The Internet of Things (IoT) devices have brought invaluable convenience to our daily lives. However, they also introduce significant security challenges. Common vulnerabilities in numerous IoT devices predominantly reside in their web services. Unfortunately, existing vulnerability detection methods either incur heavy execution overhead or produce excessive false positives/negatives. This significantly hinders the efficient analysis of vulnerabilities in web services. This paper proposes CID4IoT, a novel static automated vulnerability detection approach designed to effectively detect command injection vulnerabilities in web services provided by IoT devices. Inspired by the concept of taint analysis, CID4IoT first analyzes vulnerability reports to identify taint source functions and sink functions. Then, CID4IoT extracts pseudocode between taint source functions and sink functions to form critical code snippets. Subsequently, it utilizes LLM to analyze these critical code snippets for vulnerability detection.We implemented a prototype of CID4IoT and evaluated it on real firmware devices. CID4IoT discovered 54 previously unknown vulnerabilities, of which 39 are confirmed by CVE. Compared with state-of-the-art tools KARONTE and SaTC, it identified significantly more vulnerabilities in the test set while achieving notable improvements in analysis efficiency. The results demonstrate that CID4IoT is effective in detecting flaws in IoT devices

Source counting (SC) in an indoor environment is an important problem in computational auditory scene analysis. However, the problem is challenging, especially when reverberation and ambient noise are present in the environment. To address this problem, we propose an augmented Bayesian non-parametric (ABNP) clustering algorithm for source counting based on sound intensity (SI) captured by a small aperture microphone array. The core idea is to incorporate an infinite Gaussian mixture model (IGMM) and a time-frequency (TF) augmented weight selection and update scheme for sound intensity estimation. The use of IGMM enables the exemption of the maximum number of sources assumed in previous methods. Experiments on both simulated and real-world data show the improved performance by the proposed method as compared with the state of the art baseline methods.

The technique of speech steganography involves embedding messages within openly transmitted speech channels without arousing suspicion. Nevertheless, current methods for embedding speech in speech suffer from weak imperceptibility and low message speech intelligibility. In this paper, we introduce a novel approach that explores cross-domain redundancy by leveraging a deep encoder–decoder neural network architecture to embed Mel-spectrograms into magnitude spectrograms. Specifically, the message is transformed into its Mel-spectrogram, while the cover is transformed into its magnitude spectrogram. Subsequently, the Mel-spectrogram is embedded as residuals in the magnitude spectrogram through an encoder known as the spectrogram super-resolution network (SSRN). Upon receiving the stego, a decoder network recoveres the Mel-spectrograms of the messages, and a high-fidelity HiFi-GAN vocoder then recovers the message waveform. The encoder–decoder network’s parameters are optimized to ensure imperceptibility and high quality. To validate the superiority of our proposed method, we compare it with recently proposed baselines using common databases such as the LJ Speech and VCTK datasets. Experimental results demonstrate that our method achieves SNRs of 33.83 dB and 30.28 dB for the cover signals on these two datasets, respectively. Furthermore, both the content and speaker identity of the recovered messages are well preserved, and the experiments also confirm the robustness against noises and the security of our approach.

Acoustic signal processing in the circular harmonic domain (CHD) is an appealing method for speaker localization, since it inherently supports wideband acoustic sources and provides frequency invariant beampatterns. However, the performance of existing circular harmonic direction-of-arrival (DOA) estimation approaches can be degraded by a variety of factors, including background noise and reverberation in the acoustic environments, small aperture size of the circular array and the presence of multiple active sources. This paper addresses these issues by proposing a novel multi-speaker CHD localization method with small-sized microphone arrays using deep convolutional neural networks (CNN). The core idea is to construct circular harmonic features through joining the selected time-frequency (TF) bins of higher power and the operation of a randomization process by mimicking the sparsity property of speech signals. After that, we implement multi-speaker estimation as a multi-label classification task, and propose to use CNN with binary cross-entropy as the loss function. Experimental results show that our method performs significantly better than the baseline methods, on both simulated and real data, in terms of the accuracy of DOA estimation.

The problem of direction of arrival (DOA) estima- tion with a circular microphone array has been addressed with classical source localization methods, such as the model-based methods and the parametric methods. These methods have an ad- vantage in estimating the DOAs in a blind manner, i.e. with no (or limited) prior knowledge about the sound sources. However, their performance tends to degrade rapidly in noisy and reverberant environments or in the presence of sensor array limitations, such as sensor gain and phase errors. In this paper, we present a new approach by leveraging the strength of a convolutional neural network (CNN)-based deep learning approach. In particular, we design new circular harmonic features that are frequency- invariant as inputs to the CNN architecture, so as to offer improvements in DOA estimation in unseen adverse environments and obtain good adaptation to array imperfections. To our knowledge, such a deep learning approach has not been used in the circular harmonic domain. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than the recent baseline methods, in a variety of noise and reverberation levels, in terms of the accuracy of the DOA estimation.

Circular microphone arrays have been used for multi-speaker localization in computational auditory scene analysis, for their high flexibility in sound field analysis, including the generation of frequency-invariant eigenbeams for wideband acoustic sources. However, the localization performance of existing circular harmonic approaches, such as circular harmonics beamformer (CHB) depends strongly on the physical characteristics (such as shape) of sensor arrays, and the level of uncertainties presented in acoustic environments (such as background noise, room reverberation, and the number of sources). These uncertainties may limit the performance or practical application of the speaker localization algorithms. To address these issues, in this paper, we present a new indoor multi-speaker localization method in the circular harmonic domain based on the acoustic holography beamforming (AHB) technique and the Bayesian nonparametrics (BNP) method. More specifically, we use the AHB technique, which combines the delay-and-sum beamforming with acoustic-holography-based virtual sensing, to generate direction of arrival (DOA) measurements in the time-frequency (TF) domain, and then design a BNP algorithm based on the infinite Gaussian mixture model (IGMM) to estimate the DOAs of the individual sources without the prior knowledge about the number of sources. These estimates may degrade in the presence of room reverberation and background noise. To address this issue, we develop a robust TF bin selection and permutation method on the basis of mixture weights, using power, power ratio and local variance estimated at each TF bin. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than four recent baseline methods, in a variety of noise and reverberation levels, in terms of the root-mean-square error (RMSE) of the DOA estimation and the source detecting success rate.