Intelligent robust malware detection by implementing deep learning

A literature survey shows that the number of malware attacks is gradually growing over the years due to the growing trend of Internet of Medical Things (IoMT) devices. To detect and classify malware attacks, automated malware detection and classification is an essential subsystem in healthcare cyber-physical systems. This work proposes an attention-based multidimensional deep learning (DL) approach for a cross-architecture IoMT malware detection and classification system based on byte sequences extracted from Executable and Linkable Format (ELF; formerly named Extensible Linking Format) files. The DL approach automates the feature design and extraction process from unstructured byte sequences. In addition, the proposed approach facilitates the detection of the central processing unit (CPU) architecture of the ELF file. A detailed experimental analysis and its evaluation are shown on the IoMT cross-architecture benchmark dataset. In all the experiments, the proposed method showed better performance compared with those obtained from several existing methods with an accuracy of 95% for IoMT malware detection, 94% for IoMT malware classification, and 95% for CPU architectures classification. The proposed method also suggests a similar performance with an accuracy of 94% on the Microsoft malware dataset. Experimental results on two malware datasets indicate that the proposed method is robust and generalizable in cross-architecture IoMT malware detection, classification, and CPU architectures classification in healthcare cyber-physical systems.

Malicious software also known as "Malware" is software that uses legitimate instructions or code to perform malicious actions. Malware poses a major threat for computer security and information security in general. Over the years, malware has evolved to the point that a single malware specimen can have hundreds or maybe thousands of variants using polymorphic and metamorphic transformation to change the signature of the malware variant in propagation. The common signature-based malware detection methods are no longer robust to detect these variants due to the alteration of code. Static analysis is required to obtain these signatures and anti-virus companies are required to propagate these signature updates to their software. A faster detection method is needed to compensate the exponentially increasing number of malware variants. Machine learning is a trending approach for malware detection. This removes the need to use signature-based detection and is also faster. Software companies do not need to propagate signatures as often. Machine learning algorithms using opcode sequences can recognise patterns in the malicious code that are not present in common signatures and classify them more efficiently. Therefore, a machine learning approach for malware detection should be adopted for faster and more efficient detection. Most research in malware detection using machine learning used static attributes such as network connections, processes spawned, hashes, etc., that were not that robust to changes. In this paper we introduced our novel approach in using trigrams and PE file attributes as features for malware detection. We took a text mining approach to make our detection method more robust to polymorphism and metamorphism. The instruction sequence for critical code in malware on the assembly level is basically the same across malware families. We used opcode trigram sequences as the main feature for our machine learning algorithm. We used Support Vector Machine(SVM) as our classifying algorithm which is a discriminative classifier model that gives a definite decision whether the predicted outcome belongs to the learned class or not. The above shows our novel approach that enabled us to get higher detection rates with less features.

Malicious software (malware) assaults on computers are becoming more common, and this is a huge security risk in the modern digital world. Malware detection is a popular area of study because of the widespread impact that these assaults are having on individuals, businesses, and governments. The malware signatures and behaviour patterns used by existing malware detection technologies are time-consuming and have been shown to be unsuccessful at detecting unidentified malwares. Modern malware use polymorphic and metamorphic methods, among others, to rapidly evolve and produce a plethora of variants. Machine learning procedures (MLAs) have lately been used to effectively analyse new malware, the vast majority of which are versions of old malware. Traditional machine learning methods, which involve training a classifier with manually generated features, are likewise insufficiently powerful against these evasive strategies and incur additional work from feature engineering. In this research, we explore a mechanism for protecting the (differential) privacy of the GAN generator in order to address these concerns. The resultant model may be used to generate synthetic data for the purposes of training and validating algorithms and holding contests without compromising the confidentiality of the original dataset. In order to deal with GANs, the technique adapts the Private Aggregation of Teacher Ensembles (PATE) architecture. In this work, we use the War Strategy Algorithm (WSA) to optimise the GAN model’s weight, and as a result, the modified framework (which we refer to as PATE-WSA-GAN) allows us to competing replicas.

Malware is a major threat to the computer security. There are so many variants of malware evolving each day which results the loss in economy as well as data and personal information is precious than economy. Malware prevention can be achieved by carefully analyzing the content of the EULA. As EULA analysis deals with analyzing the unstructured text documents, text mining concept plays an important role. This paper analyzes the detection mechanism for malware detection, using text mining over an ample set of existing EULA. The comparison presented in this paper highlights some malware analysis and text mining techniques. Although there are several malware detection systems exist, evolution and revolution is required to fill the gap in malware evolution and detection. In order to achieve the goal or to verify the authenticity of an executable, this paper provides an alternate approach to detect the malware before execution. EULA analysis which acts as gateway can be included in other behavioral or data mining based malware detection system to achieve both external and internal security of the computing system including mobile devices.

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

Trust-based Voting Method for Efficient Malware Detection

Due to the increase and frequency of network attacks, network security protection needs to be established[1]. The project focuses on using machine learning technology to detect ransomware and malware and aims to improve computer security. This work involves using advanced algorithms and models to analyze different types of data to identify patterns associated with crime. The proposed system extracts relevant features from various data points, including archive behavior, network traffic, and physical interactions. These features are used to train machine learning models to distinguish between good behavior and bad behavior. By constantly learning and updating, the model improves accuracy over time and stays ahead of evolving ransomware and malware threats. This project aims to contribute to the field of cybersecurity by providing efficient and effective methods to detect and mitigate threats. The use of machine learning-based search engines should improve defense against cyber attacks, ultimately protecting sensitive data and ensuring the integrity of computer systems[2]. This research aligns with the growing need for cybersecurity in response to new solutions to combat the evolution of malware. Key Words: Cybersecurity, Ransomware detection, Malware detection, Machine learning techniques, Advanced algorithms, Data analysis, Feature extraction, Model training, Continuous learning, Threat detection, Defence mechanisms

The increasing incidence of malware presents significant obstacles to cybersecurity, necessitating sophisticated and effective techniques for detection and categorization. This paper presents a novel method that improves the precision and efficacy of malware classification by utilizing multi-processing and Bag-of-Words (BoW) vectorization. The suggested technique utilizes feature vectors to depict malware samples, leveraging the parallel processing capabilities of contemporary computer systems to expedite classification operations. This solution centers on a bespoke HexVectorizer that transforms hexadecimal strings obtained from malware binaries into feature vectors. The approach employs a balanced subset of the Microsoft Malware Classification dataset, meticulously preprocessed for dependable evaluation to assure thorough analysis. Python's multiprocessing module is utilized to meet the computing requirements of extensive datasets, facilitating the parallelization of vectorization processes and markedly enhancing processing performance. The classification system is centered on XGBoost's XGBClassifier, recognized for its superior performance and accuracy in addressing malware detection and classification issues. The experimental findings validate the efficacy of the suggested technique in real-time malware detection, confirming its relevance to diverse cybersecurity contexts. This paper offers an exhaustive elucidation of the implementation procedure, accompanied by thorough performance assessments. The results highlight the method's promise as a scalable and efficient approach for tackling the increasing issues of malware classification in cybersecurity. This research greatly advances the creation of resilient and efficient malware detection systems by integrating sophisticated vectorization techniques with cutting-edge machine learning algorithms, therefore fulfilling essential requirements in a dynamic threat environment.

Android OS is today the most used Operating System for mobile devices. However, it is susceptible to several malware attacks that may seriously compromise the privacy and security of individuals and organizations. This paper proposes an approach based on a static analysis of decompiled Android PacKages (APKs) to extract critical APIs and detect Android malware. The main contributions lie in the adoption of a graph-based data engineering schema to represent APIs taken from the Function Call Graphs of decompiled APKs and the formulation of a graph-based deep learning approach for explainable malware detection. In particular, the proposed approach, named , implements a Graph Neural Network (GNN) for binary classification (malware versus goodware), and integrates algorithm to disclose how specific API classes and control-flow edges between API calls influence malware alerts. The proposed approach was evaluated by considering 26,527 Android APKs. The results of an extensive and in-depth evaluation show that the presented GNN model achieves higher accuracy than deep neural models trained with traditional API call sequence representations and publicly available related methods. On the other hand, it produces decision explanations that yield interesting insights into the malicious patterns of APKs and support root cause analysis of missed malware alarms.

ABSTRACTWith the development of information technology, malicious software attack methods are becoming increasingly sophisticated, posing a serious threat to network security. Aiming at the problems of existing malware detection technologies, such as low detection rate, high false alarm rate, and poor real‐time performance, this paper proposes a malware detection and defense algorithm based on a hybrid convolutional graph neural network. This algorithm combines the advantages of CNN and GNN to effectively identify malicious behavior by extracting deep‐seated malware features. The experimental results show our algorithm has high accuracy and real‐time performance in malware detection. We employed publicly available malware datasets during the experiment for training and testing. After comparative experiments, the detection rate reaches 95.2%, which is 8.6% higher than the CNN algorithm and 5.3% higher than that of the GNN algorithm. In terms of false alarm rate, the algorithm in this paper is only 1.2%, far lower than the traditional CNN algorithm's 5.8% and the GNN algorithm's 3.5%. At the same time, our algorithm also shows excellent performance in real‐time, and the average detection time is only 0.12 s, which is 20% lower than the traditional algorithm. In addition, to verify the defense capability of the algorithm, we simulate various malware attack scenarios in our experiments. The results show that our algorithm can effectively identify and defend against 90.6% of malware attacks, which strongly supports network security protection.

With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make evasion harder. To better understand the properties of file-to-file relations, we construct the file co-existence graph. Resting on the constructed graph, we investigate the semantic relatedness among files, and leverage graph inference, active learning and graph representation learning for malware detection. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed learning paradigms. As machine learning-based detection systems become more widely deployed, the incentive for defeating them increases. Therefore, we go further insight into the arms race between adversarial malware attack and defense, and aim to enhance the security of machine learning-based malware detection systems. In particular, we first explore the adversarial attacks under different scenarios (i.e., different levels of knowledge the attackers might have about the targeted learning system), and define a general attack strategy to thoroughly assess the adversarial behaviors. Then, considering different skills and capabilities of the attackers, we propose the corresponding secure-learning paradigms to counter the adversarial attacks and enhance the security of the learning systems while not compromising the detection accuracy. We conduct a series of comprehensive experimental studies based on the real sample collections from Comodo Cloud Security Center and the promising results demonstrate the effectiveness of our proposed secure-learning models, which can be readily applied to other detection tasks.

Malicious software is posing a great challenge to Cybersecurity by causing huge damage to enterprises and users. Traditional signature-based techniques have found it challenging to detect malware as it has changed and evolved over time. Machine learning techniques have become popular for malware detection but they are based on heuristic feature engineering, which is expensive and requires domain expertise. In the recent years, deep learning, are being actively investigated for this crucial malware detection application to achieve robust solutions with higher accuracy. To assist in achieving this goal, the EfficientNet-B1 model pretrained on the ImageNet dataset with the last layer adapted to attain malware family classification is presented. The malware samples to be inputted are represented as byteplot grayscale images. The experimental results on a dataset comprising of 10,868 samples from 9 different malware families showed that the proposed approach can be effectively used to classify malware families. Classification accuracy of 98.57% on the held-out test set was obtained by the proposed model which outperformed the other pretrained deep learning models.

With the steady increase in the demand for Internet of Things (IoT) devices in diverse industries, such as manufacturing, medical care, and transportation infrastructure, the production of malware tailored for Smart IoT environments is also increasing. Accordingly, various malware detection studies are being conducted to detect not only known malware but also variant malware. However, it is difficult to detect malware transformed in a way that hides malicious behavior by changing and deleting bytes or modifying the assembly code. Therefore, in this study, we propose a malware detection for static security service (Mal3S) scheme that provides a secure Smart IoT environment by accurately detecting various types of malware. Mal3S extracts bytes, opcodes, API calls, strings, and dynamic link libraries (DLLs) through static analysis and then generates five types of images. Images of various sizes are trained on a multi spatial pyramid pooling network (SPP-net) model to detect malware. When evaluating the performance of Mal3S using three malware datasets, the average detection accuracy was 98.02% and the classification accuracy was 98.43%, showing better performance than existing malware detection techniques. In addition, Mal3S has demonstrated effective generalization capabilities for various types of malware.

This paper explores the use of Machine Learning (ML) techniques in enhancing malware detection amidst the evolving landscape of cyber threats. Traditional signature-based methods are becoming increasingly ineffective against sophisticated malware employing evasion tactics like polymorphism and metamorphism. Our study introduces a taxonomy of ML-based malware detection models, including supervised, unsupervised, and reinforcement learning approaches. We conduct an evaluation of these models on various datasets to determine their effectiveness in identifying malicious code. The paper discusses the strengths, weaknesses, and security concerns of these ML models, particularly their vulnerability to adversarial attacks. Among the techniques evaluated, basic supervised learning algorithms, such as Decision Trees and Random Forests, emerged as the most effective. These algorithms demonstrated superior performance in detecting known malware types, characterized by high accuracy, ease of implementation, and efficient processing times compared to more complex models. Our findings offer insights for developing more resilient and intelligent malware detection systems and address the challenges of integrating these models into real-world cybersecurity systems.

Sequential opcode embedding-based malware detection method