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With the phenomenal increase in Android apps usage and storing of personal information on mobile devices, securing this sensitive information has assumed significance. The Android application developers knowingly or unknowingly create apps that may directly or indirectly leak this information to outside world. The majority of state-of-the-art approachesdetect leaks through inter-component communication...
In recent years, malware has emerged as a critical security threat. In addition, malware authors continue to embed numerous anti-detection features to evade the existing malware detection approaches. Against this advanced class of malicious programs, dynamic behavior-based malware detection approaches outperform the traditional signature-based approaches by neutralizing the effects of obfuscation...
Advanced persistent attacks, incorporated by sophisticated malware, are on the rise against hosts, user applications and utility software. Modern malware hide their malicious payload by applying packing mechanism. Packing tools instigate code encryption to protect the original malicious payload. Packing is employed in tandem with code obfuscation/encryption/compression to create malware variants....
Packed malware imposes negative impact on the accuracy of AV scanners. It is essential for a security researcher to nullify the effects of packing tools, prior to malware detection. Numerous open and commercial packers are available to facilitate unwelcome intentions of malware authors. Thus, identification of packers becomes necessary phase prior to malware scanning. In this paper, we have proposed...
Malware poses a big threat to computer systems now a days. Malware authors often use encryption/compression methods to conceal their malicious executables data and code. These methods that transform some or all of the original bytes into a series of random looking data bytes appear in 80 to 90% of malware samples. This fact creates special challenges for anti-virus scanners who use static and dynamic...
In this paper, we use machine learning techniques for classifying a Portable Executable (PE) file as malwareor benign. This is achieved by extracting a new feature also referred to us as MultiComponent Feature composed of (a) PE metadata (b) Principal Instruction Code (PIC)(c) mnemonic bi-gram and (d) prominent unigrams that characterizes malware/benign files. Reduced feature set are obtained using...
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