AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Investigating Passive Operating System Detection

Today computer and network security are a big part of a system administrators life. New methods and applications appear and several makes the system administrator’s job easier. Gathering information is a big part of detecting threats and staying one step ahead of the black hats. This thesis looks at and investigates a specific area in network security, namely passive operating system detection. Information is important in network security and knowing your enemies are important in securing your network. Passive operating system detection helps collecting information passively, which can be used to the administrators advantage. The thesis looks at passive operating system detection applications and looks especially on the applications p0f and prads. By running both applications in a larger network and testing them in a controlled environment, the weaknesses of both applications are revealed and improvements suggested and tried implemented. Improvements discussed and tried implemented in this thesis, are adding new signatures and creating Perl scripts that improves the applications itself. The scripts deals with the output from the applications which tends to be overwhelming and needs new presentation methods

An approach to the identification of network elements composing heterogeneous end-to-end paths

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A computational intelligence enabled honeypot for chasing ghosts in the wires

A honeypot is a concealed security system that functions as a decoy to entice cyberattackers to reveal their information. Therefore, it is essential to disguise its identity to ensure its successful operation. Nonetheless, cyberattackers frequently attempt to uncover these honeypots; one of the most effective techniques for revealing their identity is a fingerprinting attack. Once identified, a honeypot can be exploited as a zombie by an attacker to attack others. Several effective techniques are available to prevent a fingerprinting attack, however, that would be contrary to the purpose of a honeypot, which is designed to interact with attackers to attempt to discover information relating to them. A technique to discover any attempted fingerprinting attack is highly desirable, for honeypots, while interacting with cyberattackers. Unfortunately, no specific method is available to detect and predict an attempted fingerprinting attack in real-time due to the difficulty of isolating it from other attacks. This paper presents a computational intelligence enabled honeypot that is capable of discovering and predicting an attempted fingerprinting attack by using a Principal components analysis and Fuzzy inference system. This proposed system is successfully tested against the five popular fingerprinting tools Nmap, Xprobe2, NetScanTools Pro, SinFP3 and Nessus

SoK: Acoustic Side Channels

We provide a state-of-the-art analysis of acoustic side channels, cover all the significant academic research in the area, discuss their security implications and countermeasures, and identify areas for future research. We also make an attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.Comment: 16 page