An electronic message is analyzed for malware contained in the message. Text of an electronic message may be analyzed to detect and process malware content in the electronic message itself. The present technology may analyze an electronic message and attachments to electronic messages to detect a uniform resource location (URL), identify whether the URL is suspicious, and analyze all suspicious URLs to determine if they are malware. The analysis may include re-playing the suspicious URL in a virtual environment which simulates the intended computing device to receive the electronic message. If the re-played URL is determined to be malicious, the malicious URL is added to a black list which is updated throughout the computer system.

Electronic message analysis for malware detection

According to one embodiment, a computerized method comprises operations of instantiating a first virtual machine instance and a second virtual machine instance to run concurrently with the first virtual machine instance. The first virtual machine instance provides a first virtual operating environment while the second virtual machine instance is adapted to share the resources allocated to the first virtual machine instance. The second virtual machine instance is further adapted to allocate additional resources upon conducting a Copy-On Write operation.

Optimized resource allocation for virtual machines within a malware content detection system

According to one embodiment, in response to receiving a plurality of uniform resource locator (URL) links for malicious determination, any known URL links are removed from the URL links based on a list of known link signatures. For each of remaining URL links that are unknown, a link analysis is performed on the URL link based on link heuristics to determine whether the URL link is suspicious. For each of the suspicious URL links, a dynamic analysis is performed on a resource of the suspicious URL link. It is classified whether the suspicious URL link is a malicious link based on a behavior of the resource during the dynamic analysis.

System and method for detecting malicious links in electronic messages

A computerized method is described in which one or more received objects are analyzed by an advanced persistent threat (APT) detection center to determine if the objects are APTs. The analysis may include the extraction of features describing and characterizing features of the received objects. The extracted features may be compared with features of known APT malware objects and known non-APT malware objects to determine a classification or probability of the received objects being APT malware. Upon determination that the received objects are APT malware, warning messages may be transmitted to a user of associated client devices. Classified objects may also be used to generate analytic data for the prediction and prevention of future APT attacks.

Advanced persistent threat (apt) detection center

Techniques for detecting malicious attacks may monitor activities during execution of content (e.g., network downloads and email) to identify an instruction backdoor on a computer. An instruction backdoor is malware that opens a port for listening for instructions from a remote server. Such instructions may trigger activation of malicious activity. These techniques employ virtual machines that, in a controlled and monitored environment, permit content, when loaded and executed, to open ports. When those ports are not the authorized ones used for communications by known programs installed on the computer, an instruction backdoor is discovered.

Systems and methods for automatically detecting backdoors

A technology is described for debugging in a cluster processing network. A scheduler can dispatch a process that is part of the cluster job for execution. Further, a compute node can be used to execute the process dispatched by the scheduler to the compute node. A debugger can be activated in response to an unhandled suspension event in the process on the compute node. In addition, the debugger can send notification messages regarding the unhandled suspension event. A job monitor can receive a notification from the debugger that an unhandled suspension event has occurred. The notification can be displayed to a user via the job monitor.

Debugging in a cluster processing network

Visual representations of multiple call stacks in a parallel programming system include a stack segments graph constructed by coalescing data from multiple stacks. The graph has nodes that represent stack segments and has arcs between adjacent segments. Similar stack frames are represented by the same node. In a stack prefix view of the graph, arcs are directed from a node representing stack frames to a node representing subsequently executed stack frames. In a method-centered view, an arc is shown between a node representing stack frames of a selected method and a node representing adjacent stack frames. The graph can be based on call stacks of all tasks or all threads, or based on call stacks of tasks or threads flagged by a user. Stack frame, thread, and/or task details are also displayed.

Simultaneously displaying multiple call stacks in an interactive debugger

Assistance is given to aid in optimizing a program's performance during initial development while the program's features are still being implemented and/or debugged, without interfering with that development, by providing easy-to-ignore yet accurate tips about a program's performance inside a debugger. Raw performance information for a software program which is being debugged in a debugger is adjusted by removing from it a measured debug overhead or other diagnostic overhead. Some factors considered when measuring overhead include pauses, context switches, debug versus release build presence, bounds checking, funceval, and call stack analyses. The debugger is enhanced to display the adjusted program performance measure in a graphical user interface, next to the corresponding source code. The enhanced debugger updates the adjusted program performance measure value and keeps its screen location current as the developer moves through the source code, providing more detailed performance information upon request.

Performance optimization tip presentation during debugging

Learn the essentials for developing mobile applications for any Windows-based mobile device. Focusing on proven techniques and practices, this guide addresses the real-world needs of experienced Microsoft Windows® mobile developers. Users are growing increasingly dependent on mobile devices, and with innovations that make it easy to manage data synchronization, this proliferation will continue. Developers need to respond to this evolution with more than simple adaptations of the user interface-they need to implement mobile solutions for most of their applications. From expert authors with years of real-world experience, this book addresses this evolution, covering key mobile-development topics, including design, debugging, deployment, performance optimization, security, and globalization. It also covers mobile applications that use Microsoft .NET Compact Framework 2.0, Microsoft SQL Server™ 2005 Everywhere Edition, and Microsoft Windows Mobile® 5.0, running on devices such as Pocket PCs and Windows Mobile Smartphones. In addition, it includes extensive code samples in Microsoft Visual C#®, with additional code sample in Microsoft Visual Basic® on the book's companion Web site.

Microsoft Mobile Development Handbook

Launching a debugging process. A method includes at a compute node on a cluster private network, receiving a debug job via a scheduler of a head node from a client on a public network. The head node is connected to both the cluster private network and the public network. The public network is external to the cluster private network. The method further includes beginning processing the debug job, and as a result initiating debugging by starting one or more debugger remote agents at the compute node. The method further includes beginning processing a user job in the presence of the started debugger remote agents at the compute node. The client is informed that the one or more debugger remote agents are ready to debug the user job. A debugger client at the client is connected to the one or more debugger remote agents.

Debugger launch and attach on compute clusters

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