3,223 research outputs found
OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
Anomaly detection in decision-making sequences is a challenging problem due
to the complexity of normality representation learning and the sequential
nature of the task. Most existing methods based on Reinforcement Learning (RL)
are difficult to implement in the real world due to unrealistic assumptions,
such as having access to environment dynamics, reward signals, and online
interactions with the environment. To address these limitations, we propose an
unsupervised method named Offline Imitation Learning based Anomaly Detection
(OIL-AD), which detects anomalies in decision-making sequences using two
extracted behaviour features: action optimality and sequential association. Our
offline learning model is an adaptation of behavioural cloning with a
transformer policy network, where we modify the training process to learn a Q
function and a state value function from normal trajectories. We propose that
the Q function and the state value function can provide sufficient information
about agents' behavioural data, from which we derive two features for anomaly
detection. The intuition behind our method is that the action optimality
feature derived from the Q function can differentiate the optimal action from
others at each local state, and the sequential association feature derived from
the state value function has the potential to maintain the temporal
correlations between decisions (state-action pairs). Our experiments show that
OIL-AD can achieve outstanding online anomaly detection performance with up to
34.8% improvement in F1 score over comparable baselines
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics
Anomaly detection is the process of finding data points that deviate from a
baseline. In a real-life setting, anomalies are usually unknown or extremely
rare. Moreover, the detection must be accomplished in a timely manner or the
risk of corrupting the system might grow exponentially. In this work, we
propose a two level framework for detecting anomalies in sequences of discrete
elements. First, we assess whether we can obtain enough information from the
statistics collected from the discriminator's layers to discriminate between
out of distribution and in distribution samples. We then build an unsupervised
anomaly detection module based on these statistics. As to augment the data and
keep track of classes of known data, we lean toward a semi-supervised
adversarial learning applied to discrete elements.Comment: 5 pages, 53rd Annual Conference on Information Sciences and Systems,
CISS 201
The New Abnormal: Network Anomalies in the AI Era
Anomaly detection aims at finding unexpected patterns in data. It has been used in several problems in computer networks, from the detection of port scans and DDoS attacks to the monitoring of time-series collected from Internet monitoring systems. Data-driven approaches and machine learning have seen widespread application on anomaly detection too, and this trend has been accelerated by the recent developments on Artificial Intelligence research. This chapter summarizes ongoing recent progresses on anomaly detection research. In particular, we evaluate how developments on AI algorithms bring new possibilities for anomaly detection. We cover new representation learning techniques such as Generative Artificial Networks and Autoencoders, as well as techniques that can be used to improve models learned with machine learning algorithms, such as reinforcement learning. We survey both research works and tools implementing AI algorithms for anomaly detection. We found that the novel algorithms, while successful in other fields, have hardly been applied to networking problems. We conclude the chapter with a case study that illustrates a possible research direction
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
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