In the second vulnerability, incorrect permissions let regular users specify their code as a print provider. Because print providers run in a local System context, users can gain additional privileges on the local machine. You cannot exploit this vulnerability remotely. Click here to learn more about these vulnerabilities.
Nokia says intelligence is all about context
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Cognitive Radio (CR) is defined as "a radio that is aware of its surroundings and adapts intelligently". While CR technology is mainly cited as the enabler for solving the spectrum scarcity problems by the means of Dynamic Spectrum Access (DSA), perspectives and potential applications of the CR technology far surpass the DSA alone. For example, cognitive capabilities and on-the-fly reconfiguration abilities of CRs constitute an important next step in the Electronic Warfare (EW). They may enable the jamming entities with the capabilities of devising and deploying advanced jamming tactics. Analogously, they may also aid the development of the advanced intelligent self-reconfigurable systems for jamming mitigation. This work outlines the development and implementation of the Spectrum Intelligence algorithm for Radio Frequency (RF) interference mitigation. The developed system is built upon the ideas of obtaining relevant spectrum-related data by using wideband energy detectors, performing narrowband waveform identification and extracting relevant statistical parameters. The recognized relevant spectrum activities are then continuously monitored and stored. Coupled with the self-reconfigurability of various transmission-related parameters, the spectrum intelligence is the facilitator for the advanced interference mitigation strategies. The implementation is done on the Cognitive Radio coaxial test bed architecture. Test bed consists of two Software Defined Radio (SDR) Secure Wideband Multi-role - Single-Channel Handheld (SWAVE HH) terminals, each interconnected with the computationally powerful System-on-Module (SoM) embodied with a Digital Signal Processor (DSP) and a Field Programmable Gate Array (FPGA). SWAVE HH is a fully functional SDR terminal operable in Very High Frequency (VHF) and Ultra High Frequency (UHF) bands, capable of hosting a multitude of both legacy and new waveforms. Additionally, it provides support for remote control of its transmit and receive parameters via the Simple Network Management Protocol (SNMP). Most of the signal processing is delegated to the SoM. After each predefined number of seconds, SWAVE HH outputs a burst of samples from its Analog-to-Digital-Converter (ADC) over the serial port to the SoM. There, the samples, corresponding to 120MHz around the center carrier frequency of the radio, are transformed into the frequency domain using the Fast Fourier Transform (FFT), and are analyzed by the implemented Energy Detector (ED). ED performs thresholding and, by employing the maximum likelihood decision rule, identifies a number of frequency regions, corresponding to narrowband waveform candidates, from the original wideband signal. Each of the identified candidate narrowband waveforms is then analyzed by the Feature Detector (FD). Namely, their maximum amplitude, center frequency and bandwidth are extracted and then compared to the features of the waveforms pre-stored in the database, eventually classifying them as either a "known" or an "unknown" waveform. Information pertaining to all the identified waveforms in the system, along with the results of their classification, and in addition the information about the currently observed spectrum holes, is then sent to the Spectrum Intelligence (SI) algorithm. SI algorithm keeps track of the occurrences of "known" and "unknown" waveform transmissions for each of the identified channels-of-interest, and subsequently triggers the corresponding action. For the simplicity purposes, we consider all "unknown" waveforms as "potentially malicious", i.e. corresponding to waveforms created by the jamming entities. SI invokes the proactive channel surfing whenever the "unknown" transmission is taking place on a carrier frequency close to the carrier frequency currently used for the transmission. SI chooses a new transmission frequency based on the identified spectrum holes, as well as the recent history of occurrences of identified "potentially malicious" waveforms in these spectrum holes. The new transmission frequency is instantly invoked by issuing the appropriate SNMP "change RF channel" command, both to the transmitter and to the receiver side. Both "known" and "unknown" signals are created and injected into the channel using the Vector Signal Generator using a pre-defined pattern. Signals powerful enough to significantly degrade the communication quality are considered. Degradation of communication link is measured by the "Link quality metric" - a Quality-of-Service (QoS) metric built-in within the SWAVE HH, which is directly related to the instantaneous Packet Error Rate (PER). The performance of the proactive channel surfing based on SI is evaluated as the percentage of time slots where successful transmission is taking place (Link quality is over the pre-defined acceptable threshold) over the percentage of time slots where transmission is considered jammed (Link quality is under the pre-defined threshold). The experiments were performed for varying number of non-overlapping channels (from 2 to 10), and varying patterns of the created interference (emulating varying complexity of the supposed jammer). The contributions of this paper are multi-fold, and may be summarized as follows: Implementation of Energy Detection based spectrum sensing algorithm is presented, which serves as the basis for the developed Feature Detection algorithm. FD continuously outputs the categorized waveforms to the Spectrum Intelligence algorithm, whose role is creating and maintaining spectrum awareness, and maintaining interference-free communication by means of proactive channel surfing scheme. The implementation of all the algorithms is done on the real-life SDR/CR platform, consisting of SDR RF front end coupled with the SoM, which is in charge of all the signal processing. Future work will focus on the development of more complex feature detection algorithm, which will be able to extract more relevant and precise (cyclostationary) features of the detected waveforms. In addition, since different waveforms exhibit different anti-jamming properties, dependent mainly on the employed modulation and bandwidth, Spectrum Intelligence algorithm will be embodied with more advanced anti-interference strategies, namely with options of switching between different waveforms, as well as altering transmission power. 2ff7e9595c
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