Machine learning models, despite achieving low error rate, continue to encounter trust issues, particularly in domains where a single incorrect prediction can be costly or disastrous. WisdomNet architecture facilitates to achieve a zero error rate if certain conditions are met by rejecting data instances it is uncertain about and delegating those cases to a human expert. However, WisdomNet still faces several challenges, such as a high rejection rate and determining the appropriate point to stop fine-training. In this paper, we propose a novel technique called Difficulty-Driven Fine Training (DDFT), which not only determines when to stop fine-training but also minimizes the rejection rate. This technique focuses on excluding difficult data from the validation set, and fine-training WisdomNet with misclassified samples until the new validation set achieves a zero error rate. We conducted experiments to identify factors contributing to increased rejection rates in certain datasets. Our experimental results show that our method reduces the rejection rate from 39.90% to 30.53%, 96.62% to 23.25%, 89.09% to 9.80%, 5.24% to 1.65%, 94.4% to 20.55% in drybeans(Sira-Dermasons), banana quality, FashionMNIST(Dress-Shirt), MNIST(2-7) and CIFAR10(Cats-Dogs) datasets, respectively, compared to an ideal WisdomNet.

Encrypted network traffic classification has become a critical task with the widespread adoption of protocols such as HTTPS and QUIC. Deep learning-based methods have proven to be effective in identifying traffic patterns, even within encrypted data streams. However, these methods face significant challenges when confronted with new applications that were not part of the original training set. To address this issue, knowledge transfer from existing models is often employed to accommodate novel applications. As the complexity of network traffic increases, particularly at higher protocol layers, the transferability of learned features diminishes due to domain discrepancies. Recent studies have explored Deep Adaptation Networks (DAN) as a solution, which extends deep convolutional neural networks to better adapt to target domains by mitigating these discrepancies. Despite its potential, the computational complexity of discrepancy metrics, such as Maximum Mean Discrepancy, limits DAN’s scalability, especially when applied to large datasets. In this paper, we propose a novel DAN architecture that incorporates Smooth Characteristic Functions (SCFs), specifically SCF-unNorm (Unnormalized SCF) and SCF-pInverse (Pseudo-inverse SCF). These functions are designed to enhance feature transferability in task-specific layers, effectively addressing the limitations posed by domain discrepancies and computational complexity. The proposed mechanism provides a means to efficiently handle situations with limited labeled data or entirely unlabeled data for new applications. The aim is to limit the target error by incorporating a domain discrepancy between the source and target distributions along with the source error. Two statistics classes, SCF-unNorm and SCF-pInverse, are used to minimize this domain discrepancy in traffic classification. The experimental results demonstrate that our proposed mechanism outperforms existing benchmarks in terms of accuracy, enabling real-time traffic classification in network systems. Specifically, we achieve up to 99% accuracy with an execution time of only three milliseconds in the considered scenarios.

Ethereum smart contracts offer innovative ways to automate transactions and execute agreements within blockchain systems. However, its inherent complexity can lead to exploitable vulnerabilities. With the advent of large language models, many studies put a special focus on identifying vulnerabilities using these models. Nonetheless, language models are ineffective with the lengthy input sequences. To overcome this limitation, this work proposes a novel multi-label vulnerability detection mechanism using pre-trained language model CodeT5+ combined with a unique multi-block fusion. The results demonstrate that the proposed mechanism can achieve up to 0.998 F1-score and require only 0.39 ms of processing time on a collected dataset comprising 421,266 contracts from Ethereum.

Traffic classification plays an indispensable role in Computer Networks and the Internet of Things. As the cybersecurity landscape evolves, a diverse array of encrypted protocols (e.g., HTTPS, GQUIC, and TLS) is becoming increasingly prevalent. Alongside this, the challenge of encrypted traffic classification has garnered renewed attention, fostered by the increasing adoption of Deep Learning (DL) methodologies. Nonetheless, the fast-paced release of new encrypted protocols necessitates frequent retraining of DL models on reformed datasets encompassing encrypted traffic from both known and unknown applications. This requirement can lead to the issues of catastrophic forgetting, particularly when classifying unknown applications. To address this shortcoming, we propose a novel two-stage Incremental Learning (IL) paradigm based on flow-exemplar selection strategy and model pruning, CoSP, to enable continuous model evolution with unknown applications. Extensive experiments on encrypted traffic datasets in a Software-defined networking environment illustrate that our method outperforms other IL approaches, achieving 1.07% and 0.94% improvements in last accuracy and forgetting, respectively.

In the dynamic realm of cybersecurity, ensuring network infrastructure security is an imperative task. With organizations increasingly relying on interconnected systems for their operations, robust and trustworthy defenses against malicious activities are necessary. Network Intrusion Detection Systems (NIDS) play a pivotal role in this defense, functioning as vigilant guardians that monitor network traffic for suspicious patterns and potential security threats. This study introduces a trustworthy NIDS designed not only to detect attacks accurately but also to abstain from making predictions in case of doubt. In the cases of unsure predictions, the system chooses to reject the predictions, thus increasing the correctness of the NIDS results. The rejected cases can be deferred to a human administrator for further verification. The methodology utilizes two approaches: WisdomNet trustable neural networks and Uncertainty Estimation with Monte Carlo dropout. The proposed method can be applied to pre-trained NIDS models to enhance their trustworthiness. Evaluation results demonstrate that the method effectively reduces the classification error rate to zero while categorizing challenging or uncertain predictions as ‘reject’ at a substantial rejection rate.

This study proposes an interesting approach for vehicle-to-vehicle (V2V) communication, which integrates blockchain technology, federated learning (FL), and allocation optimization of latency and resources. The research evaluates the proposed system using various performance metrics such as packet delivery ratio (PDR), model accuracy, and latency and demonstrates its superiority over existing techniques. Further-more, the system provides enhanced security through consensus optimization and k-anonymity for data privacy. Overall, the proposed system is a promising solution for efficient and secure V2V communication in the era of connected and autonomous vehicles. Moreover, the proposed approach achieves higher reli-ability, lower latency, and better resource utilization compared to traditional 5G.

Server and route selection (SARS) optimization is a critical aspect of traffic engineering to allocate network resources to meet diverse service requirements effectively. Existing studies have primarily focused on finding profitable or optimal solutions for the SARS problem within current time steps, considering specific constraints. However, they often have failed to address the dynamic and uncertainty of future network states. To address this gap, this paper proposes an algorithm named GAL to optimize server costs and response time while accounting for future network dynamics. GAL combines a server selection inspired by the gambling theory and a network routing based on Long Short-Term Memory Networks (LSTM). The server selection method is formulated as a gambling problem and solved using the decision-making Tug-of-War (TOW) dynamic algorithm. The routing mechanism is optimized based on predictions of future network states made by LSTM neural networks, which excel in capturing long-term dependencies. We have implemented GAL through a distributed software-defined networking (SDN) system and obtained good evaluation results regarding average response time and server cost compared to benchmark methods. These results demonstrate that GAL can effectively tackle the SARS optimization problem by considering present constraints and future network dynamics. This study can advance traffic engineering and lays a foundation for more robust resource allocation strategies in dynamic network environments.

Software-defined networking (SDN) is a novel net-working paradigm for network management and autonomous systems. However, SDN has some challenges with scalability and quality of services (QoS) in distributed multi-domain scenarios due to the unprecedented growth of heterogeneous characteristics services. There is a current gap in a standard routing mechanism for satisfying various service requirements in distributed SDN. Most existing works design a homogeneous routing strategy for heterogeneous services, which might need to be more scalable and efficient for the future of rising heterogeneous online services. This study proposes a multi service-oriented routing mechanism for multi-domain SDN, which aims to help Internet service providers (ISPs) achieve high QoS and service-level agreements (SLAs). The mechanism utilizes a service classification (through a deep learning model) and optimizes network routing (using a new cost function containing both QoS and the server load). The mechanism has been integrated into the Knowledge-defined heterogeneous network architecture and tested on four prevalent considered services: E-commerce, Interactive Data, Video On-demand, and Bulk Data Transfer. The experimental results indicate that the proposed service-oriented routing mechanism outperforms the benchmark in terms of faster server response time while reducing up to 25% of the network congestion.

Network traffic management is crucial in Computer Networks and the Internet of Things. Indeed, classifying network traffic is the foundation for enhancing the quality of management mechanisms. However, traditional traffic classification methods, such as port-based, deep packet inspection, and statistic-based, are limited in identifying new encrypted traffic characteristics. Deep Learning-based classification approaches that consider packet-based features have been explored to address this challenge. Along with other deep learning methods, Transfer Learning, where a new model can inherit knowledge previously learned by a base model, is commonly used to increase classification performance in low data resources. Unfortunately, feature transferability may decline in transfer learning. This paper proposes an encrypted traffic classification mechanism that leverages the Deep Adaptation Network architecture with Mean Embedding Test to overcome this limitation. Our experimental results show that the proposed mechanism surpasses existing benchmarks’ accuracy and can classify encrypted traffic in real-time.

Tracking the motions and positions in three-dimensional space is an exciting human gait analysis approach for healthcare and clinical examination. The high-end motion capture systems, such as Vicon cameras, could automate the gait analysis process, but the system is too costly for a clinical setting. We developed a cost-effective motion capture and gait analysis system using an existing commercial camera found on a mobile device. We take advantage of the Artificial Intelligence and Machine Learning technique in mobile devices for human pose estimation and detection to keep track of body motion in the environment. This paper presents our mobile application case study to measure three-dimensional kinematics of lower-body gait of the hip, knee, and ankle during walking tasks using the Apple Augmented reality toolkit. We evaluated our application by comparing it with the measurements observed by a Vicon T40s motion-tracking system. The results showed that the gait movement can be measured in real-time. The lower-gait angles of hip and knee agreed with those from the Vicon system, whereas the motions of small joint sections were not captured as accurately. The proposed application is a cost-effective, easy-to-use, and mobile gait analysis system that can be used to construct three-dimensional gait scores to improve the examination of gait and decision making in various clinical populations.

The crystallization of biological macromolecules like proteins is an important process to study their molecular structures. The quality of crystals is critical to be able to determine their structures using methods such as X-ray crystallography. Therefore, many wet-lab experiments are conducted using numerous screening plates to obtain successful crystal growth. High-throughput microscopy is useful to quickly collect images from the screening plates. Since the automated systems for imaging require high-end instrumentation, they are costly. This study investigates a small scale, mobile fluorescence imaging system, and application. Our system is composed of a mobile imaging system, a mobile app to capture images from plates, and a machine learning model to recognize the presence of crystals presence from images. For fluorescence imaging, we present an assembly of a smartphone or tablet integrated with a macro lens tube and illumination LEDs. The system presented in this study has magnification range from 20x to 250x macro. For the recognition of crystals, a convolutional neural network model was trained on a computer and then deployed on the mobile app. A data set of 1000 trace fluorescently labeled images was used to train and evaluate the model. The accuracy of the hold-out testing images was about 95%. The mobile app for imaging and protein recognition was developed to run on Apple iOS devices. To evaluate the system further, the recombinant inorganic pyrophosphatase protein from Klebsiella pneumoniae, which was expressed from E. coli, was crystallized using the trace fluorescent labeling method. Our system can capture quality images of protein crystals in both white and fluorescence lights. The overall accuracy of recognizing crystal or non-crystal outcomes on the pilot test is about 93%. This mobile imaging system can be useful for small group research labs and students.

The data representation as well as naming conventions used in commercial screen files by different companies make the automated analysis of crystallization experiments difficult and time-consuming. In order to reduce the human effort required to deal with this problem, we present an approach for computationally matching elements of two schemas using linguistic schema matching methods and then transform the input screen format to another format with naming defined by the user. This approach is tested on a number of commercial screens from different companies and the results of the experiments showed an overall accuracy of 97 percent on schema matching which is significantly better than the other two matchers we tested. Our tool enables mapping a screen file in one format to another format preferred by the expert using their preferred chemical names.

Protein crystallization well plate is a rectangular platform that contains wells usually organized as a grid structure. The crystallization conditions are studied through a screening process by setting up the trial conditions in the well plate. In the past, the expert evaluates the trial wells for the growth of crystals by manually viewing the plate under a microscope or using a high-throughput plate imaging and analysis system. While the first method is tedious and cumbersome, the second method requires financial investment. Recently, a few approaches were developed by collecting images using smartphones thus enabling low-cost automatic scoring (classification) of well images. Nevertheless, these recent methods do not detect which well on the plate is captured. If the user has a smartphone, the user may capture or scan any well by just moving the smartphone to the corresponding well. In this paper, we propose a mobile scanner that identifies the well by using a coded template under the well plate. The mobile scanner provides two modes: image and video. Image mode is used for single well analysis whereas video mode is used to scan the complete plate. In the video mode, the mobile scanner app generates a tilemap of the plate.

This paper presents a new method of segmenting and classifying protein crystallization trial images that were collected using trace fluorescent labeling. Trace fluorescent labeling typically involves fluorescence dye that can re-emit the illumination light at other wavelengths around the principal wavelength. The captured image has a primary color channel with respect to illumination light and fluorescence dye. Crystals will have higher intensity than non-crystal areas. But there might be bright regions that may not be crystals, thereby making inaccurate and not robust trial images classification. In this paper, we utilize the subordinate color channel besides the primary color in the image of trace fluorescently labeled protein solution. This new method extracts proper features and successfully builds a high accuracy classifier with a low rate of misclassification of crystals as non-crystals. We also present a framework that could optimize both image segmentation and classification. In our experiments, we achieved around 94% accuracy with 0.6% misclassification of crystals as non-crystal.