Abstract: The disclosed computer-implemented method for providing web tracking transparency to protect user data privacy may include (i) receiving a browser request for target websites during a browsing session, (ii) identifying a tracking type for website trackers utilized by the target websites, the tracking type including a direct tracking type or a tracking sharing type, (iii) extracting an information category for the target websites, (iv) detecting text patterns shared between the target websites in a common information category, (v) determining information collected about a user by the website trackers by combining the tracking type for the website trackers, the information category for the target websites, and the detected text patterns, and (v) performing a security action that protects against unsolicited website tracking in future browsing sessions by providing the information collected by the website trackers to the user. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Malware fingerprinting on encrypted Transport Layer Security (TLS) traffic. A method may include obtaining, by the computing device, input data of a TLS communication between a client device and a server device; extracting, by the computing device, features associated with the TLS communication; determining, by the computing device, an association between the TLS communication and a known malware family by applying a clustering algorithm to the extracted features; analyzing, by the computing device, at least one cluster determined by applying the clustering algorithm to determine a purity level; assigning, by the computing device, a malware family fingerprint to the TLS communication based on the purity level; applying, by the computing device, the malware family fingerprint to a backend of a network to determine a probability of an attack; and initiating, by the computing device, a security action based on the probability of the attack.

Abstract: The disclosed computer-implemented method for malware classification may include receiving dynamic analysis traces that include event descriptions regarding malware programs, and labels regarding classes of malware programs; performing a first mapping of the event descriptions to a first set of vector representations, wherein order of the events is not taken into account by the first mapping; performing a second mapping of the event descriptions to a second set of vector representations, wherein order of the events is taken into account by the second mapping; combining the first set of vector representations and the second set of vector representations into a combined set of vector representations; inputting the combined set of vector representations, along with the labels, into an autoencoder; and training the autoencoder to generate a feature space representation that correlates identified features with classes of malware. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Disclosed computer-implemented methods for identifying malicious domain names from a passive domain name system server log (DNS log) may include, in some examples, (1) creating a pool of domain names from the DNS log, (2) identifying respective features of each name in the pool, (3) preparing a list of known benign names and respective features of each known benign name, (4) preparing a list of known malicious names and features of each known malicious name, (5) computing a classification model based on (A) the features of each benign name on the list of benign names and (B) the features of each malicious name on the list of malicious names, (6) identifying respective features of an unclassified domain name, and (7) classifying, using the classification model, the unclassified domain name as malicious, based on the respective features of the unclassified domain name. Various other methods, systems, and computer-readable media are also disclosed.