115 research outputs found

    Fast Fourier Transform-based steganalysis of covert communications over streaming media

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    Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a new steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. Results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Physical Layer Jamming detection: a Machine Learning Approach

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    openThis paper aims to illustrate the laboratory experience carried out during March-July 2023 at Hochschule Darmstadt having as its goal the writing of a master’s thesis. The initial goal of the project was to use machine learning techniques to analyze the physical characteristics (i.e:ISO/OSI layer 1) of a wireless cellular channel in order to detect the presence of an attacker. Thus, the expected outcome of the project is to construct a binary classifier, which takes in input information from the wireless channel and outputs the state of the channel through a binary classification: that is, whether the channel is in a state recognized as normal or whether it has been corrupted by the presence of an attacker. Lab experiences were carried out using software to implement SDR, both user-side and attacker- side. Therefore, the methodologies used to conduct these experiments will be explained, speci- fying the theoretical background and commenting from a technical point of view on the results obtained.This paper aims to illustrate the laboratory experience carried out during March-July 2023 at Hochschule Darmstadt having as its goal the writing of a master’s thesis. The initial goal of the project was to use machine learning techniques to analyze the physical characteristics (i.e:ISO/OSI layer 1) of a wireless cellular channel in order to detect the presence of an attacker. Thus, the expected outcome of the project is to construct a binary classifier, which takes in input information from the wireless channel and outputs the state of the channel through a binary classification: that is, whether the channel is in a state recognized as normal or whether it has been corrupted by the presence of an attacker. Lab experiences were carried out using software to implement SDR, both user-side and attacker- side. Therefore, the methodologies used to conduct these experiments will be explained, speci- fying the theoretical background and commenting from a technical point of view on the results obtained

    Freaky Leaky SMS: Extracting User Locations by Analyzing SMS Timings

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    Short Message Service (SMS) remains one of the most popular communication channels since its introduction in 2G cellular networks. In this paper, we demonstrate that merely receiving silent SMS messages regularly opens a stealthy side-channel that allows other regular network users to infer the whereabouts of the SMS recipient. The core idea is that receiving an SMS inevitably generates Delivery Reports whose reception bestows a timing attack vector at the sender. We conducted experiments across various countries, operators, and devices to show that an attacker can deduce the location of an SMS recipient by analyzing timing measurements from typical receiver locations. Our results show that, after training an ML model, the SMS sender can accurately determine multiple locations of the recipient. For example, our model achieves up to 96% accuracy for locations across different countries, and 86% for two locations within Belgium. Due to the way cellular networks are designed, it is difficult to prevent Delivery Reports from being returned to the originator making it challenging to thwart this covert attack without making fundamental changes to the network architecture

    A Case Study in Physical-Layer Steganography Applied to Multicarrier Transmissions

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    Covert communications can be a force for good, such as providing a means of message authentication to prevent malicious actors from spoofing networks. This dissertation explores the design of a covert signal to be hidden inside the bandwidth of an Orthogonal Frequency Division Multiplexing (OFDM) signal. In order to make detection by unintended observers as difficult as possible, the covert signal operates as interference inside the OFDM signal and is set to a high Signal to Interference Ratio (SIR). Given the high SIR, the OFDM signal must be cancelled in order to recover the covert signal. The detectability of the covert signal is tested using multiple detectors with and without cancellation. Among the detectors used is a Convolutional Neural Network (CNN) designed for image classification that has been repurposed through transfer learning to detect signal activity in noise and interference. The CNN detector demonstrates resilience in the presence of narrowband interference. The cancellation algorithm is enhanced with an estimate of OFDM windowing as applied at the transmitter, which is an often-overlooked parameter in cancellation applications. The enhanced cancellation-algorithm improves the cancellation of OFDM signals by 5.3 dB in an over-the-air test. The enhanced cancellation-algorithm also improves the Packet Error Rate of OFDM signals and improves the recovery of the covert signal. The improved recovery has direct application to Power-Domain Non-orthogonal Multiple Access and Rate-Splitting Multiple Access, which both rely on successive interference cancellation. Lastly, to frustrate any efforts to analyze the covert waveform, the covert signal is augmented with an adversarial waveform designed to exploit weaknesses in CNNs used for modulation classification. The classification system suffers from uncertainty in the bandwidth estimate of the covert signal. The system will likely err on the side of making the bandwidth wider than necessary. It is demonstrated that a wider bandwidth makes the attack more successful, as opposed to other estimation errors which prior literature has shown to weaken the effectiveness of these attacks
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