Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Deep Learning for Anomaly Detection: A Review

The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.

Review: Intrusion detection by machine learning: A review

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Anomaly detection, a.k.a. outlier detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper reviews the research of deep anomaly detection with a comprehensive taxonomy of detection methods, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

/pdf/multiagent-systems-a-modern-approach-to-distributed-5fon0donq1.pdf

Multiagent systems: a modern approach to distributed artificial intelligence

A supervised intrusion detection method with new distance definition is proposed in this paper. This method based on constrained clustering, uses the produced clusters as classification model to predict which cluster the current data belongs to. The time complexity of the method is nearly linear with the size of dataset, the number of attributes and the final number of clusters. It is difference from existing supervised methods that our method can detect unknown intrusions. The experiment results on dataset KDDCUP99 demonstrate that the method has promising performance with high detection rate and low false alarm rate.

A supervised intrusion detection method

It is an important issue for the security of network that how to detect new intrusions attack. This paper investigates unsupervised intrusion detection method. A distance definition for mixed attributes, a simple method calculating cluster radius threshold, a outlier factor measured deviating degree of a cluster, and a novel intrusion detection method are proposed in this paper. The experimental results show that the method has promising performance with high detection rate and low false alarm rate, also can detect new intrusion.

/pdf/a-novel-intrusion-detection-method-1nw3f768z2.pdf

A novel intrusion detection method

It is very necessary for the mobile computing system to be equipped with the checkpointing recovery facility because of the fact that the MHs are vulnerable to the failures. Previous recovery schemes based on message logging are under the condition that exchanged messages among mobile hosts are forwarded by MSSs(m-MSS-m communication). A new recovery protocol is presented for mobile host direct to mobile host(m-m) communication wireless network in this paper. The m-m communication contributes to a less contention on the wireless network and a lower latency for message transmission than m-MSS-m. Theoretical analyses and simulation results show that the proposed approach provides higher performance in terms of fail-free overhead and recovery overhead than traditional schemes.

http://ieeexplore.ieee.org/iel5/8907/28159/01259837.pdf

An efficient recovery scheme for mobile computing system

One of the most advanced research issues in network security is intrusion-tolerant intrusion detection, which has become another essential technique to protect computer systems and prevent the intrusion from generating a system failure. This paper presents a novel intrusion-tolerant intrusion detection method based on real-time sequence forecast analysis for network stream. We devise linear regression techniques to forecast network stream sequences. According to these, it's helpful for us to analysis intruders' behaviors and to recognize undesirable intrusions. We also provide recovery strategies to tolerate intrusion. Experiments on the http server demonstrate that our method outperform the others.

An Intrusion-Tolerant Intrusion Detection Method Based on Real-Time Sequence Analysis

It has been the recent research focus and trend to apply data mining techniques in an intrusion detection system for discovering new types of attacks, but it is still in its infancy. This paper presents an innovative technique, called MMID, that applies maximal frequent itemsets mining to intrusion detection and can significantly improve the accuracy and performance of an intrusion detection system. The experimental results show that MMID is efficient and accurate for the attacks that occur intensively in a short period of time.

Mining Maximal Frequent Itemsets for Intrusion Detection

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