The article discusses the transformative impact of Generative AI (GenAI) to the field of vulnerability assessment (VA) and risk management (RM) right from the beginning of their life cycle to the end in cybersecurity (CS). Through a systematic review of over 100 publications (2021-2025), we develop a comprehensive taxonomy classifying GenAI’s dual offensive and defensive applications in VA/RM. The survey spells out the dominant techniques of GenAI and also points towards challenging aspects, which include security, explainability, and trustworthiness. The resultant findings reinforce the belief that GenAI could help resolve many traditional VA/RM challenges, thus providing fertile ground for research and practice in this area.

Given the constantly increasing number of newly published vulnerabilities, manually assessing their scores (e.g., under the Common Vulnerability Scoring System) has become unfeasible. Recently, learning-based systems have been proposed to automatically predict vulnerability scores. Such systems use vulnerability indexing databases to train deep learning algorithms. However, their practical applicability has important limitations, including a high dependency on the quality and diversity of training data, and high computational requirements. In addition, vulnerability descriptions often do not follow the standard templates and are not rich enough with respect to the expected features. In this paper, we propose a novel architecture that takes advantage of both generative artificial intelligence and lightweight deep learning techniques to provide an efficient and effective solution for automated vulnerability scoring. Data extracted from the National Vulnerability Dataset is fed into a large language model layer, whose output (i.e., an augmented dataset) is then used in a lightweight fine-tuned BERTsmall layer. We provide the results of an extensive experimental assessment of the effect of both each layer of the architecture and end-to-end performances. The results suggest that the combination of GPT3.5-Turbo and BERTsmall provides the most effective accuracy-time trade-off. We also compare the performance of the proposed architecture with other LLMs, BERT models, and cutting-edge approaches. The results show good improvements in prediction quality also when compared to a recent technique that incorporates data from 66 different sources, including the NVD.

A data-driven predictive motion planner for mobile robots, referred to as LPM-Net, has been proposed in this paper. Conventional predictive motion planners are computationally expensive, often resulting in insufficient throughput on mobile robot hardware. LPM-Net is an imitation learning-assisted local predictive non-holonomic motion planner that is capable of learning from conventional motion planners regarded as paradigm models and replicating their behavior while satisfying the same kinodynamic constraints. In addition, LPM-Net is compatible with GPU and TPU hardware, allowing for faster and more efficient processing. LPM-Net uses convolutional and recurrent long short-term memory deep neural networks to predict steering commands. This has improved computational efficiency which allows autonomous vehicles to be equipped with more cost-effective computers. In the present study, LPM-Net was tuned to mimic the behavior of a model predictive controller paradigm model. Measurements in this study demonstrate that the proposed mimic planner, LPM-Net, consumes approximately half the processing power of the conventional predictive planner, albeit with a slight increase in hesitation when reaching goals.

Vulnerability assessment is an important and well-studied subject in software security. Traditional methods use expert knowledge, which is time-consuming. Considering the constantly increasing number of vulnerabilities, automated machine learning (ML)-based solutions have been proposed to assess the severity of vulnerabilities. Existing methods concentrate on predicting the Common Vulnerability Scoring System (CVSS) score or its vector metrics using available vulnerability information. The quality and diversity of the vulnerability description data can greatly affect the accuracy of these predictions. Studies report that less than 60% of such descriptions follow the formal template. On the other hand, the performance of ML-based vulnerability scoring approaches is highly dependent on the quality of the data and the model’s architecture. In this paper, we aim to improve the performance of existing ML-based solutions in vulnerability assessment. We use generative artificial intelligence (AI) and feed the CVSS descriptions to a large-language model. We use GPT3.5Turbo to generate descriptions and propose a fine-tuned BERT-CNN model to predict the CVSS vector metrics. We conduct several experiments to assess the performance of the proposed method against the state-of-the-art. We use both the original dataset (6,370 descriptions) and the descriptions generated by GPT3.5Turbo. Our experiments show that our proposed architecture considerably improves accuracy.

Early suicidal ideation detection has long been regarded as an important task that can benefit both society and individuals. In this regard, it has been shown that, very frequently, the first symptoms of this problem can be identified by analyzing the contents shared on social media. Machine learning classification models have proven promising in capturing behavioral and textual features from posts shared on social media. This study proposes a novel machine-learning model to detect the risk of suicide from social media posts, employing both natural language processing and state-of-the-art deep learning techniques. We propose an ensemble LSTM-TCN model that benefits from a self-attention mechanism to detect suicidal ideation among users of two well-known social networks, Twitter (X) and Reddit. Furthermore, we present a comprehensive analysis of the data, examining the suicidal posts both statistically and semantically, which can provide rich knowledge about suicidal ideation. Our proposed model (AL-BTCN) outperforms the compared state-of-the-art models, resulting in over 94% accuracy, recall, and F1-score. Researchers, mental health specialists, and social media service providers can all benefit from the findings of this study.

Abstract Nowadays, cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the network. Cloud computing services are available through common internet protocols and network standards. In addition to the unique benefits of cloud computing, insecure communication and attacks on cloud networks cannot be ignored. There are several techniques for dealing with network attacks. To this end, network anomaly detection systems are widely used as an effective countermeasure against network anomalies. The anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of anomalies. Network anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning methods. This paper presents an efficient model based on autoencoders for anomaly detection in cloud computing networks. The autoencoder learns a basic representation of the normal data and its reconstruction with minimum error. Therefore, the reconstruction error is used as an anomaly or classification metric. In addition, to detecting anomaly data from normal data, the classification of anomaly types has also been investigated. We have proposed a new approach by examining an autoencoder’s anomaly detection method based on data reconstruction error. Unlike the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value, we assume that the reconstruction error is a vector. This enables our model to use the reconstruction error of every input feature as an anomaly or classification metric. We further propose a multi-class classification structure to classify the anomalies. We use the CIDDS-001 dataset as a commonly accepted dataset in the literature. Our evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy, recall, false-positive rate, and F1-score metrics.

Summary New generation application problems in big data and high‐performance computing (HPC) areas claim very diverse operational properties. The convergence requires the dynamic behavior of system components. Load balancing is a critical issue in response to the highly unpredictable, dynamic, and data‐oriented behavior of the system. Possible practical constraints such as communication and load transfer delays play an essential role in designing a dynamic load balancer. On the other hand, according to most of the new platforms' distributed nature, the load balancer should be able to perform in a fully distributed manner. In this research, we consider practical issues, including different processing power, storage capability, communication, load transfer delays, and propose two distributed and optimized load balancing methods in HPC for Big Data processing. We model the constraints and present an argument named compensating factor for the optimized load balancer. We try to minimize the task execution time by reducing the nodes' idle time. We evaluate the proposed methods in different scenarios by using Monte Carlo. Evaluations results show that proposed methods decrease idle time significantly while being scalable to network size and applicable in heterogeneous networks with dynamic resources and configuration.

Scientific applications like weather forecasting require high performance and fast response time. But this ideal requirement has always been constrained by peculiarities of underlying platforms specially distributed platforms. One such constraint is the efficiency of communication between geographically dispersed and physically distributed processes running these applications, that is the efficiency of inter process communication (IPC) mechanisms. This paper provides hard evidence that an operating system kernel-level implementation of IPC on multi-computers reduces the execution time of a weather forecasting model by nearly half on average compared to when the IPC mechanism is implemented at library level. A well known non-hydrostatic version of the Penn state/NCAR mesoscale model, called MM5, is executed on a networked cluster. The performance of MM5 is measured with two distributed implementations of IPC, a kernel-level implementation called DIPC2006 and a renowned library level implementation called MPI. It is both shown how and argued why the performance of MM5 on a DIPC2006 configured cluster is by far better than its performance on an MPI configured similar cluster. Even ignoring the favorable points of kernel-level implementations, like safety, privilege, reliability, and primitiveness, the insight is twofold. Scientist may look for more efficient distributed implementations of IPC to run their simulations faster, and computer engineers may try harder to develop more efficient distributed implementations of IPC for scientists.

Parallel programming and distributed programming are two solutions for scientific applications to provide high performance and fast response time in parallel systems and distributed systems. Parallel and distributed systems must provide inter process communication (IPC) mechanisms like message passing mechanism as underlying platforms to enable communication between local and especially geographically dispersed and physically distributed processes. Communication overhead is the major problem in these systems and there are a lot of efforts to develop more efficient message passing mechanisms or to improve the network communication speed. This paper provides hard evidence that an efficient implementation of message passing mechanism on multi-computers reduces the execution time of a molecular dynamics code. A well-known program for macromolecular dynamics and mechanics called CHARMm is executed on a networked cluster. The performance of CHARMm is measured with two distributed implementations of message passing, namely a kernel-level implementation called DIPC2006 and a renowned library level implementation called MPI. It is shown that the performance of CHARMm on a DIPC2006 configured cluster is by far better than its performance on an optimized MPI configured similar cluster. Even ignoring the favorable points of kernel-level implementations, like safety, privilege, reliability, and primitiveness, the insight is twofold. Scientists are nowadays faced with more computational complexity and look for more efficient systems and mechanisms. Efficient distributed IPC mechanisms have direct effect on running scientistspsila simulations faster, and computer engineers may try harder to develop more efficient distributed implementations of IPC.