Arif Hussain Magsi

Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy in detecting six distinct fault types, including fiber cut, fiber eavesdropping, splicing, bad connector, bending, and PC connector. Secondly, this paper assesses the classification delay of each classification algorithm. Finally, this work proposes a fiber optics fault prevention algorithm that determines to mitigate the faults accordingly. This work utilized a publicly available fiber optics dataset named OTDR_Data and applied different ML classifiers, such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT). Moreover, Ensemble Learning (EL) techniques are applied to evaluate the accuracy of various classifiers. In addition, this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) hybrid classifier. The findings reveal that the CNN-LSTM hybrid technique achieved the highest accuracy of 99% with a delay of 360 s. On the other hand, EL techniques improved the accuracy in detecting fiber optic faults. Thus, this research comprehensively assesses accuracy and delay metrics for various classifiers and proposes the most efficient attack detection system in fiber optics.

The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan–Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.

A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) has gained significant attention as an alternative solution to TCP/IP in VANET. NDN offers promising features, like intermittent connectivity support, named-based routing, and in-network content caching. Nevertheless, NDN in VANET is vulnerable to a variety of attacks. On top of attacks, an interest flooding attack (IFA) is one of the most critical attacks. The IFA targets intermediate nodes with a storm of unsatisfying interest requests and saturates network resources such as the Pending Interest Table (PIT). Unlike traditional rule-based statistical approaches, this study detects and prevents attacker vehicles by exploiting a machine learning (ML) binary classification system at roadside units (RSUs). In this connection, we employed and compared the accuracy of five (5) ML classifiers: logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB) on a publicly available dataset implemented on the ndnSIM simulator. The experimental results demonstrate that the RF classifier achieved the highest accuracy (94%) in detecting IFA vehicles. On the other hand, we evaluated an attack prevention system on Python that enables intermediate vehicles to accept or reject interest requests based on the legitimacy of vehicles. Thus, our proposed IFA detection technique contributes to detecting and preventing attacker vehicles from compromising the network resources.

Named data networking (NDN) is gaining momentum in vehicular ad hoc networks (VANETs) thanks to its robust network architecture. However, vehicular NDN (VNDN) faces numerous challenges, including security, privacy, routing, and caching. Specifically, the attackers can jeopardize vehicles’ cache memory with a Content Poisoning Attack (CPA). The CPA is the most difficult to identify because the attacker disseminates malicious content with a valid name. In addition, NDN employs request–response-based content dissemination, which is inefficient in supporting push-based content forwarding in VANET. Meanwhile, VNDN lacks a secure reputation management system. To this end, our contribution is three-fold. We initially propose a threshold-based content caching mechanism for CPA detection and prevention. This mechanism allows or rejects host vehicles to serve content based on their reputation. Secondly, we incorporate a blockchain system that ensures the privacy of every vehicle at roadside units (RSUs). Finally, we extend the scope of NDN from pull-based content retrieval to push-based content dissemination. The experimental evaluation results reveal that our proposed CPA detection mechanism achieves a 100% accuracy in identifying and preventing attackers. The attacker vehicles achieved a 0% cache hit ratio in our proposed mechanism. On the other hand, our blockchain results identified tempered blocks with 100% accuracy and prevented them from storing in the blockchain network. Thus, our proposed solution can identify and prevent CPA with 100% accuracy and effectively filters out tempered blocks. Our proposed research contribution enables the vehicles to store and serve trusted content in VNDN.

Named Data Networking (NDN) is gaining a significant attention in Vehicular Ad-hoc Networks (VANET) due to its in-network content caching, name-based routing, and mobility-supporting characteristics. Nevertheless, existing NDN faces three significant challenges, including security, privacy, and routing. In particular, security attacks, such as Content Poisoning Attacks (CPA), can jeopardize legitimate vehicles with malicious content. For instance, attacker host vehicles can serve consumers with invalid information, which has dire consequences, including road accidents. In such a situation, trust in the content-providing vehicles brings a new challenge. On the other hand, ensuring privacy and preventing unauthorized access in vehicular (VNDN) is another challenge. Moreover, NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN. In this connection, our contribution is threefold. Unlike existing rule-based reputation evaluation, we propose a Machine Learning (ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes. Based on ML evaluation results, vehicles accept or discard served content. Secondly, we exploit a decentralized blockchain system to ensure vehicles’ privacy by maintaining their information in a secure digital ledger. Finally, we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe (Pub-Sub) approach. We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection (BurST-ADMA). We used five (05) hybrid ML classifiers, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Random Forest, and Gaussian Naive Bayes. The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%. Our proposed research offers the most accurate solution to detect CPA in VNDN for safe, secure, and reliable vehicle communication.