Chatbots have become increasingly popular in recent years, but they also present security risks and vulnerabilities that need to be addressed. This systematic literature review examines the existing research relating to information security in chatbots, identifying the potential threats, proposed solutions, and future directions for research. The review finds that chatbots face various security threats, including malicious input, user profiling, contextual attacks, and data breaches, and that solutions such as blockchain technology, end-to-end encryption, and organizational controls can be used to mitigate these concerns. The review also highlights the importance of maintaining user trust and addressing privacy concerns for the successful adoption and continued use of chatbots. A taxonomy developed in this review provides a useful framework for categorizing the articles and their findings. The review concludes by identifying future research directions that include developing more sophisticated authentication and authorization mechanisms, exploring the use of privacy-enhancing technologies, and improving the detection and prevention of security threats, among others. This review contributes to the growing body of literature on information security in chatbots and can guide future research and practice in this field.

ABSTRACT Background and Motivation: The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Methods: Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. Proposed Approach: This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. Results: The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. Conclusion: Our findings demonstrate the potential of the proposed methods for enhanced attack and intrusion detection. This improved detection capability can be instrumental in safeguarding system integrity, identifying fraudulent activity, and optimizing system performance in IoT networks.

The detection of zero-day attacks remains one of the most critical challenges in cybersecurity. This systematic literature review delves into the various methods employed for detecting zero-day attacks, identifying both the strengths and weaknesses of these approaches. By critically evaluating existing literature, this review provides new insights and highlights the gaps that future research must address. The findings suggest that while machine learning and artificial intelligence offer promising solutions, there are significant challenges related to data availability, algorithmic complexity, and real-time application. This review contributes to the field by providing a comprehensive analysis of current methods and proposing future research directions to enhance zero-day attack detection. The recent CrowdStrike incident, where the company successfully thwarted a sophisticated zero-day attack, underscores the importance and urgency of advancing our detection capabilities.

Cerebral palsy (CP) is a leading cause of motor dysfunction in children, significantly impacting gait and mobility. Accurate and early diagnosis of gait abnormalities in pediatric CP patients is crucial for effective intervention and management. However, making an early-stage CP diagnosis based only on a single vision modality such as an MRI has many difficulties. Because of the baby’s obstinate movements, the possibility of early recovery, the lack of a single vision modality, and the noisy or absent brain magnetic resonance imaging (MRI) slices, the task is getting harder and harder. This study employed a robust framework that leverages data from multiple sensor modalities, including wearable inertial measurement units (IMUs), pressure-sensitive mats, and motion capture systems integrated with MRI to generate multimodal data. This multimodal data was then processed using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dynamics of gait patterns. In the experimentation, we achieved remarkable results with an accuracy of 95.33%, an AUC of 96.2%, an F1 score of 95.28%, and a misclassification rate of 0.0467. Also, the comparative analysis with state-of-the-art demonstrates that the proposed approach significantly outperforms traditional methods in identifying subtle gait abnormalities, providing a more detailed and accurate assessment of gait deviations in pediatric cerebral palsy patients.