2023

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A BLOCKCHAIN BASED FRAMEWORK FOR POTATO SEED TRACEABILITY IN AGRICULTURE SUPPLY CHAIN MANAGEMENT
(UMT, Lahore, 2023) FATIMA NAWAZ
A CIPHER PROPOSAL BASED ON A NOVEL TRIGONOMETRIC CHAOTIC MAP
(UMT, Lahore, 2023) UMER NAWAZ
In the ever-evolving digital era, the sharing of information has become commonplace across numerous platforms and channels. However, this widespread exchange also exposes data to potential vulnerabilities and threats. To counteract such risks, the implementation of a cipher algorithm becomes imperative. Modern ciphers utilize substitution boxes to introduce chaos and fortify data security. In this study, a unique substitution box is constructed utilizing trigonometric functions. To assess the effectiveness of the substitution box, its properties are thoroughly evaluated. Additionally, a novel block cipher is introduced, utilizing the substitution box to encrypt 128 bits of data. The cipher consists of 16 encryption rounds and necessitates a 128-bit key for executing multiple operations. Each round involves the generation of a distinct key for encryption purposes. Comprising four primary functions and a pre-function, the cipher incorporates the designed substitution box to offer substantial complexity, thus ensuring a high level of encryption standard through its chaotic behavior. To validate the efficiency of the proposed cipher, the constructed substitution box undergoes meticulous evaluation using various metrics, while the cipher itself is extensively tested. The obtained results unequivocally demonstrate that the proposed cipher outperforms traditional ciphers, significantly enhancing data security. Furthermore, the proposed cipher exhibits scalability and can be tailored to specific requirements, thereby offering versatility in its application.
A CYBERBULLYING MULTICLASS CLASSIFICATION WITH AN ATTENTION MECHANISM HANDCRAFTED CNNBERT WITH BILSTM
(UMT, Lahore, 2023) Ammara Asghar
Cyberbullying is an important problem that affects people across the globe and is unpleasant for both the individuals who are bullied and society overall. One type of bullying that occurs on several social networking platforms is referred to as cyberbullying. Twitter has become one of the most well-known locales for posting recordings, with a great many tweets. AI and normal language handling procedures are utilized to investigate the remarks. However, for cyberbullying analysis, many machine learning models analyze comments in fixed-length frames or sequences, which results in the loss of long-term context information. This postulation explores an original technique for recognizing cyberbullying that consolidates Convolutional Brain Organizations with Consideration System BERT (CNNBERT) and Bi-directional Long Momentary Memory (BiLSTM) models. This exploration utilizes a half and half profound learning way to deal with create a complex model that can precisely distinguish and characterize cyberbullying content across online entertainment stages. The proposed technique performs effectively by evaluating text-based data to identify complex instances of cyberbullying. Combining BiLSTM expert ability to process sequential input with the attention mechanism of BERT and CNN, which enhances feature extraction.
A NOVEL CUBIC CHAOTIC MAP FOR SBOX DESIGN AND CIPHER
(UMT, Lahore, 2023) MUHAMMAD AHMAD
The rapid advancements in technology have resulted in a significant rise in the quantity of data being shared and transmitted across various sources on the network. With data being a critical aspect of modern life, its importance cannot be overstated. Unfortunately, this also makes it easier for hackers to gain access to and steal sensitive information. To maintain data security and protect against such threats, secure connections are essential. Cryptography is employed to enhance the security of private data and communication networks, utilizing various substitution and permutation strategies. Many researchers have developed algorithms using dynamic substitution boxes to bolster security against attackers. However, since some of these S-boxes are weak and ineffective, there is a constant need for strong S-Boxes. In this thesis, a robust key-based substitution box (S-Box) based on the 1-D chaotic method is proposed and its strength using standard benchmarks has been evaluated. The results are promising that indicate that the proposed S-Box could be utilized in modern ciphers with confidence. A new cipher has been designed to use the proposed S-Box to provide security of data. The proposed cipher has eight rounds and each round is performing the same operations. The obtained results of our proposed S-Box are comparatively worthy. The cipher's proposed plaintext size is 256 bits, while the key length is 256 bits.
A RADICAL CHAOTIC MAP-BASED S-BOX AND ITS APPLICATION IN A NEW CIPHER
(UMT, Lahore, 2023) KAINAAT MALIK
The broad usage of communication technology nowadays suggests the necessity for certain security measures to be put in place. Different ciphers are being created to do this by utilizing numerous strategies. These ciphers are currently being created using chaotic maps. Using chaos in cryptographic algorithms has the advantage of making data more unstable and inconsistent. A substitution box is a unique and nonlinear essential component of block ciphers. To fend off modern attacks, extremely diffusive S-Boxes are required as nonlinear confusion sublayers in cryptosystems. By adopting better substitution box design approaches, a ciphertext of higher quality can be made. In this research, a novel chaotic map is suggested and used to create a new S-Box. A new cipher is created employing the suggested S-Box and a new function. Along with the decryption procedure, the key generation approach for the cipher is also described. Nonlinearity (NL), Linear Approximation Probability (LP), Differential Approximation Probability (DP), Strict Avalanche Criteria (SAC), and Bit Independence Criteria (BIC) are among the parameters taken into consideration when analyzing and evaluating the proposed S-Box's effectiveness against various cyberattacks. The results of the performance test show that the suggested S-Box has strong cryptographic characteristics, is resistant to several attacks and is appropriate for use in modern ciphers.
AN ENCRYPTION ALGORITHM BASED ON AN INNOVATIVE CHAOTIC MAP
(UMT, Lahore, 2023) MUHAMMAD ANS
Many companies possess data or information that only a select group of authorized individuals can evaluate. This information may be owned by a country's telecom industry, software firms, financial institutions, or the military. As the world moves towards digital growth, this dataset is being converted into a digital format, but it comes at a cost. While online information is reasonably secure, cyberattack development is also advancing. Therefore, cryptography and its numerous techniques are employed to ensure data security and integrity. Substitution boxes and permutation algorithms are frequently used in cryptography to safeguard sensitive data. S-boxes, due to their dynamic nature, are a suitable choice for security purposes in the present day. In this thesis, a simple yet effective cipher with three basic components is constructed. These components include key creation, the encryption procedure, and decryption. For the reader's convenience, their schemas and examples are also provided. After creating the S-Box is assessed using non-linearity, BIC, SAC, LP, and DP S-Box attributes. The outcomes demonstrate that the suggested cipher is robust enough to be applied to real-time applications
AN ENCRYPTION SCHEME BASED ON LOGARITHMIC SQUARE POLYNOMIAL
(UMT, Lahore, 2023) HINA ZAFAR
In today's digital era, information is a valuable resource that is routinely transferred among many resources through various communication methods. Such information exchange, however, exposes the data to possible assaults and loss. A cipher algorithm is required to reduce such hazards. A substitution box is used in ciphers to produce chaos and safeguard information. This study describes a new cipher that uses a chaotic substitution box to improve data security. A substitution box is produced in this study utilizing trigonometric functions. The parameters of the substitution box are used to assess its robustness. A novel block cipher is also developed, which leverages the suggested substitution box to encrypt 128 bits of data. The cipher has 8 encryption rounds and requires 128 bits of key to accomplish multiple operations. For encryption, a new key is created for each cycle. Because of its chaotic behavior, the suggested substitution box is meant to give great complexity, and the cipher delivers a high degree of encryption standard. To validate the suggested cipher's efficacy, the created substitution box is assessed using several metrics, and the cipher is fully tested. The results show that the suggested cipher provides better data security than standard ciphers. The enhanced S-box and the suggested encryption technique work together to deliver increased security while preserving computing efficiency, demonstrating a substantial advancement in data protection paradigms.
ANTIMICROBIAL PEPTIDES PREDICTION USING MACHINE-LEARNING ALGORITHMS
(UMT, Lahore, 2023) ROBBIA GULNAR
Evolution of drug resistant microbes develop interest among scientists in developing, discovering and re-engineer antibiotics. Antimicrobial resistance is major global health problem to combat with the issue there is need of antimicrobial treatment such as development of antimicrobial peptides. AMPs have received increasing attention as a treatment for several infectious diseases, including bacterial infections, viral infections, and fungal infections. They have several advantages over conventional antibiotics, including broad-spectrum activity, the potential to reduce resistance, and the ability to kill microorganisms quickly. However, there are several challenges associated with the development of AMP-based therapeutics, including stability, toxicity, and manufacturing cost. Despite these challenges, the potential benefits of AMPs make them an attractive research area for the development of new antimicrobial agents. Fortunately, AMP sequences with potential bactericidal properties have evolved naturally because of competition within complex communities. Unfortunately, the discovery, description, and production of AMPs is difficult and time-consuming. In this study, we used dataset of 2638 AMPs and 3700 non-AMPs to build model for AMP Prediction. We explore nine various models of “machine learning are Random Forest, Decision Tree, Naive Bayes (NB), and Support Vector Machine”, Quadratic Discriminant Analysis, Linear Discriminant Analysis, Extreme Gradient Boosting, k-Nearest Neighbor, and Light Gradient Boosting Machine (LGBM). In comparison to previous models, the LGBM classifier outperformed than all with an accuracy rate of 93.85%, and properly predicting both positive and negative datasets of AMPs. Our discussion of machine learning predictions of antimicrobial peptides concludes with developments, limitation, and potential future approaches.
Cost-Efficient, Theft Control, IoT based Electricity Metering, Monitoring & Smart Pre-Payment System
(UMT, Lahore, 2023) ABDUR REHMAN AHMAD
Electric energy is a basic need for humans in various areas such as domestic tasks, agriculture, and industries. Unfortunately, there are numerous issues related to the distribution, metering, and billing processes of electricity. The conventional method of measuring used electricity and issuing bills is complex and time-consuming, involving manual meter readings by a meter reader who visits each home and returns to the office to issue bills. Electricity theft and many other economic issues are also linked with existing metering system. This research work discuss the various problems faced with retrieving meter data and generate bills manually and theft control was also discussed in detail in this research. To address these challenges, an automatic system for metering and billing was proposed. The solution involves the development of a prepaid digital energy metering system and Pre-payment system. The system consists of two major components: a cost-efficient and highly accurate smart power meter and a Pre-payment system that allow consumers to add credit to their meters. The power meter was designed using ACS712 as a current sensor, ZMPT101 as a voltage reader, Arduino as a microcontroller board, esp8266 WiFi module for communication with an authorized server, and MFRC522 sensor for reading NFC tags used in the Pre-payment system. The proposed system enable real-time monitoring of energy consumption and allow users to consume power based on their prepaid amount. The voltage sensor achieved 99 % accuracy and current sensor achieved 97% accuracy.
DEEP LEARNING METHODS FOR PV POWER FORECASTING
(UMT, Lahore, 2023) RABBIA IJAZ
This study focuses on the precise forecasting of photovoltaic (PV) power, considering the intermittent and weather-dependent characteristics of solar energy. However, obtaining weather data and atmospheric measurements can be prohibitively expensive, particularly in developing nations. To address this challenge, we propose an innovative approach that utilizes deep learning-based prediction models solely relying on PV power data for accurate forecasting. Specifically, we investigate the application of two potent deep learning architectures, namely Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), in PV power prediction. LSTM effectively models and captures long-term dependencies in time series data, while CNN excels in extracting spatial features. Furthermore, we propose an ensemble model that combines LSTM and CNN predictions through a weighted average approach, leading to improved accuracy and predictive power compared to individual models. This ensemble methodology offers a practical and cost-effective solution for PV power forecasting in developing countries, where weather data may be limited, thus facilitating the seamless integration of renewable energy resources into the power grid.
Design of a Chaotic Nonlinear Component for a Novel Encryption Method
(UMT, Lahore, 2023) AYESHA ASHRAF
Computerized systems often hold large quantities of data. While the information is being processed, if adequate safety measures are not implemented, it might be stolen or used inappropriately. Failures in data management, including sending private information outside of secure settings, might result in data being misused. Data security is crucial to prevent this by shielding confidential data from unauthorized access by attackers. Many years ago, ciphers formed the foundation of cryptographic systems used to secure data. Key creation, encryption, and decryption are the three stages that make up a cipher. Researchers advise using S-Boxes, chaotic maps, and permutation functions in ciphers to enhance data security. There are many distinct ciphers, however as time passed, hackers discovered many techniques to decipher the ciphered material, necessitating the invention of new ciphers in the realm of cryptography. Using the use of trigonometric and algebraic functions, I have presented a cipher in this work that utilizes a novel encryption and decryption process using a chaotic map for the substitution box. The suggested cipher is nonlinear by nature, but I will attempt to make this cipher more secure and powerful to achieve better accuracy and outcomes.
DESIGN OF A RADICAL CHAOTIC MAP S-BOX AND A NOVEL ENCRYPTION METHOD
(UMT, Lahore, 2023) SHAHEER ALI
Information is a vital resource that drives the functioning of numerous systems and processes in the modern digital era. Routinely transferred across various platforms via a variety of communication routes, information serves as the backbone of our interconnected world. However, such widespread information interchange also exposes the data to the constant risk of loss and malicious attacks. This study offers a unique encryption that boosts data security by using a chaotic substitution box. The substitution box is one of the key elements of the cipher, crucial for creating chaos in the data. An encryption algorithm is required to reduce these hazards. A substitution box is used in a cipher to create confusion and protect the data. In this research, chaotic formula issued to build a substitution box. Furthermore, a novel encryption method is presented, which encrypts 256 bits of data using the proposed substitution box. The cipher uses 128 bits of key to accomplish several operations across its 16 rounds of encryption. A unique key is created for encryption for each round. Due to its chaotic behaviour and the proposed substitution box's serious complexity, the cipher offers a high level of encryption standard. The produced substitution box is analysed using several metrics, and the proposed cipher undergoes extensive testing to validate its efficacy. The outcomes show that the suggested encryption provides better data security when compared to conventional ciphers. Likewise, the suggested cipher is adaptable and can be changed to meet specific needs.
DETECTING MULTI-LABEL ATTACKS IN AC MICROGRIDS BY USING MACHINE LEARNING
(UMT, Lahore, 2023) Saman Liaqat
In today's competitive market, power utilities are increasingly turning to innovative smart grid technologies to improve the efficiency of their grids. However, this comes with a heightened risk of cyber-attacks, making strong cybersecurity measures essential for the protection of power grids. To detect and counteract false data injection (FDI) assaults, this study suggests a multi-label attack detection method for AC microgrids. In the secondary control of AC microgrids, the method uses frequency and voltage control variables as an optimization problem. The task is formulated as a multi-label classification problem to overcome the difficulty of finding the co-occurrence dependencies and discrepancies in power flow measurements brought on by FDI attacks. This allows for the use of a single model to detect various types of attacks and changes in load, improving the efficiency of the attack detection process. The proposed technique is compared against five different machine learning approaches, and its performance is evaluated using the IEEE 34-bus distribution test system. The results demonstrate effectiveness of the multi-label attack detection technique in identifying and mitigating cyber-attacks in AC microgrids. Overall, this paper provides valuable insights into the development of effective cybersecurity measures for power utilities, assisting to guarantee the secure and reliable operation of electricity grids in today’s competitive market.
DEVELOPMENT OF A NOVEL CIPHER BASED ON AN INNOVATIVE NON-LINEAR COMPONENT
(UMT, Lahore, 2023) MUHAMMAD HAZEEL AHMED
Security of data is the major part of society and with day to day life it is becoming the need of today’s world. To facilitate the society, different modes of securing the information are being used. Cryptography is used to make personal information more secure by using different ways to change and rearrange data. Different ciphers have been introduced with various methods and algorithms to secure the data. Modern ciphers are using Substitution Box to provide the security of such data. The working of substitution box is to make data meaningless for an attacker. In this article we have proposed a new chaotic map in order to design an innovative cryptographic cipher with the help of a new and strong S-box by adopting modular functions on the other hand comparing it with NL, FP, SAC, BIC, LP and DU of other S-boxes. The approach towards innovative chaotic map is furthermore efficient in the production of the stronger S-Box rather than others. Our proposed substitution box values are familiar and close to the other S-boxes.
DISTANCE LEARNING TECHNICAL & NON-TECHNICAL ISSUES AND THEIR SOLUTIONS
(UMT, Lahore, 2023) Yasmeen Farid
Distance learning is any form of remote education where the student is not physically present in the class. There were many reasons to start distance learning all over the world. The main purpose of which was to give people in remote areas access to higher education and educating people who for some reason couldn't take physical classes. Today, thanks to Distance learning, you can easily read any subject and learn any skill. In those special days of COVID-19, we had no choice but to move towards online education. There is a lot of damage to our education in the Special COVID-19 pandemic Situation anyway, but if we did not adapt to online education, there would be more damage. There are many advantages of online education, but there are also we discuss some technical issues. Distance learning is any form of remote education where the student is not physically present in the class. There were many reasons to start distance learning all over the world. The main purpose of which was to give people in remote areas access to higher education and educating people who for some reason couldn't take physical classes.
EdgeEnergy: A blockchain-based energy trading framework for smart homes & grids
(UMT, Lahore, 2023) Syed Arbaz Haider Gilani
The prevalence of crowd-scale distributed energy generation encourages the concept of peer-to-peer (P2P) electricity sharing among electricity prosumers (producer and consumer). This thesis explores ”Edge Energy” – a distributed electricity trading system over blockchain to facilitate P2P electricity trading. The proposed system orchestrates the P2P electricity distribution network as a hierarchical network of electricity prosumers, where each prosumer has capabilities for communication and energy routing. The first layer (i.e. core layer), presents the coalitions of micro-grids. The second layer is the electricity distribution network layer, i.e. a software-driven energy router’s network. Lastly, the edge layer presents the end-prosumers. Edge Energy is designed as a blockchain-assisted multi-agent system to support the prosumers network and an agent coalition mechanism to form coalitions and negotiate electricity trading. Considering the P2P nature of the proposal and the intrinsic trust deficit between the stakeholders, the market-related functionalities (i.e. matching, trading, and transaction settlement are being performed over the blockchain using smart contracts). Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process.
ENHANCING TWITTER CONTENT MODERATION: A SOFT VOTING CLASSIFIER FOR OFFENSIVE AND HATEFUL TWEETS
(UMT, Lahore, 2023) HUSSAIN SHAHID
The widespread use of social media, such as Twitter, has transformed global communication but has also led to a challenge: the presence of harmful and discriminatory content. This study introduces a Soft Voting Classifier model to enhance Twitter content moderation by accurately identifying offensive and hateful tweets. This model employs ensemble learning, combining deep learning, traditional machine learning, and rule-based systems to thoroughly assess tweets and improving the classification process. Effective model performance relies on feature selection. Identifying offensive and hateful content demands a deep understanding of language and context. The model uses a hybrid approach, integrating linguistic features like n-grams and sentiment scores with advanced features like word embeddings and contextual embeddings, considering both explicit and implicit indicators. To optimize the model's performance, careful fine-tuning involves hyperparameter selection, cross-validation, and training on a large dataset. Comprehensive evaluation metrics like precision, recall, F1-score, and accuracy gauge its effectiveness in identifying abusive tweets. This model exhibits robust performance with an accuracy of 0.94 on individual dataset and on the combined dataset. In summary, this study presents a Soft Voting Classifier model tailored for Twitter content moderation. By leveraging multiple base classifiers and advanced feature selection, it offers the potential to create safer online environments, promoting responsible digital interactions. As the digital landscape evolves, a heightened emphasis on content moderation technologies becomes essential, reducing harmful content and fostering a more inclusive digital space for Twitter users.
False data injection attack detection using machine learning
(UMT, Lahore, 2023) WAFA NOOR
With the rapid integration of smart grid technologies and increased reliance on communication networks, the security of power systems has become a critical concern. False Data Injection (FDI) attacks pose a significant threat to the stability and reliability of smart grid operations. This research aims to evaluate a novel approach for FDI attack detection in smart grids using machine learning techniques. The research explores various machine learning multi-label algorithms, such as Label Powerset, Classifier Chain, Binary Relevance, and ML-KNN, to analyze power system data and identify anomalies indicative of FDI attacks. The 14-bus power system and simulated attack scenarios are utilized to evaluate the proposed approach’s effectiveness and robustness. Results were evaluated based on row accuracy, precision, recall, and F1-score. ML-KNN outperforms all the other methods by giving 99.1% row accuracy. The findings of this research contribute to enhancing the security of smart grids against cyber threats and lay the foundation for further advancements in FDI attack detection using machine learning methodologies.
LONG RANGE, LOW-COST AUTOMATICS SMART DRIP IRRIGATION SYSTEM USING LORALORAWAN AND HETZNER SERVER
(UMT, Lahore, 2023) Anas Mohammed Ameen Bakri
The idea of Internet of things (IOT) or Internet of everything or the industrial internet of things was proposed by Kevin Ashton. It is a new technology envisioned as a global network of sensors, actuators, networking, gateways (things) etc and these devices are capable of interacting with each other. IOT can be used for the solution of different problems. IOT is a most important area of future emerging technologies. It is gaining keen attention from a wide range of different industries. Water shortage is a great problem in Pakistan for our agriculture sector. Millions of acres of land cannot cultivate due to this problem. Now a day, Traditional farming is adversely affected by improper water resource management. Normally, in Pakistan farmers are using flood irrigation system. This technique needs excessive amount of water. Sensors based precision irrigation system has the potential to improve water usage efficiency and reduce the cost. In this research work, a sensor-based drip irrigation system was developed. The system was developed by using moisture sensors, Humidity sensor, temperature sensor, wind speed sensors etc. LoRa / LoRaWAN were used for connectivity of sensors.
LYSINE ACETYLATION SITE PREDICTION IN PROKARYOTES A DEEP LEARNING APPROACH
(UMT, Lahore, 2023) HASSAN KALEEM
Post-Translational Modification (PTM) of proteins plays a vital role in both normal and disease states. Protein acetylation is an important PTM in eukaryotes as it greatly changes the properties of a protein including hydrophobicity and solubility. Therefore, in both metabolism and regulatory processes, acetylation and other PTMs perform a critical role. By investigating and accurately spotting lysine acetylation sites and pre-pone stop or alter faulty modifications that were previously supposed to occur, one can change the course of microbiological diseases like Bacteremia, UTI's, meningitis and others. Several models have been developed to identify lysine acetylation (Kace) sites with appreciable performances. This paper presents an improved approach to identify lysine acetylation (Kace) sites which achieves 0.951, 0.891, 0.813, 0.969, 0.946, and 1.0 MCC for B. subtilis, C. glutamicum, E. coli, G. kaustophilus, M. tuberculosis and S. typhimurium respectively. Machine Learning algorithms require feature extraction from protein sequences, which is a complex and time taking process. This study has introduced a deep learning-based model for the identification of Kace sites. The proposed approach significantly outperforms the existing approaches. The experimental results on the benchmark and independent dataset achieve significantly higher accuracy, very close to the actual labels. The source code for the proposed approach is available at GitHub for validation purposes: https://github.com/RaoHassanKaleem/Deep-Learning-algorithm for-accurate-prokaryotic-lysine-acetylation-site-predictio

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