Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Learning from Structured Data with High Dimensional Structured Input and Output Domain

Structured data is accumulated rapidly in many applications, e.g. Bioinformatics, Cheminformatics, social network analysis, natural language processing and text mining. Designing and analyzing algorithms for handling these large collections of structured data has received significant interests in data mining and machine learning communities, both in the input and output domain. However, it is nontrivial to adopt traditional machine learning algorithms, e.g. SVM, linear regression to structured data. For one thing, the structural information in the input domain and output domain is ignored if applying the normal algorithms to structured data. For another, the major challenge in learning from many high-dimensional structured data is that input/output domain can contain tens of thousands even larger number of features and labels. With the high dimensional structured input space and/or structured output space, learning a low dimensional and consistent structured predictive function is important for both robustness and interpretability of the model. In this dissertation, we will present a few machine learning models that learn from the data with structured input features and structured output tasks. For learning from the data with structured input features, I have developed structured sparse boosting for graph classification, structured joint sparse PCA for anomaly detection and localization. Besides learning from structured input, I also investigated the interplay between structured input and output under the context of multi-task learning. In particular, I designed a multi-task learning algorithms that performs structured feature selection & task relationship Inference. We will demonstrate the applications of these structured models on subgraph based graph classification, networked data stream anomaly detection/localization, multiple cancer type prediction, neuron activity prediction and social behavior prediction. Finally, through my intern work at IBM T.J. Watson Research, I will demonstrate how to leverage structural information from mobile data (e.g. call detail record and GPS data) to derive important places from people's daily life for transit optimization and urban planning

Cyber Data Anomaly Detection Using Autoencoder Neural Networks

The Department of Defense requires a secure presence in the cyber domain to successfully execute its stated mission of deterring war and protecting the security of the United States. With potentially millions of logged network events occurring on defended networks daily, a limited staff of cyber analysts require the capability to identify novel network actions for security adjudication. The detection methodology proposed uses an autoencoder neural network optimized via design of experiments for the identification of anomalous network events. Once trained, each logged network event is analyzed by the neural network and assigned an outlier score. The network events with the largest outlier scores are anomalous and worthy of further review by cyber analysts. This neural network approach can operate in conjunction with alternate tools for outlier detection, enhancing the overall anomaly detection capability of cyber analysts

Formalizing evasion attacks against machine learning security detectors

Recent work has shown that adversarial examples can bypass machine learning-based threat detectors relying on static analysis by applying minimal perturbations. To preserve malicious functionality, previous attacks either apply trivial manipulations (e.g. padding), potentially limiting their effectiveness, or require running computationally-demanding validation steps to discard adversarial variants that do not correctly execute in sandbox environments. While machine learning systems for detecting SQL injections have been proposed in the literature, no attacks have been tested against the proposed solutions to assess the effectiveness and robustness of these methods. In this thesis, we overcome these limitations by developing RAMEn, a unifying framework that (i) can express attacks for different domains, (ii) generalizes previous attacks against machine learning models, and (iii) uses functions that preserve the functionality of manipulated objects. We provide new attacks for both Windows malware and SQL injection detection scenarios by exploiting the format used for representing these objects. To show the efficacy of RAMEn, we provide experimental results of our strategies in both white-box and black-box settings. The white-box attacks against Windows malware detectors show that it takes only the 2% of the input size of the target to evade detection with ease. To further speed up the black-box attacks, we overcome the issues mentioned before by presenting a novel family of black-box attacks that are both query-efficient and functionality-preserving, as they rely on the injection of benign content, which will never be executed, either at the end of the malicious file, or within some newly-created sections, encoded in an algorithm called GAMMA. We also evaluate whether GAMMA transfers to other commercial antivirus solutions, and surprisingly find that it can evade many commercial antivirus engines. For evading SQLi detectors, we create WAF-A-MoLE, a mutational fuzzer that that exploits random mutations of the input samples, keeping alive only the most promising ones. WAF-A-MoLE is capable of defeating detectors built with different architectures by using the novel practical manipulations we have proposed. To facilitate reproducibility and future work, we open-source our framework and corresponding attack implementations. We conclude by discussing the limitations of current machine learning-based malware detectors, along with potential mitigation strategies based on embedding domain knowledge coming from subject-matter experts naturally into the learning process

Challenges and Open Questions of Machine Learning in Computer Security

This habilitation thesis presents advancements in machine learning for computer security, arising from problems in network intrusion detection and steganography. The thesis put an emphasis on explanation of traits shared by steganalysis, network intrusion detection, and other security domains, which makes these domains different from computer vision, speech recognition, and other fields where machine learning is typically studied. Then, the thesis presents methods developed to at least partially solve the identified problems with an overall goal to make machine learning based intrusion detection system viable. Most of them are general in the sense that they can be used outside intrusion detection and steganalysis on problems with similar constraints. A common feature of all methods is that they are generally simple, yet surprisingly effective. According to large-scale experiments they almost always improve the prior art, which is likely caused by being tailored to security problems and designed for large volumes of data. Specifically, the thesis addresses following problems: anomaly detection with low computational and memory complexity such that efficient processing of large data is possible; multiple-instance anomaly detection improving signal-to-noise ration by classifying larger group of samples; supervised classification of tree-structured data simplifying their encoding in neural networks; clustering of structured data; supervised training with the emphasis on the precision in top p% of returned data; and finally explanation of anomalies to help humans understand the nature of anomaly and speed-up their decision. Many algorithms and method presented in this thesis are deployed in the real intrusion detection system protecting millions of computers around the globe

Uncovering the Potential of Federated Learning: Addressing Algorithmic and Data-driven Challenges under Privacy Restrictions

Federated learning is a groundbreaking distributed machine learning paradigm that allows for the collaborative training of models across various entities without directly sharing sensitive data, ensuring privacy and robustness. This Ph.D. dissertation delves into the intricacies of federated learning, investigating the algorithmic and data-driven challenges of deep learning models in the presence of additive noise in this framework. The main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. A key contribution of this study is introducing specific metrics for training with noise. Signal-to-Noise Ratio (SNR) is introduced as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion mechanisms to enhance privacy without compromising performance. This research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. The present study also explores a real-world application of federated learning in weather prediction applications that suffer from the issue of imbalanced datasets. Utilizing data from multiple sources combined with advanced data augmentation techniques improves the accuracy and generalization of weather prediction models, even when dealing with imbalanced datasets. Overall, federated learning is pivotal in harnessing decentralized datasets for real-world applications while safeguarding privacy. By leveraging noise as a tool for regularization and privacy enhancement, this research study aims to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy

Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations