Abstract: An apparatus for scanning vulnerabilities, wherein the apparatus includes at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to access at least a manifest file, wherein the at least manifest file includes at least a direct dependency, scan the manifest file for a software package data, extract the software package data from the manifest file, generate at least a dependency tree as a function of the software package data, and store the dependency tree in a database. A method for scanning vulnerabilities is also disclosed.

Abstract: A system for automated malicious software detection includes a computing device, the computing device configured to receive a software component, identify at least an element of software component metadata corresponding to the software component, determine a malicious quantifier as a function of the software component metadata, wherein determining the malicious quantifier further comprises obtaining a source repository, the source repository including at least an element of source metadata, and determining the malicious quantifier as a function of the at least an element of software component metadata and the at least an element of source repository metadata using a malicious machine-learning model, and transmit a notification as a function of the malicious quantifier and a predictive threshold.

Abstract: A system for automated malicious software detection includes a computing device, the computing device configured to receive a software component, identify at least an element of software component metadata corresponding to the software component, determine a malicious quantifier as a function of the software component metadata, wherein determining the malicious quantifier further comprises obtaining a source repository, the source repository including at least an element of source metadata, and determining the malicious quantifier as a function of the at least an element of software component metadata and the at least an element of source repository metadata using a malicious machine-learning model, and transmit a notification as a function of the malicious quantifier and a predictive threshold.

Abstract: A system for automated malicious software detection includes a computing device, the computing device configured to receive a software component, identify at least an element of software component metadata corresponding to the software component, determine a malicious quantifier as a function of the software component metadata, wherein determining the malicious quantifier further comprises obtaining a source repository, the source repository including at least an element of source metadata, and determining the malicious quantifier as a function of the at least an element of software component metadata and the at least an element of source repository metadata using a malicious machine-learning model, and transmit a notification as a function of the malicious quantifier and a predictive threshold.

Abstract: A technique for determining the time of arrival of a signal involves correlating a received signal with a correlation function to produce a stream of correlation samples including a peak sample, pre-peak samples, and post-peak samples. A curve matching table includes sets of values, where each set of values represents discrete points of a correlation function having a peak at a respective different time shift relative to the timing of the peak sample. The sets of values include first sets of values that are matched with a first set of samples having no post-peak samples from the correlation samples, and second sets of values that are matched with a second set of samples having at least one post-peak sample. The time of arrival of the signal is determined from the timing of the peak of the correlation function represented by the set of values that produces the best match.

Abstract: A technique for determining the time of arrival of a signal involves correlating a received signal with a correlation function to produce a stream of correlation samples including a peak sample, pre-peak samples, and post-peak samples. A curve matching table includes sets of values, where each set of values represents discrete points of a correlation function having a peak at a respective different time shift relative to the timing of the peak sample. The sets of values include first sets of values that are matched with a first set of samples having no post-peak samples from the correlation samples, and second sets of values that are matched with a second set of samples having at least one post-peak sample. The time of arrival of the signal is determined from the timing of the peak of the correlation function represented by the set of values that produces the best match.