Temporal Analysis of Adversarial Attacks in Federated Learning

In this paper, we experimentally analyze the robustness of selected Federated Learning (FL) systems in the presence of adversarial clients. We find that temporal attacks significantly affect model performance in the FL models tested, especially when the adversaries are active throughout or during the later rounds. We consider a variety of classic learning models, including Multinominal Logistic Regression (MLR), Random Forest, XGBoost, Support Vector Classifier (SVC), as well as various Neural Network models including Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our results highlight the effectiveness of temporal attacks and the need to develop strategies to make the FL process more robust against such attacks. We also briefly consider the effectiveness of defense mechanisms, including outlier detection in the aggregation algorithm.

Multimodal Techniques for Malware Classification

The threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with multimodal machine learning approaches for malware classification, based on the structured nature of the Windows Portable Executable (PE) file format. Specifically, we train Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models on features extracted from PE headers, we train these same models on features extracted from the other sections of PE files, and train each model on features extracted from the entire PE file. We then train SVM models on each of the nine header-sections combinations of these baseline models, using the output layer probabilities of the component models as feature vectors. We compare the baseline cases to these multimodal combinations. In our experiments, we find that the best of the multimodal models outperforms the best of the baseline cases, indicating that it can be advantageous to train separate models on distinct parts of Windows PE files.

Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network

The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of advanced machine learning techniques. In this research, we present a novel approach based on a hybrid architecture combining features extracted using a Hidden Markov Model (HMM), with a Convolutional Neural Network (CNN) then used for malware classification. Inspired by the strong results in previous work using an HMM-Random Forest model, we propose integrating HMMs, which serve to capture sequential patterns in opcode sequences, with CNNs, which are adept at extracting hierarchical features. We demonstrate the effectiveness of our approach on the popular Malicia dataset, and we obtain superior performance, as compared to other machine learning methods -- our results surpass the aforementioned HMM-Random Forest model. Our findings underscore the potential of hybrid HMM-CNN architectures in bolstering malware classification capabilities, offering several promising avenues for further research in the field of cybersecurity.

An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack

In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random Forest, XGBoost, and Long Short-Term Memory (LSTM). For each model, we simulate label-flipping attacks, experimenting extensively with 10 federated clients and 100 federated clients. We vary the percentage of adversarial clients from 10% to 100% and, simultaneously, the percentage of labels flipped by each adversarial client is also varied from 10% to 100%. Among other results, we find that models differ in their inherent robustness to the two vectors in our label-flipping attack, i.e., the percentage of adversarial clients, and the percentage of labels flipped by each adversarial client. We discuss the potential practical implications of our results.

Image-Based Malware Classification Using QR and Aztec Codes

In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as Convolutional Neural Networks (CNN) in classifying images derived from executable files. In this paper, we consider an innovative method that relies on an image conversion process that consists of transforming features extracted from executable files into QR and Aztec codes. These codes capture structural patterns in a format that may enhance the learning capabilities of CNNs. We design and implement CNN architectures tailored to the unique properties of these codes and apply them to a comprehensive analysis involving two extensive malware datasets, both of which include a significant corpus of benign samples. Our results yield a split decision, with CNNs trained on QR and Aztec codes outperforming the state of the art on one of the datasets, but underperforming more typical techniques on the other dataset. These results indicate that the use of QR and Aztec codes as a form of feature engineering holds considerable promise in the malware domain, and that additional research is needed to better understand the relative strengths and weaknesses of such an approach.

XAI and Android Malware Models

Android malware detection based on machine learning (ML) and deep learning (DL) models is widely used for mobile device security. Such models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such learning models make decisions. As a result, these popular malware detection strategies are generally treated as black boxes, which can result in a lack of trust in the decisions made, as well as making adversarial attacks more difficult to detect. The field of eXplainable Artificial Intelligence (XAI) attempts to shed light on such black box models. In this paper, we apply XAI techniques to ML and DL models that have been trained on a challenging Android malware classification problem. Specifically, the classic ML models considered are Support Vector Machines (SVM), Random Forest, and $k$-Nearest Neighbors ($k$-NN), while the DL models we consider are Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). The state-of-the-art XAI techniques that we apply to these trained models are Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), PDP plots, ELI5, and Class Activation Mapping (CAM). We obtain global and local explanation results, and we discuss the utility of XAI techniques in this problem domain. We also provide a literature review of XAI work related to Android malware.

Distinguishing Chatbot from Human

There have been many recent advances in the fields of generative Artificial Intelligence (AI) and Large Language Models (LLM), with the Generative Pre-trained Transformer (GPT) model being a leading "chatbot." LLM-based chatbots have become so powerful that it may seem difficult to differentiate between human-written and machine-generated text. To analyze this problem, we have developed a new dataset consisting of more than 750,000 human-written paragraphs, with a corresponding chatbot-generated paragraph for each. Based on this dataset, we apply Machine Learning (ML) techniques to determine the origin of text (human or chatbot). Specifically, we consider two methodologies for tackling this issue: feature analysis and embeddings. Our feature analysis approach involves extracting a collection of features from the text for classification. We also explore the use of contextual embeddings and transformer-based architectures to train classification models. Our proposed solutions offer high classification accuracy and serve as useful tools for textual analysis, resulting in a better understanding of chatbot-generated text in this era of advanced AI technology.

Online Clustering of Known and Emerging Malware Families

Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information security. Due to the large amount of malware samples available, it is essential to categorize malware samples according to their malicious characteristics. Clustering algorithms are thus becoming more widely used in computer security to analyze the behavior of malware variants and discover new malware families. Online clustering algorithms help us to understand malware behavior and produce a quicker response to new threats. This paper introduces a novel machine learning-based model for the online clustering of malicious samples into malware families. Streaming data is divided according to the clustering decision rule into samples from known and new emerging malware families. The streaming data is classified using the weighted k-nearest neighbor classifier into known families, and the online k-means algorithm clusters the remaining streaming data and achieves a purity of clusters from 90.20% for four clusters to 93.34% for ten clusters. This work is based on static analysis of portable executable files for the Windows operating system. Experimental results indicate that the proposed online clustering model can create high-purity clusters corresponding to malware families. This allows malware analysts to receive similar malware samples, speeding up their analysis.

Feature Analysis of Encrypted Malicious Traffic

In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and firewalls typically will not have access to encryption keys, and therefore direct detection of malicious encrypted data is unlikely to succeed. However, previous work has shown that traffic analysis can provide indications of malicious intent, even in cases where the underlying data remains encrypted. In this paper, we apply three machine learning techniques to the problem of distinguishing malicious encrypted HTTP traffic from benign encrypted traffic and obtain results comparable to previous work. We then consider the problem of feature analysis in some detail. Previous work has often relied on human expertise to determine the most useful and informative features in this problem domain. We demonstrate that such feature-related information can be obtained directly from machine learning models themselves. We argue that such a machine learning based approach to feature analysis is preferable, as it is more reliable, and we can, for example, uncover relatively unintuitive interactions between features.

Social Media Bot Detection using Dropout-GAN

Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. In terms of classification accuracy, our approach outperforms the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification technique.