A Survey of Evaluation Techniques for Android Anti-Malware using Transformation Attacks

Android an open-source operating system mainly used for mobile phones have become increasingly popular. Studies suggest that mobile malware threats have recently become a real concern and the impact of malware is getting worse. 2014 saw an astounding 75 percent increase in the Android mobile malware. It is therefore imperative to evaluate the resistance and robustness of anti-malware products for android against various malware. To evaluate existing anti-malware, a systematic framework called DroidChameleon is developed with several common transformation techniques. This survey examines the effectiveness and robustness of popular antimalware tools and compare them against one another aiding in the decision making process involved with developing a secure system

Evaluation of Android anti-malware resistance against transformation attacks

Android being most popular and user-friendly is targeted by most of the malware authors. The malware authors use various transformation techniques to create different variants of malwares. Different transformation techniques such as obfuscation, repackaging, renaming are used mostly. Many anti-malwares are developed to secure the Android devices. Android does not offer file access permissions to all the applications installed. Thus anti-malwares may not provide complete security to the Android devices. In this paper, many such different techniques are presented that can be used to evaluate different anti-malwares

Malware Detection Using N-GRAM Based File Signature Based Method

We know that malware can affect on computer data, they disturb computer .there is large growth in virus of different like Trojan horses, worms, benign etc. however developer has need pay attention on that activity ,need to develop strong anti-analysis technique for that. Malware detection is critical technique in computer security. signature based method for malware detection is used, this is mostly used in commercial antivirus software but this method detect malware only when virus caused damage or already registered. otherwise it fail to detect malware. Applying a methodology proven successful in similar problem-domains, we propose the use of n-grams as file signatures in order to detect unknown malware whilst keeping low false positive ratio. We show that n-grams signatures provide an effective way to detect unknown malware

Anomaly Detection in LAN with ARP Request Monitoring

学位の種別: 修士University of Tokyo(東京大学

Multi-dimensional key generation of ICMetrics for cloud computing

Despite the rapid expansion and uptake of cloud based services, lack of trust in the provenance of such services represents a significant inhibiting factor in the further expansion of such service. This paper explores an approach to assure trust and provenance in cloud based services via the generation of digital signatures using properties or features derived from their own construction and software behaviour. The resulting system removes the need for a server to store a private key in a typical Public/Private-Key Infrastructure for data sources. Rather, keys are generated at run-time by features obtained as service execution proceeds. In this paper we investigate several potential software features for suitability during the employment of a cloud service identification system. The generation of stable and unique digital identity from features in Cloud computing is challenging because of the unstable operation environments that implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a multi-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space. Subsequently, a smooth entropy algorithm is developed to evaluate the entropy of key space

Review of Contemporary Literature on Machine Learning based Malware Analysis and Detection Strategies

Abstract: malicious software also known as malware are the critical security threat experienced by the current ear of internet and computer system users. The malwares can morph to access or control the system level operations in multiple dimensions. The traditional malware detection strategies detects by signatures, which are not capable to notify the unknown malwares. The machine learning models learns from the behavioral patterns of the existing malwares and attempts to notify the malwares with similar behavioral patterns, hence these strategies often succeeds to notify even about unknown malwares. This manuscript explored the detailed review of machine learning based malware detection strategies found in contemporary literature