Muhammad Salman Khan

Learn More
Keywords: Health care Fall detection Unsupervised classification Source separation Mel-frequency cepstral coefficient One class support vector machine a b s t r a c t We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person's normal activities to construct a data(More)
The WWW is currently experiencing a revolutionary growth due to numerous emerging tools, techniques and concepts. Digital journals thus need to transform themselves to cope with this evolution of the web. With their growing information size and access, conventional techniques for managing a journal and supporting authors and readers are becoming(More)
In this work we propose a new binaural spectral subtraction method for the suppression of late reverberation. The proposed approach is a cascade of three stages. The first two stages exploit distinct observations to model and suppress the late reverberation by deriving a gain function. The musical noise artifacts generated due to the processing at each(More)
Source separation algorithms that utilize only audio data can perform poorly if multiple sources or reverberation are present. In this paper we therefore propose a video-aided model-based source separation algorithm for a two-channel reverberant recording in which the sources are assumed static. By exploiting cues from video, we first localize individual(More)
Today's evolving cyber security threats demand new, modern, and cognitive computing approaches to network security systems. In the early years of the Internet, a simple packet inspection firewall was adequate to stop the then-contemporary attacks, such as Denial of Service (DoS), ports scans, and phishing. Since then, DoS has evolved to include Distributed(More)
A novel multimodal (audio-visual) approach to the problem of blind source separation (BSS) is evaluated in room environments. The main challenges of BSS in realistic environments are: sources are moving in complex motions and the room impulse responses are long. For moving sources the unmixing filters to separate the audio signals are difficult to calculate(More)
Host Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In this paper, the authors have developed a new cognitive host based anomaly detection system based on supervised AdaBoost machine learning algorithm. Particularly, information fractal dimension based approach is incorporated(More)
Today's evolving cyber security threats demand new, modern, and cognitive computing approaches to network security systems. In the early years of the Internet, a simple packet inspection firewall was adequate to stop the then-contemporary attacks, such as Denial of Service (DoS), ports scans, and phishing. Since then, DoS has evolved to include Distributed(More)
This paper presents a cognitive feature extraction model based on scaling and multifractal dimension trajectory to analyze internet traffic time series. DNS (Domain Naming System) traffic time series is considered that contains tagged DNS Denial of Service attacks. The first step of the analysis involves transforming the DNS time series into a multifractal(More)