Vanga Odelu

With the rapid growth of Internet of Vehicles (IoV) technology, the attack surface has expanded significantly in recent years. Traditional intrusion detection systems, such as signature-based and rule-based approaches, are inadequate for IoV environments due to their limitations in detecting zero-day attacks. To address this challenge, machine learning techniques have been widely explored in the literature for zero-day attack detection. However, the highly imbalanced nature of IoV datasets often leads to biased performance, favoring majority attack classes and reducing detection accuracy for minority classes. This paper proposes a Zero-Day Intrusion Detection System (Z-IDS) built on a hybrid LSTM–BiLSTM architecture. To mitigate class imbalance, a Generative Adversarial Network (GAN) is employed to generate synthetic samples. The proposed Z-IDS is evaluated using a cleaned version of the CICIoV2024 dataset under four experimental setups: (A) baseline testing; (B) training with GAN-augmented data; (C) leave-one-class-out testing for unseen attack detection (i.e., zero-day attacks); and (D) cross-dataset validation using the Car-Hacking (2020) dataset. The model achieves an accuracy of 99.72% with GAN-augmented data and 99.99% in zero-day attack scenarios. These results demonstrate the effectiveness of the proposed LSTM–BiLSTM-based Z-IDS framework in detecting both known and previously unseen attacks within IoV environments.

Internet of Things (IoT) have gained popularity in recent times. With an increase in the number of IoT devices, security and privacy vulnerabilities are also increasing. For sensitive domains like healthcare and industrial sectors, such vulnerabilities can cause havoc. Thus, authentication is an important aspect for establishing a secure communication between various participants. In this paper, we study the two recent authentication and key exchange protocols. We prove that these protocols are vulnerable to replay attack and modification attack, and also suffer from technical correctness. We then present the possible improvements to overcome the discussed vulnerabilities. The enhancement preserves performance of the original protocols.

In 2022, Neela and Kavitha proposed an improved RSA encryption algorithm (IREA) for cloud environment. In this paper, we review and comment on the correctness of the IREA technique. We prove that the private key generation in the proposed IREA is mathematical incorrect. That is, decryption of the cipher generated by the public key is not possible with the private key. Further, we discuss the possible modifications in IREA to make it correct decryption.

With rapid technological growth, security attacks are drastically increasing. In many crucial Internet-of-Things (IoT) applications such as healthcare and defense, the early detection of security attacks plays a significant role in protecting huge resources. An intrusion detection system is used to address this problem. The signature-based approaches fail to detect zero-day attacks. So anomaly-based detection particularly AI tools, are becoming popular. In addition, the imbalanced dataset leads to biased results. In Machine Learning (ML) models, F1 score is an important metric to measure the accuracy of class-level correct predictions. The model may fail to detect the target samples if the F1 is considerably low. It will lead to unrecoverable consequences in sensitive applications such as healthcare and defense. So, any improvement in the F1 score has significant impact on the resource protection. In this paper, we present a framework for ML-based intrusion detection system for an imbalanced dataset. In this study, the most recent dataset, namely CICIoT2023 is considered. The random forest (RF) algorithm is used in the proposed framework. The proposed approach improves 3.72%, 3.75% and 4.69% in precision, recall and F1 score, respectively, with the existing method. Additionally, for unsaturated classes (i.e., classes with F1 score < 0.99), F1 score improved significantly by 7.9%. As a result, the proposed approach is more suitable for IoT security applications for efficient detection of intrusion and is useful in further studies.