A multi-environment (M-En) network, integrating various network architectures, faces significant challenges in detecting malicious traffic due to diverse protocols and traffic patterns. Developing separate security frameworks for each network type increases management overhead, limits scalability, and raises costs. Another issue in this domain is the limited deployment-oriented validation of malicious traffic detection systems (MTDS), which restricts their effectiveness in protecting M-En networks under live conditions. To address these challenges, this study proposes a unified simulation-based, deployment-feasible, transfer learning-based MTDS for M-En networks, with a focus on both IoT and traditional IP-based infrastructures. In our client-server simulation setup on Ubuntu, the client captures traffic at the central gateway and forwards it to the server for analysis, where flagged malicious packets are discarded at the gateway following batch-level analysis. A representative M-En feasibility dataset is generated using partial least squares (PLS) canonical analysis by merging two benchmark datasets, IoT23 (IoT malware traffic) and CICDDoS2019 (traditional IP-based DDoS traffic). An RNN-LSTM-based transfer learning model is employed for feature extraction from this M-En dataset, and various machine learning algorithms are trained and evaluated in both offline and deployment-oriented simulation settings. Logistic Regression emerges as the best-performing model, achieving a mean accuracy of 0.98 in offline testing and 0.71 during simulation-based gateway evaluation, along with the lowest computational time (averaging 0.521 seconds offline and 0.59 seconds per batch during simulated deployment) and minimal memory and CPU usage.

Anomaly detection is crucial for ensuring the security and reliability of next-generation heterogeneous networks, which integrate a variety of devices (e.g., IoT and traditional devices). Autoencoder (AE)-based approaches have shown promise in identifying network anomalies by modeling benign traffic and detecting deviations. However, existing AE methods, mostly evaluated in homogeneous environments, struggle to generalize in next-generation multi-environment (M-En) networks (comprising both IoT and traditional devices) due to diverse traffic patterns. This generalization issue is evidenced through an initial ablation study, where AE models trained solely on a single traffic type (e.g., IoT-only) fail to detect anomalies effectively in M-En networks. To address this limitation, a Residual Autoencoder (RD-AE) specifically designed for anomaly detection in next-generation M-En networks is presented. RD-AE is trained on a combined actual benign dataset containing both IoT and traditional traffic, allowing it to accurately identify abnormal (malicious) deviations in M-En networks. An attention-based BiLSTM classifier subsequently categorizes these detected anomalies, differentiating between malicious IoT and traditional traffic to support targeted defensive measures. A human-in-the-loop continuous learning mechanism is integrated into the classifier, ensuring adaptability to emerging threats. A realistic testbed is used to generate M-En traffic and evaluate the proposed framework in real time. Experimental results show that RD-AE achieves up to 97.4% accuracy in detecting previously unseen attack scenarios.

Cybersecurity concerns are rising with the rapid adoption of technology as cybercriminals grow more active. Protecting complex networks like Software-Defined Networking (SDN) is increasingly challenging because its centralized architecture introduces vulnerabilities that traditional security systems struggle to handle. This paper investigates the application of large language models (LLMs) for intrusion detection in SDN environments. Our proposed approach fine-tunes three LLMs, GPT_NEO, Phi-2, and Llama2-7b, through Quantized Low-Rank Adaptation (QLoRA), enabling efficient 4-bit quantization and reduced memory usage. Structured network features are transformed into natural language prompts for binary classification of benign and malicious traffic. Experimental results show that all models achieve high accuracy, with Llama2-7b and Phi-2 reaching high scores across multiple data scales. CodeCarbon tracking highlights the environmental trade-offs, with Llama27b consuming the most energy and Phi-2 being the most efficient. Our proposed framework for Phi-2 and GPT_NEO achieves a 1.00 accuracy score with the lowest$\text{CO}_{2}$emission of 0.173 when compared with the baselines. Further, we explore explainability challenges for our LLM models, noting the limitations of token-level interpretability tools in handling dense textual embeddings.

The role of artificial intelligence (AI) in cybersecu-rity has grown due to increasing threats from malicious actors. It aids in threat detection, behavioral analysis, malware detection, phishing identification, and enhancing security measures. However, AI can also be weaponized for cyberattacks, as malicious actors use AI-based tools for sophisticated and adaptable assaults on security systems. This study contributes to cybersecurity by defending against AI-based threats. Machine learning models were trained on diverse, complex datasets to counter sophisticated AI-based attacks in multi-environments (M-En). We have utilized auto-encoders to generate our M-En dataset by combining two benchmark datasets: UNSW-NB15 and IoTID-20, that represent traditional IP-based and IoT-based traffic, respectively. Three generative models (CTGAN, CopulaGAN, and TVAE) produced AI-based traffic, leading to a dataset comprising traditional and AI-generated traffic. Machine learning and deep learning models were deployed on this M-En dataset. The ensemble Extra Trees classifier achieved the highest accuracy score of 0.983 for binary classification and 0.968 for multiclass problems. Our proposed approach demonstrates its effectiveness in countering AI-based traffic as well as traditional network traffic within the M-En networks.

Cybersecurity poses a growing threat to technology infrastructure, especially raising concerns for automobile technology. Modern vehicles, reliant on connectivity, face a critical challenge in safeguarding their in-vehicle networks from cyber-attacks. Although the Controller Area Network is a standard for in-vehicle networks, its lack of security features exposes vehicles to vulnerabilities. Especially, the use of AI for offensive purposes further intensifies the threat to automotive technology infrastructure to protect it from cyber-attacks. This study proposes an approach to enhance in-vehicle network security, employing machine learning algorithms to protect against both AI-based generated attacks and traditional attacks. The Conditional Tabular Generative Adversarial Network (CTGAN) is utilized to generate in-vehicle network traffic, which is then combined with benchmark in-vehicle network traffic to create a complex and diverse scenario. The Random Forest model achieved a significant accuracy score for both benchmark in-vehicle network traffic and AI-based traffic, with scores of 0.93 and 0.89, respectively.

Multi-environment networks, such as those in smart homes, handle both IoT and traditional IP-based traffic. Weak security protocols in IoT devices and the diverse traffic flow make these networks vulnerable to security breaches. This study delves into this pressing challenge and presents a pioneering solution—FAMTDS (Fully Automated Malicious Traffic Detection System)—designed explicitly for multi-environment networks. FAMTDS addresses the critical need for robust security measures by intelligently analyzing the amalgamation of IoT and IP-based traffic. FAMTDS comprises three pivotal stages: innovative multi-environment dataset creation, optimization of machine learning model hyperparameters, and holistic system optimization. For the multi-environment dataset, we amalgamate two prominent open-source datasets, UNSW-NB-15 and IoTID-20. Initially, crucial features are extracted from both datasets using an extra trees classifier. Subsequently, an equal number of features is generated by employing a neural network to harmonize these datasets. The resulting multi-environment dataset encompasses 19 distinct attack types, a comprehensive inclusion unprecedented in prior research on malicious traffic detection. This dataset exhibits diversity owing to its varied traffic samples, addressing a crucial gap in existing studies. To accommodate this diversity, machine learning models are deployed with fine-tuned hyperparameters. The Mouth Flame Optimizer streamlines feature extraction, feature generation, and hyperparameter tuning, automating the optimization process. FAMTDS demonstrates exceptional performance, achieving an accuracy score of 0.85 for the multi-environment dataset. We also integrated the CICDDOS2019 dataset with IoTID-20 in our multi-environment dataset, achieving a notable accuracy of 0.82 against recent attacks, thus enhancing our approach’s validation. To further validate the generalizability of our proposed approach, we applied it to zero-day attack prediction. Our method demonstrated an accuracy of 0.84 for zero-day attacks, indicating its effectiveness in detecting newly emerging threats in multi-environment networks.

The rapid advancement of network architectures, protocols, and tools poses significant challenges to network security, especially due to the use of AI-based tools by cybercriminals. It is crucial to develop a versatile malicious traffic detection system capable of identifying attacks across diverse traffic types. This paper presents an enhanced system for detecting malicious traffic in multi-environment (M-En) networks, including IoT, SDN, and traditional IP-based traffic. Existing techniques often under-utilize diversity in network traffic, limiting their effectiveness. To address this, we propose a comprehensive approach that combines the power of Synthetic Data Augmentation TEchnique (S-DATE) and Particle Swarm Optimizer (PSO)-based Diverse-Self Ensemble Model (D-SEM). Our approach proposes the M-En dataset, a combination of three different network architectures datasets including InSDN, UNSWNB-15, and IoTID-20 that accurately represent real-world scenarios. S-DATE is then employed to address imbalanced data distribution in novel generated M-En dataset, enabling better model convergence and enhancing the detection rate of normal and abnormal traffic. Additionally, we introduce PSO-D-SEM, a novel ensemble model that leverages the diversity provided by PSO to handle the complexity of M-En networks. The PSO-D-SEM combines individual models trained on a subset of the M-En dataset, resulting in improved overall performance. The experimental results demonstrate the superiority of our enhanced system, achieving a significant accuracy score of 0.989. Further, we also deploy a statistical T-test to demonstrate the significance of the proposed PSO-D-SEM approach in comparison with state-of-the-art methods.

With the large use of social media, the dissemination of intentionally altered and falsified information has become easy, thus posing negative effects on society. Detecting fake content is a non-trivial task as fake news has unique characteristics and challenges. Additionally, the wide use of artificial intelligence (AI) for fake content generation makes the detection of fake content further complicated. Fake news presents engineered content, making it difficult for traditional approaches to comprehend. Existing fake news detection approaches face four problems: lack of robustness, adaptability, limited or no use of auxiliary information, and inability to handle diversity. Fake content diversity introduces the models’ complexities and degrades their performance. Similarly, the accuracy of fake news detection approaches remains low for practical systems. This study focuses on detecting fake news by using an AI-based approach to obtain high accuracy and robustness by using the concept of text transformation into images. It transforms the text into a standard image format which enriches the feature space and boosts the performance of machine learning models. Extensive experiments using two different datasets involving binary and multi-class classification reveal that the proposed approach outperforms existing solutions by yielding superior accuracy. The use of AI approaches helps obtain higher accuracy of 99.70% and 92% for fake news detection using ISOT and LIAR datasets, respectively.

The emergence of new network architectures, protocols, and tools has made it easier for cybercriminals to launch attacks using AI-based tools, presenting challenges in network security. To protect such systems, a versatile malicious traffic detection system is required that can identify attacks regardless of the type of traffic coming toward the network. In this paper, a system is proposed that can singly analyze multi-environment traffic (IoT and traditional IP-based) to detect malicious activity. The existing techniques for managing Multi-Environment traffic are inefficient due to the absence of AI utilization. To overcome these issues, the proposed approach generates a novel multienvironment traffic dataset by merging existing network datasets containing both traditional IP-based traffic and IoT network traffic. Synthetic Data Augmentation TEchnique (S-DATE) is also proposed to overcome the problem of imbalanced data distribution in the new multi-environment dataset. The results show that the utilization of S-DATE results in faster machine learning model convergence and an improvement in the detection rate of normal and abnormal traffic. The proposed approach achieves an impressive overall detection rate of 0.991 and is statistically significant compared to other state-of-the-art approaches.

ChatGPT OpenAI, a large-language chatbot model, has gained a lot of attention due to its popularity and impressive performance in many natural language processing tasks. ChatGPT produces superior answers to a wide range of real-world human questions and generates human-like text. The new OpenAI ChatGPT technology may have some strengths and weaknesses at this early stage. Users have reported early opinions about the ChatGPT features, and their feedback is essential to recognize and fix its shortcomings and issues. This study uses the ChatGPT tweets Arabic dataset to automatically find user opinions and sentiments about ChatGPT technology. The dataset is preprocessed and labeled using the TextBlob Arabic Python library into positive, negative, and neutral tweets. Despite extensive works for the English language, languages like Arabic are less studied regarding tweet analysis. Existing literature about Arabic tweet sentiment analysis has mainly focused on machine learning and deep learning models. We collected a total of 27,780 unstructured tweets from Twitter using the Tweepy SNscrape Python library using various hash-tags such as # Chat-GPT, #OpenAI, #Chatbot, Chat-GPT3, and so on. To enhance the model’s performance and reduce computational complexity, unstructured tweets are converted into structured and normalized forms. Tweets contain missing values, URL and HTML tags, stop words, punctuation, diacritics, elongations, and numeric values that have no impact on the model performance; hence, these increase the computational cost. So, these steps are removed with the help of Python preprocessing libraries to enhance text quality and consistency. This study adopts Transformer-based models such as RoBERTa, XLNet, and DistilBERT that automatically classify the tweets. Additionally, a hybrid transformer-based model is proposed to obtain better results. The proposed hybrid model is developed by combining the hidden outputs of the RoBERTA and BERT models using a concatenation layer, then adding dense layers with “Relu” activation employed as a hidden layer to create non-linearity and a “softmax” activation function for multiclass classification. They differ from existing state-of-the-art models due to the enhanced capabilities of both models in text classification. Hybrid models combine the different models to make accurate predictions and reduce bias and enhanced the overall results, while state-of-the-art models are incapable of making accurate predictions. Experiments show that the proposed hybrid model achieves 96.02% accuracy, 100% precision on negative tweets, and 99% recall for neutral tweets. The performance of the proposed model is far better than existing state-of-the-art models.

Abstract With the multitude of companies that flourish today, job seekers want to join companies with highly satisfied employees. So, job satisfaction prediction is an important task that helps companies in sustaining or redesigning employee policies. Such predictions not only help in reducing employee attrition but also affect the goodwill and reputation of a company. The higher satisfaction level of current employees attracts potential new employees and confirms the positive policies of a company toward its employees. Job satisfaction prediction can be performed using employee reviews either manually or via automated machine learning algorithms. This study first evaluates four widely used machine learning algorithms, that is, random forest, logistic regression, support vector classifier, and gradient boosting, and then proposes a deep learning model to predict employee job satisfaction level. Experiments are carried out on a dataset that contains text reviews from the employees of Google, Facebook, Amazon, Microsoft, and Apple. Three feature extraction methods are analyzed as well including term frequency‐inverse document frequency (TF‐IDF), bag‐of‐words (BOW), and global vector for word representation (GloVe). Performance is evaluated using accuracy, precision, recall, F1 score, as well as, macro average precision, and weighted average. The performance of the proposed model is compared with state‐of‐the‐art deep learning models. Results demonstrate that the proposed model performs better than both the machine learning and state‐of‐the‐art approaches.