Malware Detection Using Hidden Markov Model

In this chapter, we review sequence-based machine learning methods that are used for malware detection and classification. We start by reviewing the datatypes extracted from code: static features and dynamic traces of program execution. We review recent research that applies machine learning on opcode and API call sequences, call graphs, system calls, registry changes, information flow traces, as well as hybrid and raw data, to detect and classify malware. With a focus on metamorphic malware, we discuss Hidden Markov Models (HMMs) and Long Short-Term Memory (LSTM) networks. We describe their input formats, such as one-hot encoding and vector embeddings, the architecture of the machine learning models, the training process, and the output formats. Finally, we discuss commercial and open-source tools that are used for data extraction from software.

For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer’s attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

Metamorphic malware changes its internal structure across generations, but its functionality remains unchanged. Well-designed metamorphic malware will evade signature detection. Recent research has revealed techniques based on hidden Markov models (HMMs) for detecting many types of metamorphic malware, as well as techniques for evading such detection. A worm is a type of malware that actively spreads across a network to other host systems. In this project we design and implement a prototype metamorphic worm that carries its own morphing engine. This is challenging, since the morphing engine itself must be morphed across replications, which imposes significant restrictions on the structure of the worm. Our design also employs previously developed techniques to evade detection. We provide test results to confirm that this worm effectively evades signature and HMM-based detection, and we consider possible detection strategies. This worm provides a concrete example that should prove useful for additional malware detection research.

Previous research has shown that hidden Markov model (HMM) is a compelling option for malware identification. However, some advanced metamorphic malware have proven to be more challenging to detect with these techniques. In this paper, we separated the importance of the some part of the malware files to train the HMMs aiming at extracting the significant sequences of malware opcodes. These parts have been deemed important according to their dissimilarity to the benign files, as all parts of a malware file are not representative of the malicious nature. Extracting these parts has been performed using the methods similar to sound processing. The results demonstrate that the proposed method has the higher accuracy to the metamorphic malware detection and also has the higher speed at classification, compared to the previous methods.

Malware is a serious threat that weakens computer security. Malware detection is a branch of computer security concerned with the investigation and prevention of malicious software. It is not the only way to protect a company from a cyber-attack. Companies must analyse their risks and vulnerabilities in order to be effective. With this increase in cyber fraud and other malicious activities, traditional methods are not enough to block computers from it as this method has many drawbacks. In order to tackle these issues, researchers have been developing new techniques such as heuristic analysis, and static & dynamic analysis which can detect more than 90% of malware samples. In this paper, we will look at various techniques for detecting computer malware and malicious websites, as well as a versatile framework in which different machine-learning algorithms can be used to successfully distinguish between malware and clean files while attempting to reduce the number of false positives. In this paper, we have reviewed the rise of computer malware and how traditional methods of detection are being replaced by innovative techniques such as the behavioural-based model and the signature-based model.

A novel deep learning-based approach for malware detection

Objectives: 1. To propose a framework for Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique. 2. To extract behavioral patterns from execution reports of malware in terms of its features and generates a data repository. 3. To select the most promising features using information theory based concepts. Methods/Statistical Analysis: Today, malware is a major concern of computer security experts. Variety and in- creasing number of malware affects millions of systems in the form of viruses, worms, Trojans etc. Many techniques have been proposed to analyze the malware to its class accurately. Some of analysis techniques analyzed malware based upon its structure, code flow, etc. without executing it (called static analysis), whereas other techniques (termed as dynamic analysis) focused to monitor the behavior of malware by executing it and comparing it with known malware behavior. Dynamic analysis has proved to be effective in malware detection as behavior is more difficult to mask while executing than its underlying code (static analysis). In this study, we propose a framework for Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique. The proposed framework extracts behavioral patterns from execution reports of malware in terms of its features and generates a data repository. Further, it selects the most promising features using information theory based concepts. Findings: The proposed framework detects the family of unknown malware samples after training of a classifier from malware data repository. We validated the applicability of the proposed framework by comparing with the other dynamic malware analysis technique on a real malware dataset from Virus Total. Application: The proposed framework is a Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique.

Malware detection plays a critical role in securing endpoints such as workstations, servers, cloud instances, and mobile devices. It involves identifying and mitigating malicious activities triggered by various forms of malware, including viruses, trojans, worms, ransomware, adware, and spyware. With the rapid proliferation of sophisticated and evasive malware variants, such as polymorphic and metamorphic malware, traditional signature-based and heuristic approaches are no longer sufficient. These conventional methods often fail to detect previously unseen or obfuscated threats, making the adoption of more intelligent and adaptive systems essential. In response to this challenge, this project explores the application of machine learning techniques to enhance malware detection and classification. Leveraging publicly available malware datasets, various supervised and unsupervised algorithms—such as Decision Trees, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes, and Artificial Neural Networks (ANN)—will be employed and evaluated. Additionally, feature extraction and dimensionality reduction techniques such as Principal Component Analysis (PCA) will be applied to improve model performance. The objective is to assess and compare the models using metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. This project aims to identify the most effective machine learning model for real-time, scalable, and accurate malware detection, contributing to improved cybersecurity practices and proactive threat mitigation. Keywords: Malware detection, machine learning, enterprise security, information gain, random forest, PE analysis, API calls.

Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.

Metamorphic malware changes its internal structure with each generation, while maintaining its original behavior. Current commercial antivirus software generally scan for known malware signatures; therefore, they are not able to detect metamorphic malware that sufficiently morphs its internal structure. Machine learning methods such as hidden Markov models (HMM) have shown promise for detecting hacker-produced metamorphic malware. However, previous research has shown that it is possible to evade HMM-based detection by carefully morphing with content from benign files. In this paper, we combine HMM detection with a statistical technique based on the chi-squared test to build an improved detection method. We discuss our technique in detail and provide experimental evidence to support our claim of improved detection.

Malware detection has played a crucial role in many cyberattacks in recent years. Due to the obfuscated nature of malware, the traditional static analysis technique tends to be ineffective. Additionally, modern malware often can identify dynamic analysis environments, posing challenges to dynamic analysis methods. Thus, feature extraction relies on analysis techniques that tend to be less effective in obfuscated malware, resulting in poor performance of subsequent machine learning-based detectors. This study introduces a Bypass Anti-emulation-based Malware Detection framework (BAE-MD) for enhancing the efficiency of obfuscated malware detection. In other words, BAE-MD includes a method that can bypass the anti-emulation mechanism of malware in a controlled dynamic environment. This forces the malware to decrypt and decompress its actual malicious code to memory. By doing so, Yara rules can be applied to memory dump to extract more than $60$ features to feed into detectors. BAE-MD is evaluated on a malware dataset in comparison with others using static and dynamic analysis technique-based feature extraction. The experimental results can confirm that our method outperforms the others. More investigations are also carried out to illustrate the efficiency of BAE-MD. These results suggest that BAE-MD is a promising approach for dealing with the continuous evolution of malware.

Recent work has presented a technique based on structural entropy measurement as an effective way to detect metamorphic malware. The technique uses two steps, file segmentation and sequence comparison, to calculate file similarity. In previous work, it was observed that similar malware have similar measures of Kolmogorov complexity. A proposed method of estimating Kolmogorov complexity was to calculate the compression ratio of a given malware which could then be used to cluster the malicious software. Malware detection has also been attempted through the use of adaptive data compression and showed promising results. In this paper, we combine these concepts and propose using compression ratios as an alternative measure of entropy with the purpose of segmenting files according to their structural characteristics. We then compare the segment-based sequences of two given files to determine file similarity. The idea is that, even after malware is transformed using a metamorphic engine, the resulting variants still share identifiable structural similarities with the original. Using this proposed technique to identify metamorphic malware, we compare our results with previous work.

Abstract—The increasing prevalence and sophistication of malware attacks have made it essential to develop advanced, automated detection systems. Traditional signature-based methods often fall short, particularly when faced with novel or polymorphic threats. This paper introduces an intelligent malware analysis framework that leverages deep learning models for effective and efficient real-time detection. Integrating both static and dynamic analysis, the system applies deep neural networks to classify software as malicious or benign. The framework aims to enhance detection accuracy, minimize false positives, and improve overall user experience. Keywords: Malware Detection, Deep Learning, User-Centric Security, Static Analysis, Dynamic Analysis, Neural Networks, Cybersecurity

Long Short-term Memory (LSTM) is a sub set of recurrent neural network (RNN) which is specifically used to train to learn long-term temporal dynamics with sequences of arbitrary length. In this paper, long short-term memory (LSTM) architecture is followed for Android malware detection. The data set for evaluation contains real known benign and malware applications from static and dynamic analysis. To achieve acceptable malware detection rates with low computational cost, various LSTM network topologies with several network parameters are used on all extracted features. A stacked LSTM with 32 memory blocks containing one cell each has performed well on detection of all individual behaviors of malicious applications in comparison to other traditional static machine learning classifier. The architecture quantifies experimental results up to 1000 epochs with learning rate 0.1. This is primarily due to the reason that LSTM has the potential to store long-range dependencies across time-steps and to correlate with successive connection sequences information. The experiment achieved the Android malware detection of 0.939 on dynamic analysis and 0.975 on static analysis on well-known datasets.