Biblio

Malwares are designed to cause harm to the machine without the user's knowledge. Malwares belonging to different families infect the system in its own unique way causing damage which could be irreversible and hence there is a need to detect and analyse the malwares. Manual analysis of all types of malwares is not a practical approach due to the huge effort involved and hence Automated Malware Analysis is resorted to so that the burden on humans can be decreased and the process is made robust. A lot of Automated Malware Analysis tools are present right now both offline and online but the problem arises as to which tool to select while analysing a suspicious binary. A comparative analysis of three most widely used automated tools has been done with different malware class samples. These tools are Cuckoo Sandbox, Any. Run and Intezer Analyze. In order to check the efficacy of the tool in both online and offline analysis, Cuckoo Sandbox was configured for offline use, and Any. Run and Intezer Analyze were configured for online analysis. Individual tools analyse each malware sample and after analysis is completed, a comparative chart is prepared to determine which tool is good at finding registry changes, processes created, files created, network connections, etc by the malicious binary. The findings conclude that Intezer Analyze tool recognizes file changes better than others but otherwise Cuckoo Sandbox and Any. Run tools are better in determining other functionalities.

This document addresses the issue of the actual security level of PDF documents. Two types of detection approaches are utilized to detect dangerous elements within malware: static analysis and dynamic analysis. Analyzing malware binaries to identify dangerous strings, as well as reverse-engineering is included in static analysis for t1he malware to disassemble it. On the other hand, dynamic analysis monitors malware activities by running them in a safe environment, such as a virtual machine. Each method has its own set of strengths and weaknesses, and it is usually best to employ both methods while analyzing malware. Malware detection could be simplified without sacrificing accuracy by reducing the number of malicious traits. This may allow the researcher to devote more time to analysis. Our worry is that there is no obvious need to identify malware with numerous functionalities when it isn't necessary. We will solve this problem by developing a system that will identify if the given file is infected with malware or not.

Enforcing security and resilience in a cloud platform is an essential but challenging problem due to the presence of a large number of heterogeneous applications running on shared resources. A security analysis system that can detect threats or malware must exist inside the cloud infrastructure. Much research has been done on machine learning-driven malware analysis, but it is limited in computational complexity and detection accuracy. To overcome these drawbacks, we proposed a new malware detection system based on the concept of clustering and trend micro locality sensitive hashing (TLSH). We used Cuckoo sandbox, which provides dynamic analysis reports of files by executing them in an isolated environment. We used a novel feature extraction algorithm to extract essential features from the malware reports obtained from the Cuckoo sandbox. Further, the most important features are selected using principal component analysis (PCA), random forest, and Chi-square feature selection methods. Subsequently, the experimental results are obtained for clustering and non-clustering approaches on three classifiers, including Decision Tree, Random Forest, and Logistic Regression. The model performance shows better classification accuracy and false positive rate (FPR) as compared to the state-of-the-art works and non-clustering approach at significantly lesser computation cost.

As time progresses, new and complex malware types are being generated which causes a serious threat to computer systems. Due to this drastic increase in the number of malware samples, the signature-based malware detection techniques cannot provide accurate results. Different studies have demonstrated the proficiency of machine learning for the detection and classification of malware files. Further, the accuracy of these machine learning models can be improved by using feature selection algorithms to select the most essential features and reducing the size of the dataset which leads to lesser computations. In this paper, we have developed a machine learning based malware analysis framework for efficient and accurate malware detection and classification. We used Cuckoo sandbox for dynamic analysis which executes malware in an isolated environment and generates an analysis report based on the system activities during execution. Further, we propose a feature extraction and selection module which extracts features from the report and selects the most important features for ensuring high accuracy at minimum computation cost. Then, we employ different machine learning algorithms for accurate detection and fine-grained classification. Experimental results show that we got high detection and classification accuracy in comparison to the state-of-the-art approaches.

Malware threat classification involves understanding the behavior of the malicious software and how it affects a victim host system. Classifying threats allows for measured response appropriate to the risk involved. Malware incident response depends on many automated tools for the classification of threat to help identify the appropriate reaction to a threat alert. Cuckoo Sandbox is one such tool which can be used for automated analysis of malware and one method of threat classification provided is a threat score. A security analyst might submit a suspicious file to Cuckoo for analysis to determine whether or not the file contains malware or performs potentially malicious behavior on a system. Cuckoo is capable of producing a report of this behavior and ranks the severity of the observed actions as a score from one to ten, with ten being the most severe. As such, a malware sample classified as an 8 would likely take priority over a sample classified as a 3. Unfortunately, this scoring classification can be misleading due to the underlying methodology of severity classification. In this paper we demonstrate why the current methodology of threat scoring is flawed and therefore we believe it can be improved with greater emphasis on analyzing the behavior of the malware. This allows for a threat classification rating which scales with the risk involved in the malware behavior.

With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.

Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.

Nowadays, the digitization of the world is under a serious threat due to the emergence of various new and complex malware every day. Due to this, the traditional signature-based methods for detection of malware effectively become an obsolete method. The efficiency of the machine learning techniques in context to the detection of malwares has been proved by state-of-the-art research works. In this paper, we have proposed a framework to detect and classify different files (e.g., exe, pdf, php, etc.) as benign and malicious using two level classifier namely, Macro (for detection of malware) and Micro (for classification of malware files as a Trojan, Spyware, Ad-ware, etc.). Our solution uses Cuckoo Sandbox for generating static and dynamic analysis report by executing the sample files in the virtual environment. In addition, a novel feature extraction module has been developed which functions based on static, behavioral and network analysis using the reports generated by the Cuckoo Sandbox. Weka Framework is used to develop machine learning models by using training datasets. The experimental results using the proposed framework shows high detection rate and high classification rate using different machine learning algorithms

Nowadays, Malware has become a serious threat to the digitization of the world due to the emergence of various new and complex malware every day. Due to this, the traditional signature-based methods for detection of malware effectively becomes an obsolete method. The efficiency of the machine learning model in context to the detection of malware files has been proved by different researches and studies. In this paper, a framework has been developed to detect and classify different files (e.g exe, pdf, php, etc.) as benign and malicious using two level classifier namely, Macro (for detection of malware) and Micro (for classification of malware files as a Trojan, Spyware, Adware, etc.). Cuckoo Sandbox is used for generating static and dynamic analysis report by executing files in the virtual environment. In addition, a novel model is developed for extracting features based on static, behavioral and network analysis using analysis report generated by the Cuckoo Sandbox. Weka Framework is used to develop machine learning models by using training datasets. The experimental results using proposed framework shows high detection rate with an accuracy of 100% using J48 Decision tree model, 99% using SMO (Sequential Minimal Optimization) and 97% using Random Forest tree. It also shows effective classification rate with accuracy 100% using J48 Decision tree, 91% using SMO and 66% using Random Forest tree. These results are used for detecting and classifying unknown files as benign or malicious.