A common technique used by military to realize low probability of intercept (LPI) is linear frequency modulation (LFM) in the field of electronic intelligence (ELINT). This paper estimates the pulse width (PW) and the pulse repetition period (PRP) of LFM signal using instantaneous powers. The instantaneous powers were obtained either using time-marginal or power maxima approximated from a modified version of the Wigner-Ville distribution (WVD). The instantaneous power was also gotten directly from the signal by multiplication with its conjugate. Measurement was then carried out when the instantaneous power is ‘ON’ (the PW) and when it is ‘OFF’ (the PRP) at carefully selected thresholds. Thereafter, the mWVD-based algorithm was tested in the presence of additive white Gaussian noise (AWGN) at various signal-to-noise ratios. Results obtained during the test showed that the time marginal method emerged the best with minimum signal-to-noise ratio (SNR) of -5dB followed closely by the direct method with minimum SNR of -1dB at different thresholds. The results show that the proposed algorithm based on this modified WVD can be deployed in the practical field to determine radar’s performance and function
A malicious URL is a link that is created to spread spams, phishing, malware, ransomware, spyware, etc. A user may download malware that can adversely affect the computer by clicking on an infected URL, or might be convinced to provide confidential information to a fraudulent website causing serious losses. These threats must be identified and handled in a decent time and in an effective way. Detection is traditionally done through the blacklist usage method, which relies on keyword matching with previously known malicious domain names stored in a repository. This method is fast and easy to implement, with the advantage of having low false-positive rates regarding previously recognized malicious URLs. However, this method cannot recognize newly created malicious URLs. To solve this problem, many machine-learning models have been used. In this paper, we introduce an effective machine learning approach that uses an ensemble learner algorithm called AdaBoost (Adaptive Boosting), combined with different algorithms that enhance detection. For datasets filtration, we used CfsSubsetEval technique, which is an algorithm that searches for a subset of features that work well together. Datasets were collected from the UNB repository; divided into four categories: spam, phishing, malware, and defacement URLs; combined with benign URLs, dataset content is based on lexical features. The experimental results indicate that the proposed approach was successful in enhancing the detection accuracy of malicious URLs with less false-positive rates for all experimental algorithms.
The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) -time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.
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