This paper looks into the modulation classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying SNR conditions ranging from 0dB to 20dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data and on-line classification on the low-cost spectrum sensors are studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.

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Abstract. The class of Cross-site Scripting (XSS) vulnerabilities is the most prevalent security problem in the field of Web applications. One of the main attack vectors used in connection with XSS is session hijacking via session identifier theft. While session hijacking is a client-side attack, the actual vulnerability resides on the server-side and, thus, has to be handled by the website's operator. In consequence, if the operator fails to address XSS, the application's users are defenseless against session hijacking attacks. In this paper we present SessionShield, a lightweight client-side protection mechanism against session hijacking that allows users to protect themselves even if a vulnerable website's operator neglects to mitigate existing XSS problems. SessionShield is based on the observation that session identifier values are not used by legitimate clientside scripts and, thus, need not to be available to the scripting languages running in the browser. Our system requires no training period and imposes negligible overhead to the browser, therefore, making it ideal for desktop and mobile systems.

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alram rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.