Image malware detection using deep learning

We are currently living in an area where artificial intelligence is making out every day to day life much easier to manage. Some researchers are continuously developing the codes of artificial intelligence to utilize the benefits of the human being. And there is the process called data mining, which is used in many domains, including finance, engineering, biomedicine, and cyber security. The utilization of data mining, artificial intelligence algorithms like deep learning is so vast that we can't even name them all. This technology has almost touched every industry and cyber security is the most beneficial. The process of enhancing cyber security with the help of deep learning methods has come out of the theory books and many organizations are utilizing them rather than using a traditional piece of software to defend against online threats. Especially in the field of recognizing and classifying codes or malware. And this is essential, because, with the advent of cloud computing and the Internet of Things, expand potential malware infection sites from PCs to any electronic device. This makes our day to day life very unsafe. In this post, first, we will describe in brief how deep learning can be the most useful and promising techniques to detect malware. Besides this we will go through a deep neural network,ResNet for malware dynamic behavior classification jobs

Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning

We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-grams (N=2) of bytes in the Discrete Cosine Transform (DCT) domain and a neural network is trained for malware detection. A shallow neural network is trained for classification, and its accuracy is compared with deep-network architectures such as ResNet that are trained using transfer learning. Neither dis-assembly nor behavioral analysis of malware is required for these methods. Motivated by the visual similarity of these images for different malware families, we compare our deep neural network models with standard image features like GIST descriptors to evaluate the performance. A joint feature measure is proposed to combine different features using error analysis to get an accurate ensemble model for improved classification performance. A new dataset called MaleX which contains around 1 million malware and benign Windows executable samples is created for large-scale malware detection and classification experiments. Experimental results are quite promising with 96% binary classification accuracy on MaleX. The proposed model is also able to generalize well on larger unseen malware samples and the results compare favorably with state-of-the-art static analysis-based malware detection algorithms

Malware Analysis with Machine Learning

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2022Malware attacks have been one of the most serious cyber risks in recent years. Almost every week, the number of vulnerability reports is increasing in the security communities. One of the key causes for the exponential growth is the fact that malware authors started introducing mutations to avoid detection. This means that malicious files from the same malware family, with the same malicious behaviour, are constantly modified or obfuscated using a variety of technics to make them appear to be different. Characteristics retrieved from raw binary files or disassembled code are used in existing machine learning-based malware categorization algorithms. The variety of such attributes has made it difficult to develop generic malware categorization methods that operate well in a variety of operating scenarios. To be effective in evaluating and categorizing such enormous volumes of data, it is necessary to divide them into groups and identify their respective families based on their behaviour. Malicious software is converted to a greyscale image representation, due to the possibility to capture subtle changes while keeping the global structure helps to detect variations. Motivated by the Machine Learning results achieved in the ImageNet challenge, this dissertation proposes an agnostic deep learning solution, for efficiently classifying malware into families based on a collection of discriminant patterns retrieved from its visualization as images. In this thesis, we present Malwizard, an adaptable Python solution suited for companies or end users, that allows them to automatically obtain a fast malware analysis. The solution was implemented as an Outlook add-in and an API service for the SOAR platforms, as emails are the first vector for this type of attack, with companies being the most attractive targets. The Microsoft Classification Challenge dataset was used in the evaluation of the noble approach. Therefore, its image representation was ciphered and generated the correspondent ciphered image to evaluate if the same patterns could be identified using traditional machine learning techniques. Thus, allowing the privacy concerns to be addressed, maintaining the data analysed by neural networks secure to unauthorized parties. Experimental comparison demonstrates the noble approach performed close to the best analysed model on a plain text dataset, completing the task in one-third of the time. Regarding the encrypted dataset, classical techniques need to be adapted in order to be efficient

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201

Deep Learning Models for Detecting Malware Attacks

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Deep learning (DL) is one of the emerging and promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on the Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are presented. We also present feature extraction approaches and a review of recent DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on malware detection including future directions to further advance knowledge and research in this field.Comment: Revised figures 2 and 3, revised title, remove typos page 1

Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using increasingly sophisticated techniques to breach security systems and steal sensitive data. In recent years, machine learning, deep learning, and transfer learning techniques have emerged as promising tools for predicting cybercrime and preventing it before it occurs. This paper aims to provide a comprehensive survey of the latest advancements in cybercrime prediction using above mentioned techniques, highlighting the latest research related to each approach. For this purpose, we reviewed more than 150 research articles and discussed around 50 most recent and relevant research articles. We start the review by discussing some common methods used by cyber criminals and then focus on the latest machine learning techniques and deep learning techniques, such as recurrent and convolutional neural networks, which were effective in detecting anomalous behavior and identifying potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset, and then focus on active and reinforcement Learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. Overall, this paper presents a holistic view of cutting-edge developments in cybercrime prediction, shedding light on the strengths and limitations of each method and equipping researchers and practitioners with essential insights, publicly available datasets, and resources necessary to develop efficient cybercrime prediction systems.Comment: 27 Pages, 6 Figures, 4 Table