Timed Automata for Mobile Ransomware Detection

Considering the plethora of private and sensitive information stored in smartphone and tablets, it is easy to understand the reason why attackers develop everyday more and more aggressive malicious payloads with the aim to exfiltrate our data. One of the last trend in mobile malware landascape is represented by the so-called ransomware, a threat capable to lock the user interface and to cipher the data of the mobile device under attack. In this paper we propose an approach to model an Android application in terms of timed automaton by considering system call traces i.e., performing a dynamic analysis. We obtain encouraging results in the experimental analysis we performed exploiting real-world  (ransomware and legitimate) Android applications

Survey of Machine Learning Techniques for Malware Analysis

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies for keeping pace with the speed of development of novel malware. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis. We systematize surveyed papers according to their objectives (i.e., the expected output, what the analysis aims to), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of problems concerning the datasets used in considered works, and finally introduce the novel concept of malware analysis economics, regarding the study of existing tradeoffs among key metrics, such as analysis accuracy and economical costs

Hunting For Metamorphic JavaScript Malware

Internet plays a major role in the propagation of malware. A recent trend is the infection of machines through web pages, often due to malicious code inserted in JavaScript. From the malware writer’s perspective, one potential advantage of JavaScript is that powerful code obfuscation techniques can be applied to evade de- tection. In this research, we analyze metamorphic JavaScript malware. We compare the effectiveness of several static detection strategies and we quantify the degree of morphing required to defeat each of these techniques

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network

Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method

Detection of Malware in Large Networks using Deep Auto Encoders

Data mining and machine learning have been heavily studied in recent years with the purpose of detecting sophisticated malware. The majority of these approaches rely on architectures that do not involve deeply enough into the learning process, despite the fact that they have yielded excellent results. This is because deep learning is finding increasing application in both business and academia thanks due to its skills in feature learning. In this paper, we develop a Deep Auto Encoder (DAE) based detection mechanism to detect the malwares crawling in the large scale networks. The DAE act as an unsupervised deep learning model that helps in detecting the malwares. The simulation is conducted on two different datasets to test the robustness of the model. The results show that the proposed method has higher rate of accuracy in detecting the attacks than other methods

Cyber-offenders versus traditional offenders: An empirical comparison

Bernasco, W. [Promotor]Ruiter, S. [Promotor]Gelder, J.-.L. van [Copromotor