Abstract: Inline package name based supply chain attack detection and prevention is disclosed. An indication that a client device has made a request to a remote server for a package is received. A data appliance then performs an action responsive to the received indication. In an example implementation, the data appliance makes a determination of whether the request for the package is associated with a nonexisting package.

Abstract: A sample is received for analysis. A determination is made that the sample was compiled for a CPU architecture that is different from a host CPU architecture. The sample is executed in an emulated user space corresponding to the CPU architecture for which the sample was compiled. The emulated user space is provided by executing a user space emulation utility in a virtual machine that shares the host CPU architecture.

Abstract: Inline package name based supply chain attack detection and prevention is disclosed. An indication that a client device has made a request to a remote server for a package is received. A data appliance then performs an action responsive to the received indication. In an example implementation, the data appliance makes a determination of whether the request for the package is associated with a nonexisting package.

Abstract: Domain Name System (DNS) security using process information is provided. An application accessing an internet service using a domain name is determined. Process information associated with the application along with an associated DNS query to identify an IP address associated with the domain name are identified. The process information and the associated DNS query to a DNS security service are sent. An action based on a response from the DNS security service is performed.

Abstract: Automatic generation of a malware signature is disclosed. Code of a sample including packages and function names is parsed. Standard type packages and vendor type packages are filtered from the code of the sample to obtain main type packages. A signature using a fuzzy hash for the sample is generated based on the main type packages. A determination of whether the sample is malware is performed using the signature and a similarity score threshold.

Abstract: Techniques for providing a smart proxy for a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a smart proxy for a large scale high-interaction honeypot farm includes receiving tunneled traffic at a smart proxy from a sensor for a honeypot farm that is executed in a honeypot cloud, wherein the tunneled traffic is forwarded attack traffic, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; and forwarding the tunneled traffic to an instance of the matching type of vulnerable service.

Abstract: Techniques for providing a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a large scale high-interaction honeypot farm includes sending traffic detected at a sensor to a smart proxy for a honeypot farm that is executed in a honeypot cloud, wherein the traffic is forwarded attack traffic that is sent using a tunneling protocol, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; forwarding the traffic to an instance of the matching type of vulnerable service; and executing a security agent associated with the instance of the matching type of vulnerable service to identify a threat by monitoring behaviors and detecting anomalies or post exploitation activities.

Abstract: Domain Name System (DNS) security using process information is provided. An application accessing an internet service using a domain name is determined. Process information associated with the application along with an associated DNS query to identify an IP address associated with the domain name are identified. The process information and the associated DNS query to a DNS security service are sent. An action based on a response from the DNS security service is performed.

Abstract: A sample is received for analysis by a virtualized environment. A determination is made that the sample was compiled for a CPU architecture that is different from a host CPU architecture. The sample is executed in an emulated user space corresponding to the CPU architecture for which the sample was compiled. The emulated user space is provided by executing a user space emulation utility in a virtual machine that shares the host CPU architecture.

Abstract: A sample is received for analysis. A determination is made that the sample was compiled for a CPU architecture that is different from a host CPU architecture. The sample is executed in an emulated user space corresponding to the CPU architecture for which the sample was compiled. The emulated user space is provided by executing a user space emulation utility in a virtual machine that shares the host CPU architecture.

Abstract: Techniques for providing a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a large scale high-interaction honeypot farm includes sending traffic detected at a sensor to a smart proxy for a honeypot farm that is executed in a honeypot cloud, wherein the traffic is forwarded attack traffic that is sent using a tunneling protocol, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; forwarding the traffic to an instance of the matching type of vulnerable service; and executing a security agent associated with the instance of the matching type of vulnerable service to identify a threat by monitoring behaviors and detecting anomalies or post exploitation activities.

Abstract: Techniques for providing a smart proxy for a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a smart proxy for a large scale high-interaction honeypot farm includes receiving tunneled traffic at a smart proxy from a sensor for a honeypot farm that is executed in a honeypot cloud, wherein the tunneled traffic is forwarded attack traffic, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; and forwarding the tunneled traffic to an instance of the matching type of vulnerable service.

Abstract: Techniques for providing a smart proxy for a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a smart proxy for a large scale high-interaction honeypot farm includes receiving tunneled traffic at a smart proxy from a sensor for a honeypot farm that is executed in a honeypot cloud, wherein the tunneled traffic is forwarded attack traffic, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; and forwarding the tunneled traffic to an instance of the matching type of vulnerable service.

Abstract: Techniques for providing a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a large scale high-interaction honeypot farm includes sending traffic detected at a sensor to a smart proxy for a honeypot farm that is executed in a honeypot cloud, wherein the traffic is forwarded attack traffic that is sent using a tunneling protocol, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; forwarding the traffic to an instance of the matching type of vulnerable service; and executing a security agent associated with the instance of the matching type of vulnerable service to identify a threat by monitoring behaviors and detecting anomalies or post exploitation activities.

Abstract: A sample is received for analysis by a virtualized environment. A determination is made that the sample was compiled for a CPU architecture that is different from a host CPU architecture. The sample is executed in an emulated user space corresponding to the CPU architecture for which the sample was compiled. The emulated user space is provided by executing a user space emulation utility in a virtual machine that shares the host CPU architecture.

Abstract: A sample is received for analysis by a virtualized environment. A determination is made that the sample was compiled for a CPU architecture that is different from a host CPU architecture. The sample is executed in an emulated user space corresponding to the CPU architecture for which the sample was compiled.

Abstract: Techniques for providing a smart proxy for a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a smart proxy for a large scale high-interaction honeypot farm includes receiving tunneled traffic at a smart proxy from a sensor for a honeypot farm that is executed in a honeypot cloud, wherein the tunneled traffic is forwarded attack traffic, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; and forwarding the tunneled traffic to an instance of the matching type of vulnerable service.

Abstract: Techniques for providing a large scale high-interaction honeypot farm are disclosed. In some embodiments, a system/method/computer program product for providing a large scale high-interaction honeypot farm includes sending traffic detected at a sensor to a smart proxy for a honeypot farm that is executed in a honeypot cloud, wherein the traffic is forwarded attack traffic that is sent using a tunneling protocol, and wherein the honeypot farm includes a plurality of container images of distinct types of vulnerable services; selecting a matching type of vulnerable service from the plurality of container images of distinct types of vulnerable services based on a profile of the attack traffic; forwarding the traffic to an instance of the matching type of vulnerable service; and executing a security agent associated with the instance of the matching type of vulnerable service to identify a threat by monitoring behaviors and detecting anomalies or post exploitation activities.

Abstract: A first mobile application is received. A reversing operation is performed on the first mobile application. A static analysis engine is used to determine a plurality of libraries included in the mobile application. Each library included in the plurality of libraries is categorized. A determination that the first mobile application is similar to a second mobile application based at least in part on a comparison of the respective categorizations of the respective libraries included in the respective first and second mobile applications. Commonality in the libraries of the two mobile applications can be used for a variety of purposes including detecting repackaging and also common authorship.

Abstract: A sample is received for analysis by a virtualized environment. A determination is made that the sample was compiled for a CPU architecture that is different from a host CPU architecture. The sample is executed in an emulated user space corresponding to the CPU architecture for which the sample was compiled.