Lifelong Learning for Sentiment Classification

Chen, Zhiyuan; Ma, Nianzu; Liu, Bing

doi:10.48550/arxiv.1801.02808

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…Consequently, the concept of lifelong learning can hold realistic significance in long-term real-world applications. The concept of lifelong machine learning [106,265,266] is directed towards the construction of a model that can perform the retraining process repeatedly for the learning of new emerging patterns related to each behaviour. The model should be able to adapt to and learn from new environments continuously [106,265,266].…”

Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

“…The concept of lifelong machine learning [106,265,266] is directed towards the construction of a model that can perform the retraining process repeatedly for the learning of new emerging patterns related to each behaviour. The model should be able to adapt to and learn from new environments continuously [106,265,266]. Researchers have reported that the further they trained the algorithm with the latest features of known DDoS attacks, the more they improved the detection probabilities for known and unknown DDoS attacks.…”

Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

710

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

710

333

View full text Add to dashboard Cite

show abstract

“…Therefore, ML algorithms should have the ability to learn adaptively and continuously to alleviate these anticipated attacks given the scale of network entry points. The current ML paradigm works in two steps: first, a specific training dataset runs a machine-learning algorithm to construct a model, and then the model is used for its intended IoT application [36,37]. The process of continuous ML is still in early development, so challenges exist in applying continuous learning to IoT.…”

Section: Common Challenges and Opportunities In Network Intrusionmentioning

confidence: 99%

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

et al. 2022

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Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid IDSs are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.

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“…Lifelong learning agents learn one task at a time, continuously acquire new knowledge and refine existing knowledge [6,7,39]. By adding a specific KB, the capability to identify new tasks, and the ability to learn on the job, Z. Chen et al extended the definition of LL and employed the LL method in the field of topic modeling [8,40,41].…”

Section: Incremental Performance Improvement Paradigmsmentioning

confidence: 99%

Improving Temporal Event Scheduling through STEP Perpetual Learning

et al. 2022

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Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods.

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Lifelong Learning for Sentiment Classification

Cited by 6 publications

References 16 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Improving Temporal Event Scheduling through STEP Perpetual Learning

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