One of the emerging research opportunities in machine learning is to develop computing systems that learn many tasks continuously and improve the performance of learned tasks incrementally over time. In real world, learners have to adapt to labeled and unlabeled samples from various tasks which arrive randomly. In this paper, we propose an efficient algorithm called Efficient Perpetual Learning Algorithm (EPLA) which is suitable for learning multiple tasks in both offline and online settings. The algorithm, which is an extension of ELLA,4 is part of what we call perpetual learning that can learn new tasks or refine knowledge of learned tasks for improved performance with newly arrived labeled samples in an incremental fashion. Several salient features exist for EPLA. The learning episodes are triggered via either extrinsic or intrinsic stimuli. Agent systems based on the proposed algorithm can be engaged in an open-ended and alternating sequence of learning episodes and working episodes. Unlabeled samples can be used to self-train the learner in small data setting. Compared with ELLA, EPLA shows almost equivalent performance without memorizing any labeled samples learned previously.
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.
With successful machine learning applications in many fields, researchers tried to introduce machine learning into intrusion detection systems for building classification models. Although experimental results showed that these classification models could produce higher accuracy in predicting network attacks on the offline datasets, compared with the operational intrusion detection systems, machine learning is rarely deployed in the real intrusion detection environment. This is what we call the last mile problem with the machine learning approach to network intrusion detection, the discrepancy between the strength and requirements of machine learning and network operational semantics. In this paper, we aim to bridge the aforementioned gap. In particular, an LCC-RF-RFEX feature selection approach is proposed to select optimal features of the specific type of attacks from dataset, and then, an intrusion-specific approach is introduced to convert them into detection patterns that can be used by the nonmachine-learning detector for the corresponding specific attack detection in the real-world network environment. To substantiate our approach, we take Snort, KDDCup’99 dataset, and Dos attacks as the experimental subjects to demonstrate how to close the last-mile gap. For the specific type of Dos attacks in the KDDCup’99 dataset, we use the LCC-RF-RFEX method to select optimal feature subset and utilize our intrusion-specific approach to generate new rules in Snort by using them. Comparing performance differences between the existing Snort rule set and our augmented Snort rule set with regard to Dos attacks, the experimental results showed that our approach expanded Snort’s detection capability of Dos attacks, on average, reduced up to 25.28% false-positive alerts for Teardrop attacks and Synflood attacks, and decreased up to 98.87% excessive alerts for Mail bomb attacks.
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