Malware Analysis Research Articles

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

IHBOT: An Intelligent and Hybrid Model for Investigation and Classification of IoT Botnet

The Internet of Things (IoT) is revolutionizing the technological market with exponential growth year wise. This revolution of IoT applications has also brought hackers and malware to gain remote access to IoT devices. The security of IoT systems has become more critical for consumers and businesses because of their inherent heterogenous design and open interfaces. Since the release of Mirai in 2016, IoT malware has gained an exponential growth rate. As IoT system and their infrastructure have become critical resources that triggers IoT malware injected by various shareholders in different settings. The enormous applications cause flooding of insecure packets and commands that fueled threats for IoT applications. IoT botnet is one of the most critical malwares that keeps evolving with the network traffic and may harm the privacy of IoT devices. In this work, we presented several sets of malware analysis mechanisms to understand the behavior of IoT malware. We devise an intelligent and hybrid model (IHBOT) that integrates the malware analysis and distinct machine learning algorithms for the identification and classification of the different IoT malware family based on network traffic. The clustering mechanism is also integrated with the proposed model for the identification of malware families based on similarity index. We have also applied YARA rules for the mitigation of IoT botnet traffic.

The Rise of Deep Learning in Cyber Security: Bibliometric Analysis of Deep Learning and Malware

Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y.

Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence.

Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence.

A comprehensive investigation into robust malware detection with explainable AI

In today’s digital world, malware poses a serious threat to security and privacy by stealing sensitive data and disrupting computer systems. Traditional signature-based detection methods have become inefficient and time-consuming. However, data-driven AI techniques, particularly machine learning (ML) and deep learning (DL), have shown effectiveness in detecting malware by analyzing behavioral characteristics. Despite their promising performance, the black-box nature of these models requires improved explainability to facilitate their adoption in real-world applications. This can complicate the ability of cybersecurity experts to evaluate the model’s reliability. In this work, Explainable Artificial Intelligence (XAI) is employed to comprehend and evaluate the decisions made by machine learning models in the detection of malware on Android devices. To evaluate malware detection, experiments were conducted using CICMalDroid dataset by applying ML models like Logistic Regression and several tree algorithms. An overall 94% F1-score was achieved, and interpretable explanations for model decisions were provided, highlighting more critical features that contributed to accurate classifications. It was found that employing XAI techniques can provide valuable insights for malware analysis researchers, enhancing their understanding of the operations of the ML model, rather than solely focusing on improving accuracy.

Advancements in automated malware analysis: evaluating the efficacy of open-source tools in detecting and mitigating emerging malware threats to US businesses

Advancements in automated malware analysis: evaluating the efficacy of open-source tools in detecting and mitigating emerging malware threats to US businesses

Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network

Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network

Uncovering security vulnerabilities through multiplatform malware analysis

AbstractCybercriminals, keen on staying anonymous, focus on creating secure connections to hide their activities. This has fueled the rise of malware, especially remote access trojans (RATs), designed to protect themselves for long‐term use and profit. This research investigates how multiplatform malware analysis can expose security weaknesses across Windows, Linux, and macOS systems. We designed and analyzed custom malware to uncover vulnerabilities that attackers could exploit to establish persistent, hidden access. The malware was programmed to bypass detection while performing actions like stealing sensitive data, launching denial‐of‐service attacks, and generating fake website traffic. Controlled lab experiments and real‐world simulations confirmed the malware's ability to operate stealthily on various platforms, highlighting critical security gaps. By uncovering these vulnerabilities, this study provides valuable insights for strengthening cybersecurity defenses.

PAFE: A lightweight visualization-based fast malware classification method

With the development of automated malware toolkits, cybersecurity faces evolving threats. Although visualization-based malware analysis has proven to be an effective method, existing approaches struggle with challenging malware samples due to alterations in the texture features of binary images during the visualization preprocessing stage, resulting in poor performance. Furthermore, to enhance classification accuracy, existing methods sacrifice prediction time by designing deeper neural network architectures. This paper proposes PAFE, a lightweight and visualization-based rapid malware classification method. It addresses the issue of texture feature variations in preprocessing through pixel-filling techniques and applies data augmentation to overcome the challenges of class imbalance in small sample datasets. PAFE combines multi-scale feature fusion and a channel attention mechanism, enhancing feature expression through modular design. Extensive experimental results demonstrate that PAFE outperforms the current state-of-the-art methods in both efficiency and effectiveness for malware variant classification, achieving an accuracy rate of 99.25 % with a prediction time of 10.04 ms.

Reverse Engineering for Static Analysis of Android Malware in Instant Messaging Apps

Malware poses a significant threat to Android devices due to their high prevalence and vulnerability to attacks. Analyzing malware on these devices is crucial given the persistent and sophisticated threats targeting Android users. Static analysis of Android malware is a key approach used to detect malicious software without executing the application. This method involves meticulously examining the application's source code or binaries to identify signs of suspicious or harmful activities. The research methodology consists of three stages. The first stage involves collecting malware samples spread through instant messaging applications. The second stage employs reverse engineering, where APK files are decompiled to extract their contents. Following this, a static analysis is conducted, focusing on the AndroidManifest.xml file and the source code to identify the behavior and potential threats posed by the malware. The static analysis results revealed that Android malware often requests sensitive permissions to access personal data, such as receiving, reading, and sending SMS, as well as accessing location and contacts. Further analysis uncovered that after acquiring this data, the malware transmits it to the Telegram API via authenticated HTTP requests using specific tokens and chat_ids. These findings highlight that the permissions requested by the malware are designed to clandestinely collect and export personal data, posing a severe threat to the privacy and security of Android users.

Dynamic Malware Analysis Using Machine Learning-Based Detection Algorithms

With the increasing popularity of cell phone use, the risk of malware infections on such devices has increased, resulting in financial losses for both individuals and organizations. Current research focuses on the application of machine learning for the detection and classification of these malware programs. Accordingly, the present work uses the frequency of system calls to detect and classify malware using the XGBoost, LightGBM and random forest algorithms. The highest results were obtained with the LightGBM algorithm, achieving 94,1 % precision and 93,9 % accuracy, recall, and F1-score, demonstrating the effectiveness of both machine learning and dynamic malware analysis in mitigating security threats on mobile devices.

Analyzing Threat Level of the Backdoor Attack Method for an Organization’s Operation

Backdoor attacks played a critical part in the catastrophe, as well as the overall impact of cyberattacks. Backdoor assaults are additionally influencing the landscape of malware and threats, forcing companies to concentrate more on detecting and establishing vulnerability tactics in order to avoid hostile backdoor threats. Despite advances in cybersecurity systems, backdoor assaults remain a source of concern because of their propensity to remain undetected long after the attack vector has been started. This research is aimed to examine the threats of backdoor attack methods in an organization's operational network, provide a full-scale review, and serve as direction for training and defensive measures. The fundamental inspiration was drawn from the alarming and involving threat in cybersecurity, which necessitates a better awareness of the level of risk and the concurrent requirement for increased security measures. Most traditional security solutions usually fail to detect harmful backdoors due to the stealthy nature of backdoor code within the system, necessitating a unique approach to full-scale threat analysis. A multi-phase approach that begins with considerable reading and examination of existing literature to get insight into typical backdoor attack methodologies and application methods. Following analysis, testing was carried out in a virtual lab in a controlled environment because thorough malware analysis testing must adhere to ethical and legal cyber testing laws to avoid any penalties or foolish breaches. This methodology also included testing on numerous attack channels combined with backdoor attacks, such as detecting software vulnerabilities, phishing emails, and direct payload injection, to determine the complexity of the different attack vectors. Each of the collected data is utilized to create a threat model that predicts the amount of risk associated with the backdoor attack approach. The finding contributes to the development of more resilient defence mechanisms, while also strengthening the overall organization's security architecture and protocols.

A Novel Static Analysis Approach Using System Calls for Linux IoT Malware Detection

The proliferation of Internet of Things (IoT) devices on Linux platforms has heightened concerns regarding vulnerability to malware attacks. This paper introduces a novel approach to investigating the behavior of Linux IoT malware by examining syscalls and library syscall wrappers extracted through static analysis of binaries, as opposed to the conventional method of using dynamic analysis for syscall extraction. We rank and categorize Linux system calls based on their security significance, focusing on understanding malware intent without execution. Feature analysis of the assigned syscall categories and risk ranking is conducted with statistical tests to validate their effectiveness and reliability in differentiating between malware and benign binaries. Our findings demonstrate that potential threats can be reliably identified with an F1 score of 96.86%, solely by analyzing syscalls and library syscall wrappers. This method can augment traditional static analysis, providing an effective preemptive measure to enhance Linux malware analysis. This research highlights the importance of static analysis in strengthening IoT systems against emerging malware threats.

SINNER: A Reward-Sensitive Algorithm for Imbalanced Malware Classification Using Neural Networks with Experience Replay

Reports produced by popular malware analysis services showed a disparity in samples available for different malware families. The unequal distribution between such classes can be attributed to several factors, such as technological advances and the application domain that seeks to infect a computer virus. Recent studies have demonstrated the effectiveness of deep learning (DL) algorithms when learning multi-class classification tasks using imbalanced datasets. This can be achieved by updating the learning function such that correct and incorrect predictions performed on the minority class are more rewarded or penalized, respectively. This procedure can be logically implemented by leveraging the deep reinforcement learning (DRL) paradigm through a proper formulation of the Markov decision process (MDP). This paper proposes SINNER, i.e., a DRL-based multi-class classifier that approaches the data imbalance problem at the algorithmic level by exploiting a redesigned reward function, which modifies the traditional MDP model used to learn this task. Based on the experimental results, the proposed formula appears to be successful. In addition, SINNER has been compared to several DL-based models that can handle class skew without relying on data-level techniques. Using three out of four datasets sourced from the existing literature, the proposed model achieved state-of-the-art classification performance.

Oblivion: an open-source system for large-scale analysis of macro-based office malware

AbstractMacro-based Office files have been extensively used as infection vectors to embed malware. In particular, VBA macros allow leveraging kernel functions and system routines to execute or remotely drop malicious payloads, and they are typically heavily obfuscated to make static analysis unfeasible. Current state-of-the-art approaches focus on discriminating between malicious and benign Office files by performing static and dynamic analysis directly on obfuscated macros, focusing mainly on detection rather than reversing. Namely, the proposed methods lack an in-depth analysis of the embedded macros, thus losing valuable information about the attack families, the embedded scripts, and the contacted external resources. In this paper, we propose Oblivion, an open-source framework for large-scale analysis of Office macros, to fill in this gap. Oblivion performs instrumentation of macros and executes them in a virtualized environment to de-obfuscate and reconstruct their behavior. Moreover, it can automatically and quickly interact with macros by extracting the embedded PowerShell and non-PowerShell attacks and reconstructing the whole macro behavior. This is the main scope of our analysis: we are more interested in retrieving specific behavioural patterns than detecting maliciousness per se. We performed a large-scale analysis of more than 30,000 files that constitute a representative corpus of attacks. Results show that Oblivion could efficiently de-obfuscate malicious macros by revealing a large corpus of PowerShell and non-PowerShell attacks. We measured that this efficiency can be quantified in an analysis time of less than 1 min per sample, on average. Moreover, we characterize such attacks by pointing out frequent attack patterns and employed obfuscation strategies. We finally release the information obtained from our dataset with our tool.

TLS key material identification and extraction in memory: Current state and future challenges

Memory forensics is a crucial part of digital forensics as it can be used to extract valuable information such as running processes, network connections, and encryption keys from memory. The last is especially important when considering the widely used Transport Layer Security (TLS) protocol used to secure internet communication, thus hampering network traffic analysis. Particularly in the context of cybercrime investigations (such as malware analysis), it is therefore paramount for investigators to decrypt TLS traffic. This can provide vital insights into the methods and strategies employed by attackers. For this purpose, it is first and foremost necessary to identify and extract the corresponding TLS key material in memory.In this paper, we systematize and evaluate the current state of techniques, tools, and methodologies for identifying and extracting TLS key material in memory. We consider solutions from academia but also identify innovative and promising approaches used “in the wild” that are not considered by the academic literature. Furthermore, we identify the open research challenges and opportunities for future research in this domain. Our work provides a profound foundation for future research in this crucial area.

In the time loop: Data remanence in main memory of virtual machines

Data remanence in the physical memory of computers, i.e., the fact that data remains temporarily in memory even after power is cut, is a well-known issue which can be exploited for recovering cryptographic keys and other data in forensic investigations. Since virtual machines in many aspects mimic their physical counterparts, we investigate whether data remanence is also observable in virtual machines. Using KVM as an example of virtualization technology, we experimentally show that it is common for a substantial amount of volatile data to remain in the memory of virtual machines after a reboot. In digital forensic analysis scenarios such as malware analysis using virtual machines, our observations imply high risks of evidence contamination if no precautions are taken. So while the symptoms of data remanence in virtual machines are similar to physical machines, the implications for digital forensic analysis appear very different.

A lightweight deep learning-based android malware detection framework

Android, as the most prevalent mobile operating system (OS) in recent years, has been widely applied in various cell phones, tablets, and embedded devices, greatly facilitating people’s lives. However, Android malware is also emerging, which significantly endangers people’s information security. The current Android malware detection systems are often suffering from cumbersome structures and massive computational resources, which seriously limits their direct deployment on mobile devices. We design a lightweight Android malware detection system named MCADS, which consists of a two-layer structure. At the first layer, we use an augmented Multilayer Perceptron (MLP) for the preliminary analysis of malware. At the second layer, we propose a new lightweight variant of Convolutional Neural Network (CNN) to further analyze the Apps that cannot be accurately identified in the first layer. Finally, a careful experimental investigation has been conducted. The proposed method achieves an accuracy of 98.12%, surpassing that of the compared methods. The promising results demonstrate the potential of MCADS as a practical adjunctive solution for detecting malware, complementing existing complex detection methods.

Reducing Malware Analysis Overhead With Coverings

Reducing Malware Analysis Overhead With Coverings

Ransomware Detection and Prevention Using Machine Learning and Honeypots: A Short Review

Ransomware is a type of computer malware that is currently widespread and highly dangerous. Ransomware attacks have become a major cybersecurity risk, posing significant risks to individuals and organizations alike. Also, traditional techniques for malware analysis, such as signature matching and heuristics, are no longer viable due to the exponential growth of malware. Researchers have explored various approaches to address this issue, but there is a lack of proper documentation and comparison of existing works. This paper presents an analysis of ransomware detection systems, which is part of an ongoing research project aiming to develop an open-source ransomware detection system to address the identified gaps in the field. To prevent such attacks, it is recommended to regularly backup files and avoid clicking on untrusted email links and attachments. Machine learning has been suggested by researchers as a more effective method of detecting malware. With internet use on the rise, honeypots offer a viable option for reducing security threats and safeguarding classified data from hackers. Honeypots are regarded as valuable resources for thwarting attacks and giving important insight about the origin and behavior of such attacks, which is useful for analysts who conduct such investigations. This paper provides a general view of cybersecurity, machine learning (ML), cyber threats, and honeypot systems towards mitigating system attacks and understanding their origin and behavior. Index Terms— Cybersecurity, Ransomware, Honeypots, Machine Learning, Detection.