Adversarial Machine Learning for the Protection of Legitimate Software

Obfuscation is the transforming a given program into one that is syntactically different but semantically equivalent. This new obfuscated program now has its code and/or data changed so that they are hidden and difficult for attackers to understand. Obfuscation is an important security tool and used to defend against reverse engineering. When applied to a program, different transformations can be observed to exhibit differing degrees of complexity and changes to the program. Recent work has shown, by studying these side effects, one can associate patterns with different transformations. By taking this into account and attempting to profile these unique side effects, it is possible to create a classifier using machine learning which can analyze transformed software and identifies what transformation was used to put it in its current state. This has the effect of weakening the security of obfuscating transformations used to protect legitimate software. In this research, we explore options to increase the robustness of obfuscation against attackers who utilize machine learning, particular those who use it to identify the type of obfuscation being employed. To accomplish this, we segment our research into three stages. For the first stage, we implement a suite of classifiers that are used to xiv identify the obfuscation used in samples. These establish a baseline for determining the effectiveness of our proposed defenses and make use of three varied feature sets. For the second stage, we explore methods to evade detection by the classifiers. To accomplish this, attacks setup using the principles of adversarial machine learning are carried out as evasion attacks. These attacks take an obfuscated program and make subtle changes to various aspects that will cause it to be mislabeled by the classifiers. The changes made to the programs affect features looked at by our classifiers, focusing mainly on the number and distribution of opcodes within the program. A constraint of these changes is that the program remains semantically unchanged. In addition, we explore a means of algorithmic dead code insertion in to achieve comparable results against a broader range of classifiers. In the third stage, we combine our attack strategies and evaluate the effect of our changes on the strength of obfuscating transformations. We also propose a framework to implement and automate these and other measures. We the following contributions: 1. An evaluation of the effectiveness of supervised learning models at labeling obfuscated transformations. We create these models using three unique feature sets: Code Images, Opcode N-grams, and Gadgets. 2. Demonstration of two approaches to algorithmic dummy code insertion designed to improve the stealth of obfuscating transformations against machine learning: Adversarial Obfuscation and Opcode Expansion 3. A unified version of our two defenses capable of achieving effectiveness against a broad range of classifiers, while also demonstrating its impact on obfuscation metrics

Obfuscated computer virus detection using machine learning algorithm

Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the traditional signature based detection method and investigated the use of machine learning technique for obfuscated computer virus detection. In this work, text strings are used and have been extracted from virus program codes as the features to generate a suitable classifier model that can correctly classify obfuscated virus files. Text string feature is used as it is informative and potentially only use small amount of memory space. Results show that unknown files can be correctly classified with 99.5% accuracy using SMO classifier model. Thus, it is believed that current computer virus defense can be strengthening through machine learning approach

Eigenvalue Analysis for Metamorphic Detection

Metamorphic viruses change their structure on each infection while maintaining their function. Although many detection techniques have been proposed, practical and effective metamorphic detection remains a difficult challenge. In this project, we analyze a novel method for detecting metamorphic viruses. Our approach was inspired by a well-known facial recognition technique that is based on eigenvalue analysis. We compute eigenvectors using opcode sequences extracted from a set of known metamorphic viruses. These eigenvectors can then be used to score a given executable file, based on its extracted opcode sequence. We perform extensive testing to determine the effectiveness of this scoring technique for classifying metamorphic malware. Our results show that this approach yields very good results when applied to highly metamorphic malware

Effective methods to detect metamorphic malware: A systematic review

The succeeding code for metamorphic Malware is routinely rewritten to remain stealthy and undetected within infected environments. This characteristic is maintained by means of encryption and decryption methods, obfuscation through garbage code insertion, code transformation and registry modification which makes detection very challenging. The main objective of this study is to contribute an evidence-based narrative demonstrating the effectiveness of recent proposals. Sixteen primary studies were included in this analysis based on a pre-defined protocol. The majority of the reviewed detection methods used Opcode, Control Flow Graph (CFG) and API Call Graph. Key challenges facing the detection of metamorphic malware include code obfuscation, lack of dynamic capabilities to analyse code and application difficulty. Methods were further analysed on the basis of their approach, limitation, empirical evidence and key parameters such as dataset, Detection Rate (DR) and False Positive Rate (FPR)

Application of Adversarial Attacks on Malware Detection Models

Malware detection is vital as it ensures that a computer is safe from any kind of malicious software that puts users at risk. Too many variants of these malicious software are being introduced everyday at increased speed. Thus, to guarantee security of computer systems, huge advancements in the field of malware detection are made and one such approach is to use machine learning for malware detection. Even though machine learning is very powerful, it is prone to adversarial attacks. In this project, we will try to apply adversarial attacks on malware detection models. To perform these attacks, fake samples that are generated using Generative Adversarial Networks (GAN) algorithm are used and these fake malware data along with the actual data is given to a machine learning model for malware detection. Here, we will also be experimenting with the percentage of fake malware samples to be considered and observe the behavior of the model according to the given input. The novelty of this project is given by the use of adversarial samples that are generated by the implementation of word embeddings produced by our generative algorithms

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network

Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method

GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph