such as Facebook, Twitter, and Instagram are increasing, making spam detection more popular. Many languages are used for spam review identification, including Chinese, Urdu, Roman Urdu, English, Turkish, etc.; however, there are fewer high-quality datasets available for Urdu. This is mainly because Urdu is less extensively used on social media networks such as Twitter, making it harder to collect huge volumes of relevant data. This paper investigates policy-based Urdu tweet spam detection. This study aims to collect over 1,100,000 real-time tweets from multiple users. The dataset is carefully filtered to comply with Twitter’s 100-tweet-per-hour limit. For data collection, the snscrape library is utilized, which is equipped with an API for accessing various attributes such as username, URL, and tweet content. Then, a machine learning pipeline consisting of TF-IDF, Count Vectorizer, and the following machine learning classifiers: multinomial naïve Bayes, support vector classifier RBF, logical regression, and BERT, are developed. Based on Twitter policy standards, feature extraction is performed, and the dataset is separated into training and testing sets for spam analysis. Experimental results show that the logistic regression classifier has achieved the highest accuracy, with an F1-score of 0.70 and an accuracy of 99.55%. The findings of the study show the effectiveness of policy-based spam detection in Urdu tweets using machine learning and BERT layer models and contribute to the development of a robust Urdu language social media spam detection method.
Wajid Ali, Congxu Zhu, Rabia Latif, Muhammad Asim, and Muhammad Usman Tariq
MDPI AG
Image encryption techniques protect private images from unauthorized access while they are being transmitted. Previously used confusion and diffusion processes are risky and time-consuming. Therefore, finding a solution to this problem has become necessary. In this paper, we propose a new image encryption scheme that combines the Intertwining Logistic Map (ILM) and Orbital Shift Pixels Shuffling Method (OSPSM). The proposed encryption scheme applies a technique for confusion inspired by the rotation of planets around their orbits. We linked the technique of changing the positions of planets around their orbits with the shuffling technique of pixels and combined it with chaotic sequences to disrupt the pixel positions of the plain image. First, randomly selected pixels from the outermost orbit are rotated to shift the pixels in that orbit, causing all pixels in that orbit to change their original position. This process is repeated for each orbit until all pixels have been shifted. This way, all pixels are randomly scrambled on their orbits. Later on, the scrambled pixels are converted into a 1D long vector. The cyclic shuffling is applied using the key generated by the ILM to a 1D long vector and reshaped into a 2D matrix. Then, the scrambled pixels are converted into a 1D long vector to apply cyclic shuffle using the key generated by the ILM. After that, the 1D long vector is converted into a 2D matrix. For the diffusion process, using ILM generates a mask image, which is then XORed with the transformed 2D matrix. Finally, a highly secure and unrecognizable ciphertext image is obtained. Experimental results, simulation analysis, security evaluation, and comparison with existing image encryption schemes show that it has a strong advantage in defending against common attacks, and the operating speed of this encryption scheme also performs excellently in practical image encryption applications.
Rabia Khan, Naima Iltaf, Rabia Latif, and Nor Shahida Mohd Jamail
Institute of Electrical and Electronics Engineers (IEEE)
Recent advancements in the domain of recommender systems have stemmed from the inspiration of representing the user-item interaction into graphs. These heterogeneous graphs comprehensively capture the non-linear relationships between users and items alongwith features and emneddings. Graph Convolution Networks (GCNs) are state-of-the-art graph-based learning models that learn and represent the graph structures by recursively stacking layers of convolution and non-linear activation operations. GCNs are augmented with the strength of deep learning paradigms resulting in achieving better performance as compared to traditional CF (Collaborative Filtering) methods. Despite modern improvements in the domain of recommender systems, cold-start users are a daunting challenge in the design of recommender systems since the conventional recommendation services are based on solely one data source. During the recent years, cross-domain recommendation methods have gained popularity because of the availability of information in multiple domains for cold start users. We supplement this information by utilizing the data contained in the metadata of users alongwith the strength of modelling graphs using GCNs. Our proposed algorithm seams the strength of GCNs with cross-domain paradigm utilizing the richness in metadata in user’s feedback to overcome the sparsity in user-item rating matrix. The combined advantages of GCNs and cross-domain approaches alleviated the issues of cold-start users by transferring user preferences from a source domain to a target domain.
Sara Ahmed AlAnsary, Rabia Latif, and Tanzila Saba
Springer Nature Switzerland
Ayat Abodayeh, Reem Hejazi, Ward Najjar, Leena Shihadeh, and Rabia Latif
IEEE
Web scraping is an essential tool for automating the data-gathering process for big data applications. There are many implementations for web scraping, but barely any of them is based on Python's BeautifulSoup library. Therefore, this paper aims at creating a web scraper that gathers data from any website and then analyzes the data accordingly. For results and analysis, the web scraper has been implemented on the Amazon website which collects a product's name, price, number of reviews, rate, and link. We further highlighted the web scraper's capabilities by assimilating the results into an interface that integrates data visualization techniques to analyze the results gathered. The web scraper proved to be efficient upon execution, in which it scraped five pages and analyzed them, and visualized the information in approximately ten seconds. The limitations as a result of this implementation mainly revolved around applying it to specific product names rather than generic ones and extracting specific information that we wanted from the resulting products. Moreover, BeautifulSoup cannot extract all the data available to not compromise on speed. Further studies can be done by researchers who wish to reuse this implementation and modify it according to the data they want to extract, the analysis they wish to perform, and the website they wish to scrape. The implementation can be helpful, thus, to developers who are novices in the web scraping field or to researchers that wish to reuse the code for small data analytics projects.
Arwa Alajroush, Rabia Latif, and Tanzila Saba
IEEE
The term "cloud computing" refers to a method of managing and using resources remotely. Networks, servers, applications, machines, and other resources and storage are all part of the shared and virtualized resource pool that is offered. In addition to providing the flexibility, stability, and scalability many organizations need the services of cloud computing, this reduces the need for setting up the private cloud and deploy separate hardware resources. However, multi-could settings presented several security and trust issues. This paper primary contribution is an examination of contemporary, serious trust challenges that arise in a multi-cloud context. And to put out a survey comparing trust management frameworks for Multi-Cloud environment to address the challenges identified. The suggested solution is given to have a more robust and secure trust framework based on data compliance.
Rabia Latif, Malik Uzair Ahmed, Shahzaib Tahir, Seemab Latif, Waseem Iqbal, and Awais Ahmad
Springer Science and Business Media LLC
AbstractEdge computing is a distributed architecture that features decentralized processing of data near the source/devices, where data are being generated. These devices are known as Internet of Things (IoT) devices or edge devices. As we continue to rely on IoT devices, the amount of data generated by the IoT devices have increased significantly due to which it has become infeasible to transfer all the data over to the Cloud for processing. Since these devices contain insufficient storage and processing power, it gives rise to the edge computing paradigm. In edge computing data are processed by edge devices and only the required data are sent to the Cloud to increase robustness and decrease overall network overhead. IoT edge devices are inherently suffering from various security risks and attacks causing a lack of trust between devices. To reduce this malicious behavior, a lightweight trust management model is proposed that maintains the trust of a device and manages the service level trust along with quality of service (QoS). The model calculates the overall trust of the devices by using QoS parameters to evaluate the trust of devices through assigned weights. Trust management models using QoS parameters show improved results that can be helpful in identifying malicious edge nodes in edge computing networks and can be used for industrial purposes.
Muhammad Awais, Shahzaib Tahir, Fawad Khan, Hasan Tahir, Ruhma Tahir, Rabia Latif, and Mir Yasir Umair
Elsevier BV
Tanzila Saba, Amjad Rehman, Mirza Naveed Shahzad, Rabia Latif, Saeed Ali Bahaj, and Jaber Alyami
Wiley
Afnan AlFaadhel and Rabia Latif
IEEE
The COVID-19 pandemic has greatly affected humanity by destabilizing the world economy through strain on hospital systems and deaths. Medical personnel is working around the clock to establish vaccines. On the other hand, technology contributes to the fight against the virus by tracking COVID-19 infections. Many digital contact tracking smartphone applications have been created to address this epidemic successfully. However, the applications lack transparency, raising worries about their privacy. Contact tracing has been employed to stop the spread of the disease. When battling the coronavirus epidemic, computerized contact tracking has quickly emerged as an essential tool. Therefore, the research conducted in this paper focuses on the challenges of tracking applications to analyze the perspective view of privacy issues. Besides, the paper proposes policies for data privacy to aid in making the tracking applications more effective and successful.
Gahadh Faisal AlMudahi, Lama Khalid AlSwayeh, Sara Ahmed AlAnsary, and Rabia Latif
IEEE
General knownledge presumes that social media users are increasing. As of October 2021, more than 4.5 billion people are using social networks. Popularly utilized for communication, knowledge participation, thoughts communication, videos, building networks, images, and so on. However, there is still a lack of knowledge of the consequences associated with the use of said programs, and the growth of users increases the possible vulnerabilities and attacks. This paper proposes some suggestions for mitigation of risks and threats when people publish anything online. The authors' contribution provided in this paper is the suggested solutions for each of the listed and discussed threats and risks.
Manal AlShalaan, Reem AlSubaie, and Rabia Latif
IEEE
The growing privacy concerns for location information across location-based services raise the need for advanced confidentiality and access control mechanisms to secure sensitive user information from unauthorized access or disclosure. Such privacy management strategies safeguard identifiable location information, preventing the service provider from accessing such data. This paper examines this location privacy issue by reviewing existing research papers on the problem and proposing a security solution that resolves the limitations of these strategies. This approach enhances location privacy across multiple platforms, leading to a reliable location-based service environment.
Rabia Latif
Institute of Electrical and Electronics Engineers (IEEE)
The global population is around 7.4 billion people. This population density requires connectivity to improve the standard of living by transmitting and receiving variety of services. As a result, numerous forms of communication among objects are required for our everyday living demands, independent of their nature. Furthermore, to create a good relationship, every object that is regarded as associate of another object should have distinct criteria such as scalability, interoperability, and trustworthiness. Many security threats, however, have an impact on the social interaction between objects in a social internet of things (SIoT) context, including illegal admittance and suspicious behavior owing to a lack of verification architecture. Others include attempting to provide a proper viewpoint of a malicious object to earn the trust of other objects. As a result, there is a requirement for an acceptable method to check the behavior of objects such as capability, commitment, reliability, and previous job satisfaction before proceeding with any type of job assignment. This will aid in distinguishing between malicious and trustworthy objects by anticipating their upcoming behavior, allowing better judgments regarding service assignment to be made. This study proposes a context-dependent trust management technique (ConTrust) for choosing and allocating jobs in a SIoT environment. The feature-property match approach, as well as the combination of capability, commitment, and satisfaction, were utilized to increase the efficiency of trust assessment and the resolution of context-dependent difficulties. The proposed trust model considers job characteristics, object capabilities and honesty, and the impact of malicious conduct. The experimental results show that the proposed ConTrust model is viable and capable of ensuring the reliability and efficacy of SIoT service sharing between objects as compared to the benchmark models considered in this work.
Salma Kulsoom, Seemab Latif, Tanzila Saba, and Rabia Latif
IEEE
Statement of Purpose (SOP) plays a vital role in the university admissions process as reviewers assess the personality of the students by reading their SOPs. In past, the Big Five personality traits of the students are assessed to predict their future academic performance. An exciting application of machine learning is the personality assessment using personality traits and behavior. In this paper, our focus is on developing a deep learning-based personality assessment model for the detection of Big Five Personality traits from SOP and mapping them to speculate a student's academic performance at the university. Our proposed model uses Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN) and Bi-Directional LSTM (Bi- LSTM) architectures to extract features and predict ratios of Big Five traits in the SOP. The proposed model has been trained and tested on an essays' dataset and 400 students' SOP collected from computer science undergraduate students. Maximum accuracy achieved for essays dataset is 88.2 % and for student's personal statement is 67.0 % with FastText Embedding.
Bello Musa Yakubu, Rabia Latif, Aisha Yakubu, Majid Iqbal Khan, and Auwal Ibrahim Magashi
PeerJ
The increasing number of rice product safety issues and the potential for contamination have established an enormous need for an effective strategy for the traceability of the rice supply chain. Tracing the origins of a rice product from raw materials to end customers is very complex and costly. Existing food supply chain methods (for example, rice) do not provide a scalable and cost-effective means of agricultural food supply. Besides, consumers lack the capability and resources required to check or report on the quality of agricultural goods in terms of defects or contamination. Consequently, customers are forced to decide whether to utilize or discard the goods. However, blockchain is an innovative framework capable of offering a transformative solution for the traceability of agricultural products and food supply chains. The aim of this paper is to propose a framework capable of tracking and monitoring all interactions and transactions between all stakeholders in the rice chain ecosystem through smart contracts. The model incorporates a system for customer satisfaction feedback, which enables all stakeholders to get up-to-date information on product quality, enabling them to make more informed supply chain decisions. Each transaction is documented and stored in the public ledger of the blockchain. The proposed framework provides a safe, efficient, reliable, and effective way to monitor and track rice products safety and quality especially during product purchasing. The security and performance analysis results shows that the proposed framework outperform the benchmark techniques in terms of cost-effectiveness, security and scalability with low computational overhead.
Rabia Latif, Syeda Hadia Afzaal, and Seemab Latif
Springer Science and Business Media LLC
Muhammad Waleed, Rabia Latif, Bello Musa Yakubu, Majid Iqbal Khan, and Seemab Latif
MDPI AG
With the innovation of embedded devices, the concept of smart marketplace came into
 existence. A smart marketplace is a platform on which participants can trade multiple resources,
 such as water, energy, bandwidth. Trust is an important factor in the trading platform, as the
 participants would prefer to trade with those peers who have a high trust rating. Most of the existing
 trust management models for smart marketplace only provide a single aggregated trust score for
 a participant. However, they lack the mechanism to gauge the level of commitment shown by a
 participant while trading a particular resource. This work aims to provide a fine-grained trust score
 for a participant with respect to each resource that it trades. Several parameters, such as resource
 availability, success rate, and turnaround time are used to gauge the participant’s level of commitment,
 specific to the resource being traded. Moreover, the effectiveness of the proposed model is validated
 through security analysis against ballot-stuffing and bad-mouthing attacks, along with simulationbased
 analysis and a comparison in terms of accuracy, false positive, false negative, computational
 cost and latency. The results indicate that the proposed trust model has 7% better accuracy, 30.13%
 lower computational cost and 31.74% less latency compared to the existing benchmark model.
Syeda Faizan Fatima, Seemab Latif, and Rabia Latif
IEEE
Social media allows people to express themselves, however, there exists a threat of abuse and harassment. This threat leads to a negative impact on society which results in a change in people behaviour and they stop expressing their ideas freely. Classification of unethical behaviour in comments is a multi-label classification task. Due to the limited availability of the dataset, training does not yield worthy accuracies. Hence, a large training corpus is needed. This work, therefore, proposes to supplement training data by making use of transfer learning. Bi-directional Encoder Representations from Transformers (BERT) pre-trained model is fine-tuned to detect unethical users’ behaviour. The approach used in this work achieved competitive accuracy for the task of multi-label classification on the toxicity dataset of Wikipedia Comments Corpus.
Nauman Riaz, Seemab Latif, and Rabia Latif
IEEE
This paper investigates different deep learning models for various tasks of Natural Language Processing. Recent ongoing research is about the Transformer models and their variations (like the Reformer model). The Recurrent Neural Networks models were efficient up to an only a fixed size of the window. They were unable to capture long-term dependencies for large sequences. To overcome this limitation, the attention mechanism was introduced which is incorporated in the Transformer model. The dot product attention in transformers has a complexity of O(n2) where n is the sequence length. This computation becomes infeasible for large sequences. Also, the residual layers consume a lot of memory because activations need to be stored for back-propagation. To overcome this limitation of memory efficiency and to make transformers learn over larger sequences, the Reformer models were introduced. Our research includes the evaluation of the performance of these two models on various Natural Language Processing tasks.
Atif Khurshid, Seemab Latif, and Rabia Latif
IEEE
Deep learning approaches to surface realisation are often held back by the lack of good quality datasets. These datasets require significant human effort to design and are rarely available for low-resource languages. We investigate the possibility of cross-lingual transfer learning of grammatical features in a multilingual text-to-text transformer. We train several mT5-small transformer models to generate grammatically correct sentences by reordering and inflecting words, first using monolingual data in one language and then in another language. We show that language comprehension and task-specific performance of the models benefit from pretraining on other languages with similar grammar rules, while languages with dissimilar grammar appear to disorient the model from its previous training. The results indicate that a model trained on multiple languages may familiarize itself with their common features and, thus, require less data and processing time for language-specific training. However, the experimental models are limited by their entirely text-to-text approach and insufficient computational power. A complete multilingual realisation model will require a more complex transformer variant and longer training on more data.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
Amman Durrani, Seemab Latif, Rabia Latif, Haider Abbas,"Detection of Denial of Service (DoS) Attack in Vehicular Ad hoc Networks: A Systematic Literature Review", Ad Hoc & Sensor Wireless Networks, 2017.
Nazish Yaqoob, Seemab Latif, Rabia Latif, Haider Adaptive Rule based Approach to Resolve Real Time VoIP Wholesale Billing Dispute", Customization of Software Engineering Principles for Rapid Mobile Application Development, Journal of Information Science and Engineering, 2017.