928 research outputs found
Innovative machine learning techniques for security detection problems
University of Technology, Sydney. Faculty of Engineering and Information Technology.Most of the currently available network security techniques cannot cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing techniques for preventing intrusions such as encryption and firewalls, Intrusion Detection System (IDS) technology has established itself as an emerging field that is able to detect unauthorized access and abuse of computer systems from both internal users and external offenders. Most of the novel approaches in this field have adopted Artificial Intelligence (AI) technologies such as Artificial Neural Networks (ANN) to improve detection performance. The true power and advantage of ANN lie in its ability to represent both linear and non-linear underlying functions and learn these functions directly from the data being modeled. However, ANN is computationally expensive due to its demanding processing power and this leads to the overfitting problem, i.e. the network is unable to extrapolate accurately once the input is outside of the training data range. These limitations challenge security systems with low detection rate, high false alarm rate and excessive computation cost. In this research, a novel Machine Learning (ML) algorithm is developed to alleviate those difficulties of conventional detection techniques used in available IDS. By implementing Adaptive Boosting and Semi-parametric radial-basis-function neural networks, this model aims at minimizing learning bias (how well the model fits the available sample data) and generalization variance (how stable the model is for unseen instances) at an affordable cost of computation. The proposed method is applied to a set of Security Detection Problems which aim to detect security breaches within computer networks. In particular, we consider two benchmarking problems: intrusion detection and anti-spam filtering. It is empirically shown that our technique outperforms other state-of-the-art predictive algorithms in both of the problems, with significantly increased detection accuracy, minimal false alarms and relatively low computation
Generative AI for Space-Air-Ground Integrated Networks (SAGIN)
Recently, generative AI technologies have emerged as a significant
advancement in artificial intelligence field, renowned for their language and
image generation capabilities. Meantime, space-air-ground integrated network
(SAGIN) is an integral part of future B5G/6G for achieving ubiquitous
connectivity. Inspired by this, this article explores an integration of
generative AI in SAGIN, focusing on potential applications and case study. We
first provide a comprehensive review of SAGIN and generative AI models,
highlighting their capabilities and opportunities of their integration.
Benefiting from generative AI's ability to generate useful data and facilitate
advanced decision-making processes, it can be applied to various scenarios of
SAGIN. Accordingly, we present a concise survey on their integration, including
channel modeling and channel state information (CSI) estimation, joint
air-space-ground resource allocation, intelligent network deployment, semantic
communications, image extraction and processing, security and privacy
enhancement. Next, we propose a framework that utilizes a Generative Diffusion
Model (GDM) to construct channel information map to enhance quality of service
for SAGIN. Simulation results demonstrate the effectiveness of the proposed
framework. Finally, we discuss potential research directions for generative
AI-enabled SAGIN.Comment: 9page, 5 figure
Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey
The rapid development of information and communications technology has
enabled the use of digital-controlled and software-driven distributed energy
resources (DERs) to improve the flexibility and efficiency of power supply, and
support grid operations. However, this evolution also exposes
geographically-dispersed DERs to cyber threats, including hardware and software
vulnerabilities, communication issues, and personnel errors, etc. Therefore,
enhancing the cyber-resiliency of DER-based smart grid - the ability to survive
successful cyber intrusions - is becoming increasingly vital and has garnered
significant attention from both industry and academia. In this survey, we aim
to provide a systematical and comprehensive review regarding the
cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an
integrated threat modeling method is tailored for the hierarchical DER-based
smart grid with special emphasis on vulnerability identification and impact
analysis. Then, the defense-in-depth strategies encompassing prevention,
detection, mitigation, and recovery are comprehensively surveyed,
systematically classified, and rigorously compared. A CRE framework is
subsequently proposed to incorporate the five key resiliency enablers. Finally,
challenges and future directions are discussed in details. The overall aim of
this survey is to demonstrate the development trend of CRE methods and motivate
further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication
Consideratio
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Machine learning and blockchain technologies for cybersecurity in connected vehicles
Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified
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