Traditional malware signature detection methods extract signatures from an invariant portion of the actual executable part of a target application being scanned for malware. However, these detection methods present numerous problems when implemented in mobile networks and on mobile platforms.
A major constraint of scanning within a network is fragmented data. Generally, mobile phone applications contain executables which are compressed and divided into multiple packages for facilitating transmission of such executable in a mobile network. These data packages must then be re-assembled and uncompressed prior to installation and execution. For example, software installation files (SIS packages) in the Symbian® operating system and CAB packages in the Windows Mobile® operating system, have to be re-assembled and “uncompressed” to obtain the executables. Therefore, while scanning for malware, it is not possible to obtain the entire data corresponding to a malicious application because the malicious application may exist across multiple packages in a compressed format. Also, it is cumbersome and time consuming to attempt to re-assemble and uncompress such data packets as they pass through the mobile network due to the latency these operations would introduce.
Also, comprehensive signature matching as a virus or malware detection method on memory-constrained devices, like mobile phones, is difficult to efficiently implement due to the need for a large database of identified malware signatures. String matching is also processor intensive and results in a high computational tax on a mobile device wherein such mobile platforms have relatively low processing power such that large processing and memory requirements results in lowered performance and excessive battery drain.
When files in file systems are modified, it is good practice to run malware checks on any changed files. However, some mobile device operating systems provide only a flag to indicate that a file somewhere in the file system has change. Therefore, it is difficult to determine which file to scan for malware.
Malware checking using signatures is often processor and memory intensive. Malware also changes constantly, requiring continual updates to stay current. Therefore, some of the best and most advanced programs for detecting malware are usually running at network operations centers and may not be available for running on mobile handsets. Mobile handsets, especially those with limited processing capability and operating systems or those that do not permit memory access for malware scanning, will require some other method of verifying that resident applications are free of malware.
In some mobile devices, e.g., BlackBerry® type devices by Research In Motion® and some Palm® devices, access to the binary code of an installed program is prohibited by the operating system. Access to the binary code in other devices may also be limited due to constraints such as power, permissioning, or other limitations. Therefore, one cannot always apply the traditional binary, signature-based methods of scanning binary code to detect malware in such devices.
Using traditional signature detection, every malware variant will have its own signature. Polymorphic viruses, i.e., viruses that change their signatures every time they replicate, may create undetectable variants. Scalability is also a concern for these signature-based detection algorithms since a new signature needs to be added for every new malware variant in the worst case.
Even though each new malware variant tends to perform the same or similar kind of malicious behavior, traditional signature detection can only detect a new malware variant by adding a new signature to its list and scanning for the new signature. As the list of variants grows, the number of signatures grows, further taxing a system's computational and storage capacity.
Signature-based detection requires both storage of a large number of known malware signatures and processing capacity to search for suspected malware programs with those signatures. Mobile devices however are limited in both storage space and computational ability, thus making them difficult platforms for using existing methods of malware detection that were designed for more robust computing platforms such as desktop and laptop systems.
Most malware, whether worm or virus, share a common characteristic: they tend to spread over time from one device to another device if not contained. The ability to get up-to-date and real-time metrics on mobile networks is valuable to network administrators tracking worm and other virus attacks. There is a need to assimilate statistical information about potential malware on the network and present it to network administrators in a meaningful way so they can quickly become apprised of worm and other virus attacks before they have had a chance to widely proliferate.
New malware and malware variants are constantly appearing. Once new malware has been identified, service providers need a way to update mobile devices in the network so that they can remove the new malware from the mobile devices or prevent other mobile devices from becoming infecting. With most malware prevention systems, users manually initiate a process to update their malware prevention system with a server. In the interim, however, their systems remain vulnerable to the new malware. With the growing popularity of smart phones and the potential for greater interaction between mobile phones, there is a need to be able to update mobile devices as soon as new malware is identified.