Journal of Computer Security - Volume 32, issue 6

Authors: Li, Yiming | Liu, Shengli

Article Type: Research Article

Abstract: Identity-based chameleon hash (IBCH) is a cryptographic primitive with nice properties. IBCH equips each user with a trapdoor and the hash values can be publicly evaluated w.r.t. the identity of any user. On the one hand, it is hard to find collisions for the hash values without the user’s trapdoor. On the other hand, with the help of the user’s trapdoor, finding collisions becomes easy. An important application of IBCH is to upgrade an identity-based signature (IBS) scheme to an on-line/off-line identity-based signature (OO-IBS) scheme. OO-IBS is a useful tool to provide authenticity in lightweight smart devices, since it only …involves light on-line computations and does not need key certificate. Up to now, there are many IBCH constructions from traditional number-theoretic assumptions like RSA, CDH, etc. However, none of the existing IBCH schemes achieve the post-quantum security in the standard model. In this paper, we propose a new IBCH scheme from lattices. The security of our IBCH is reduced to a well-accepted lattice-based assumption – the Short Integer Solution (SIS) assumption in the standard model. Our work provides the first post-quantum solution to IBCH in the standard model. Show more

Keywords: Identity-based chameleon hash, lattice-based cryptography, post-quantum cryptograph, standard model

DOI: 10.3233/JCS-220121

Citation: Journal of Computer Security, vol. 32, no. 6, pp. 509-531, 2024

Authors: Cui, Susu | Han, Xueying | Dong, Cong | Li, Yun | Liu, Song | Lu, Zhigang | Liu, Yuling

Article Type: Research Article

Abstract: Detecting encrypted malware traffic promptly to halt the further propagation of an attack is critical. Currently, machine learning becomes a key technique for extracting encrypted malware traffic patterns. However, due to the dynamic nature of network environments and the frequent updates of malware, current methods face the challenges of detecting unknown malware traffic in open-world environment. To address the issue, we introduce MVDet, a novel method that employs machine learning to mine the behavioral features of malware traffic based on multi-view analysis. Unlike traditional methods, MVDet innovatively characterizes the behavioral features of malware traffic at 4-tuple flows from four views: …statistical view, DNS view, TLS view, and business view, which is a more stable feature representation capable of handling complex network environments and malware updates. Additionally, we achieve a short-time behavioral features construction, significantly reducing the time cost for feature extraction and malware detection. As a result, we can detect malware behavior at an early stage promptly. Our evaluation demonstrates that MVDet can detect a wide variety of known malware traffic and exhibits efficient and robust detection in both open-world and unknown malware scenarios. MVDet outperforms state-of-the-art methods in closed-world known malware detection, open-world known malware detection, and open-world unknown malware detection. Show more

Keywords: Encrypted traffic, malware traffic detection, multi-view features, machine learning

DOI: 10.3233/JCS-230024

Citation: Journal of Computer Security, vol. 32, no. 6, pp. 533-555, 2024

Authors: Kiran, Garikamukkala Vijaya | Krishnan, Vidhya

Article Type: Research Article

Abstract: A massive amount of data is transmitted in the Internet of Things (IoT). Nowadays, the concerning of security issues are the major factor while transferring data through wireless networks. Since, data privacy becomes complicated. In this research work, a newly proposed model for multimedia steganography is developed. Initially, the required video is obtained from the publically available datasets, and then the acquired input is subjected to the Adaptive Discrete Cosine Transformation (DCT) based block process. The optimal blocks are chosen by the Adaptive Multi-cascaded ResNet (AMC-ResNet) model for applying stego data. Here, the parameter optimization takes place in the DCT …and ResNet model to enhance the steganography performance via the Mouth Brooding Fish Emperor Penguin Optimization (MBFEPO) derived from the Mouth Brooding Fish Algorithm (MBFA) and Emperor Penguin Optimization Algorithm (EPOA). Finally, the inverse DCT is employed at the blocks to get the final stego video. In the audio steganography phase, the wanted audio is gathered from external websites. The collected data are given to the Short-time Fourier Transform (STFT) to convert into the spectrogram image, and then the spectrogram image is given to the Adaptive DCT block, selecting the block to apply stego data. Thus, the blocks are selected with the utilization of the Adaptive Multi-cascaded ResNet (AMC-ResNet), where the parameters within the DCT and the ResNet are optimized via the same MBFEPO to improve the performance. After, the Inverse ADCT is applied to reconstruct the spectrogram image. Then, the resultant stego audio is obtained by using the Inverse STFT. Finally, several experiments are conducted to estimate the working ability of the proposed steganography model. The outcome of the recommended model shows 12.3%, 52.6%, 12.3%, and 84.3% better performance SFO, HBA, MBFA, and EPOA in terms of median. The recommended model performs superior performance rather than the existing approaches. Show more

Keywords: Multimedia steganography, Adaptive Discrete Cosine Transformation Adaptive Multi-cascaded ResNet, Mouth Brooding Fish Emperor Penguin Optimization, Short-Time Fourier transform

DOI: 10.3233/JCS-230049

Citation: Journal of Computer Security, vol. 32, no. 6, pp. 557-588, 2024