Polymorphic Malware Detection

The evolution of malware use scenarios necessitates the development of effective strategies to neutralise their destructive impact. One of the most threatening types of malware is polymorphic (metamorphic) viruses, as they are largely able to evade detection by intrusion detection systems, information security management (security events), antivirus software and systems for proactive detection of atypical threats and targeted attacks on endpoints due to their ability to change their own signature. In addition, there has been a rapid increase in recent cyber incidents involving the use of polymorphic (metamorphic) malware. The main reason for this growth is the availability of artificial intelligence technologies that allow attackers to modify the code of already classified malware quickly and efficiently, without requiring significant specialised technical competence. A comparative analysis of existing approaches to detecting polymorphic, oligomorphic and metamorphic malware is carried out. It is found that no group of methods uses to its advantage the key feature of polymorphic (metamorphic) malware – invariant behaviour by a certain subset of features that characterise the same vector of destructive impact of malware. With a view to neutralising the property of modification of its own code by polymorphic (metamorphic) malware, the article proposes an approach to determining its invariant component during behavioural analysis based on a combination of the advantages of behavioural analysis and machine learning techniques – reducing the dimensionality of the studied feature space. Such an approach will potentially allow determining the invariant behaviour of malware as a subset of the studied features for each known type of malware, which in turn forms the basis for implementing a new approach to the effective detection of modified (advanced) malware.

Malicious software, also called malware, is one of the major threats on the Internet today. Despite various antivirus programs, thousands of Internet hosts are daily infected with malware, such as viruses, worms, and Trojan horses. Due to using a variety of obfuscation techniques, polymorphic malware can easily evade signature-based detection techniques by continually changing their appearance or patterns. However, all polymorphic malware samples in the same malware family often follow the same behavioral pattern that can be used to generate a behavioral signature. In this paper, we propose MalHunter, a novel method based on sequence clustering and sequence alignment to automatic generation of behavioral signatures for polymorphic malware detection. We first generate a set of behavioral sequences for different samples of a polymorphic malware, each of which represents a thread's behavior. We then group similar behavioral sequences into the same cluster and generate an alignment pattern for each cluster. We finally build a multiple behavioral signature for the polymorphic malware. MalHunter stores fewer signatures in the signature database due to the generation of a multiple behavioral signature for different samples of each polymorphic malware. The experimental results on a malware collection suggest that MalHunter is both precise and succinct for effective matching and detection of polymorphic malware.

Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.

Despite the continuous updating of anti- detection systems for malicious programs (malware), malware has moved to an abnormal threat level; it is being generated and spread faster than before. One of the most serious challenges faced by anti-detection malware programs is an automatic mutation in the code; this is called polymorphic malware via the polymorphic engine. In this case, it is difficult to block the impact of signature-based detection. Hence new techniques have to be used in order to analyse modern malware. One of these techniques is machine learning algorithms in a virtual machine (VM) that can run the packed malicious file and analyse it dynamically through automated testing of the code. Moreover, recent research used image processing techniques with deep learning framework as a hybrid method with two analysis types and extracting a feature engineering approach in the analysis process in order to detect polymorphic malware efficiently. This paper presents a brief review of the latest applied techniques against this type of malware with more focus on the machine learning method for analysing and detecting polymorphic malware. It will discuss briefly the merits and demerits of it.

Malware or malicious code intends to harm computer systems without the knowledge of system users. These malicious softwares are unknowingly installed by naive users while browsing the Internet. Once installed, the malware performs unintentional activities like (a) steal username, password; (b) install spy software to provide remote access to the attackers; (c) flood spam messages; (d) perform denial of service attacks; etc. With the emergence of polymorphic and metamorphic malware, signature-based detectors are failing to detect new variants of these malware. The primary reason is that malicious code developed in new generation have different syntactic structures from their predecessor, thereby defeating any pattern matching techniques. Thus, the detection of morphed malware remains a complex open research problem for malware analysts. In this chapter, the authors discuss different types of malware with their detection methods. In addition, they present a proposed method employing machine learning techniques for the detection of metamorphic malware. The methodology demonstrates that appropriately selecting prominent features could improve the classification accuracy. The study also depicts that proposed methods that do not require signatures are effective in identifying and classifying morphed malware.

Antivirus software is one of the most widely used tools for detecting and stopping malicious and unwanted files. However, the long term effect of traditional host based antivirus is questionable. Antivirus software fails to detect many modern threats and its increasing complexity has resulted in vulnerabilities that are being exploited by malware. This paper advocates a new model for malware detection on end hosts based on providing antivirus as an in-cloud network service. This model enables identification of malicious and unwanted software by multiple detection engines Respectively,This approach provides several important benefits including better detection of malicious software, enhanced forensics capabilities and improved deployability. Malware detection in cloud computing includes a lightweight, cross-Storge host agent and a network service. In this paper Combines detection techniques, static signatures analyze and Dynamic analysis detection. Using this mechanism we find that cloud- malware detection provides 35% better detection coverage against recent threats compared to a single antivirus engine and a 98% detection rate across the cloud environment.

Malware stand for Malicious Software became a major threat facing the massive amount of data transmitted through the internet and the systems holding that data. Malware detection is the process of identifying the malicious behavior or object as malware. Many methods used to do the detection process, these methods are varied depending on the process used by the detector -anti virus or anti malware is a commercial name of detectors. Signature base, behavior base and specification base. Increasing the detection accuracy is the main goal of researchers in the last decade. In this paper we introduce a dynamic malware detection model by applying the innate immune system to improve the detection accuracy. The proposed model applied to the portable executable file representation by extracting the API call logs from new installed windows environment due to the wide spread of this type of files in different platforms. The results of the experiments show a better detection accuracy of the proposed model for known malware and promising improvement on the new unknown malware and polymorphic malware.

Living in the digital era has brought us countless benefits while introducing certain risks. Today, hundreds of thousands of new malware appear every day, thus increasing the risk of data being stolen, corrupted or exploited by malicious entities. While signatures are typically used to detect known malware using anti-virus scanners, this approach is unable to detect new malware (i.e. zero day attacks), encrypted malware, or polymorphic malware able to change its identifiable features or behavior to evade detection. In this work, we propose a smart artificial intelligence based malware detection approach that leverages a combination of machine learning models as well as static and dynamic analysis techniques for the real-time detection of new or polymorphic malware. The system design is elaborated and extensive testing results are presented to showcase the capabilities of our proposed solution. The performance of eight machine learning models are compared to identify the optimal model for static and dynamic malware analysis, thus providing insights on the ability to use machine learning for real-time malware detection.

The malicious code on the network is increasingly rampant that the traditional detection method of characteristic code has been difficult to deal with malicious code, with features of various variants, deformations and other problems. In this paper we present a new static analysis model based on software fingerprint to distinguish malicious codes. Through obtaining the software call graph by disassembling the binary file and mapping it as an image, shape moments can be obtained as the software fingerprint based on the retrieval theory of content image, combined with moment theory and the image's color, texture and shape features. The idea of pattern matching is used to measure the extracted software fingerprint similarity to determine whether it is malicious code or not. Then, we analyze the collected program samples. Test and verify whether the program has good performance in uniqueness, invariability and sensibility. With the rapid popularization of Internet and corporate information technology acceleration, the computer is a great convenience to people's lives, whether it is shopping, leisure or work and more obvious the importance of the Internet, but due to the openness of the Internet and flexible application and operating system vulnerability so that people can enjoy the benefits brought by the computer at the same time, also is experiencing distress and abuse of all kinds of malicious code threats to network security events increased year by year. Network security incidents, the most serious harm caused by malicious code, causing huge economic losses to the country as a whole, society and the individual, information security has become a major challenge facing. Domestic and foreign researchers turned to the semantics of malicious code, trying to judge the signatures of the two deformation malicious code through the instruction-level semantics rather than program syntax, further can determine whether the deformation of the malicious code. Trying to evade detection of malicious code, malicious codes are disassembled and for standardization or stack analysis system call for the use of fuzzy transformation technology to discriminate. In recent years, the researchers also uses malicious code detection technology engineering methodology, based on feature detection based on the application of malicious code detection technology based on data mining and machine learning. However, while foreign research scholars during the malicious code detection and anti-virus software R & D, the attacker malicious code using anti-debugging techniques, anti-Hook technology, to detect whether the code being debugged, the to find themselves debugger or analysis environment, malicious code using fuzzy transform technique and a series of anti-debugging measures show some non-anomalous behavior of the code, so as to protect their own purposes. In this process, we need manually assistive technology. Most automated virus analysis software only to capture some of the behavior, security experts need further analysis and screening of experimental results, and finally determine the extent of the harm of malicious behavior. In summary, the malicious code on the network more and more rampant code detection method for the traditional characteristics of difficult to deal with malicious code variants, deformation problem, solve the problem of the detection of unstable deformation caused due to malicious code upgrade has become a research focus and difficulty, but also of the issues that must be resolved key issues.

Since the beginning of computing, malicious software has changed dramatically, becoming more complex and elusive. The increase in ransomware attacks has brought attention to the serious risks that malware poses, affecting not only individuals but also organizations, governments, and vital infrastructure like transportation networks and hospitals. Mitigating these dangers requires early identification of harmful behaviour, yet detecting new and unknown malware is still quite difficult. Static and dynamic analysis are the two main types of malware analysis approaches. Dynamic analysis watches how a file behaves in a controlled setting, whereas static analysis looks at a file without running it. Static analysis is less successful since malware writers use evasion strategies including dynamic code loading, encryption, and code obfuscation to evade detection. Conversely, dynamic analysis improves detection capabilities and provides deeper insights into malware behaviour while offering resilience against such evasion tactics. Notwithstanding these benefits, no one method is infallible, and current technologies are not always able to adequately capture the intricacies of polymorphic malware. The methods currently used to analyse polymorphic malware are thoroughly reviewed in this survey, with an emphasis on their advantages, disadvantages, and room for development. This study intends to aid in the creation of more resilient and flexible malware detection systems by assessing the efficacy of different analytical methodologies.

Objectives: This study aimed to design an application that effectively scans, detects, and removes malware based on their signatures and behaviours. Methods/Statistical analysis: The rapid growth in the number and types of malware poses high security risks despite the numerous antivirus softwares with Signature-Based Detection (SBD) method. The SBD method depends on the signatures or malware names that are available in the algorithm database. Findings: Malware is a type of malicious software that poses security threats to the targeted system, resulting in information loss, resource abuse, or system damage. The antivirus software is one of the most commonly used security tools to detect and remove malware. However, the malware defences should focus on the malware signatures since there is no universal way of recognising all malware. Therefore, this study suggested N/A detection technique as the dynamic method (behaviour-based detection method) that depends on the Windows Registry (system database). Both static and dynamic detection methods were assessed in this study. Based on the experimental outcomes, SBD method detected and removed most of malware (only known viruses). Application/Improvements: Meanwhile, the N/A detection method detected and removed all injected malware (known and unknown Trojan horse) within a relatively low running time. Keywords: Dynamic Method, Malicious Software, Malware Detection, Signature Analysis, Static Method

Advanced Persistent Threat is an attack campaign in which an intruder or team of intruders establishes a long-term presence on a network to mine sensitive data, which becomes more dangerous when combined with polymorphic malware. This type of malware is not only undetectable, but it also generates multiple variants of the same type of malware in the network and remains in the system’s main memory to avoid detection. Few researchers employ a visualization approach based on a computer’s memory to detect and classify various classes of malware. However, a preprocessing step of denoising the malware images was not considered, which results in an overfitting problem and prevents us from perfectly generalizing a model. In this paper, we introduce a new data engineering approach comprising two main stages: Denoising and Re-Dimensioning. The first aims at reducing or ideally removing the noise in the malware’s memory-based dump files’ transformed images. The latter further processes the cleaned image by compressing them to reduce their dimensionality. This is to avoid the overfitting issue and lower the variance, computing cost, and memory utilization. We then built our machine learning model that implements the new data engineering approach and the result shows that the performance metrics of 97.82% for accuracy, 97.66% for precision, 97.25% for recall, and 97.57% for f1-score are obtained. Our new data engineering approach and machine learning model outperform existing solutions by 0.83% accuracy, 0.30% precision, 1.67% recall, and 1.25% f1-score. In addition to that, the computational time and memory usage have also reduced significantly.

In today’s world, computer viruses and other forms of malware are among the biggest of the nightmares that haunt information security experts, not to mention a layman. Antivirus programs are the most common, if not the only, relied upon solution available to deal with malware. Different types of antivirus programs offered by multi-billion dollar antivirus industry, signature-based, heuristic-based and hybrid, are still a long way from meeting the expected level of contribution. Significant performance deterioration is also a major downside of antivirus programs. This paper discusses various manual techniques that can be exercised in combination with existing automated countermeasures in order to help in effectively preventing malware with improved performance. The scope of this paper is limited to Microsoft Windows family of operating systems.

Identifying malicious software executables is made difficult by the constant adaptations introduced by miscreants in order to evade detection by antivirus software. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we apply methods designed for gene sequencing to detect malware in a manner robust to attacker adaptations. Whereas most gene classification tools are optimized for and restricted to an alphabet of four letters (nucleic acids), we have selected the Strand gene sequence classifier for malware classification. Strand’s design can easily accommodate unstructured data with any alphabet, including source code or compiled machine code. To demonstrate that gene sequence classification tools are suitable for classifying malware, we apply Strand to approximately 500 GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting nine classes of polymorphic malware. Experiments show that, with minimal adaptation, the method achieves accuracy levels well above 95% requiring only a fraction of the training times used by the winning team’s method.

A Novel Approach towards Windows Malware Detection System Using Deep Neural Networks