Overview The Scenario Imagine you are a cybersecurity expert working for a company looking to overcome issues mentioned in Section 6 of A Comprehe

Telecommunication Systems (2019) 70:447–489 https://doi.org/10.1007/s11235-018-0475-8

A comprehensive survey on network anomaly detection

Gilberto Fernandes Jr.1 · Joel J. P. C. Rodrigues1,2,3,4,6 · Luiz Fernando Carvalho5 · Jalal F. Al-Muhtadi6 · Mario Lemes Proença Jr.7

Published online: 2 July 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract Nowadays, there is a huge and growing concern about security in information and communication technology among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. In this study, the main objective is to review the most important aspects pertaining to anomaly detection, covering an overview of a background analysis as well as a core study on the most relevant techniques, methods, and systems within the area. Therefore, in order to ease the understanding of this survey’s structure, the anomaly detection domain was reviewed under five dimensions: (1) network traffic anomalies, (2) network data types, (3) intrusion detection systems categories, (4) detection methods and systems, and (5) open issues. The paper concludes with an open issues summary discussing presently unsolved problems, and final remarks.

Keywords Anomaly detection · Network security · Network management · Intrusion detection · Anomaly detection methods

1 Introduction

Nowadays, the scientific community has a constant worry about high-efficiency security and quality of service in large-scale networks. The expansion of new communication technologies and services, along with an increasing number of interconnected network devices, web users, services, and applications, contributes to making computer networks ever larger and more complex as systems. Moreover, there is the so called boundless communication paradigm, for next genera- tion networks, which envisages offering anytime, anywhere, anyhowcommunicationstoitsusersandrequiresthefullinte-

B Joel J. P. C. Rodrigues [email protected]

Gilberto Fernandes Jr. [email protected]

Luiz Fernando Carvalho [email protected]

Jalal F. Al-Muhtadi [email protected]

Mario Lemes Proença Jr. [email protected]

1 Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

gration and interoperability of emergent technologies [1–4]. These issues make it even more complex and challenging to maintain precise network management and lead to seri- ous network vulnerabilities, as security incidents may occur more frequently [5,6].

Such security instances can be caused either by outsiders, as malicious attacks aiming to shut down services or steal private information, or by inside factors (operational prob- lems), such as configuration errors, server crashes, power outages, traffic congestion, or non-malicious large file trans- fers [7]. Regardless of the source, such threats, which are commonly called anomalies, can have a significant impact

2 National Institute of Telecommunications (Inatel), Av. João de Camargo, 510 – Center, Santa Rita do Sapucaì 37540-000, Brazil

3 ITMO University, St. Petersburg, Russia

4 University of Fortaleza (UNIFOR), Fortaleza, Brazil

5 State University of Campinas (UNICAMP), Campinas, Brazil

6 College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh 12372, Saudi Arabia

7 Computer Science Department, State University of Londrina (UEL), Londrina, Brazil

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448 G. Fernandes Jr. et al.

on the network service and end-users and harm computer network operations and availability.

The term anomaly has several definitions. Barnett and Lewis define a data set anomaly as “observation (or a sub- set of observations) which appears to be inconsistent with the remainder of that set of data” [8]. Chandola et al. express this term as “patterns in data not conforming to a well-defined notion of normal behavior” [9]. According to Lakhina et al., “anomalies are unusual and significant changes in a network’s traffic levels, which can often span multiple links” [10]. Hoque et al. define it as “non-conforming inter- estingpatternscomparedtothewell-definednotionofnormal behavior” [11]. By these definitions, it is clear that the con- cept of normality is one of the main steps toward developing a solution to detect network anomalies.

Although apparently unpretentious, the problem of defin- ing a region denoting normal behavior and marking as an anomaly any occasion contrasting this normal pattern, is defiant. Faster diagnosis, lower complexity and suit- able corrections of the causes are the main objectives of the field. Every factor is vital to developing a bet- ter anomaly detection approach. The precision and speed factors, alongside with the correct identification of such abnormal events in a timely fashion are critical to reduc- ing significant service degradation, malicious damage, and cost. For this reason, the research community has been devel- oping a lot of models, algorithms, and mechanisms, over the years, to develop better solutions and approaches to guaranteeing the health of ever larger and complex network systems.

Researchers have been studying the anomaly detec- tion subject since the early 19th century, and so far, they have produced a multitude of papers, each using a vari- ety of techniques, from statistical models, up to evolu- tionary computation approaches. Nevertheless, it is not a straightforward task to identify and categorize all exist- ing anomaly detection techniques. Plenty of topics must be considered, such as anomaly types, system types, tech- niques and algorithms used, as well as technical dilemmas such as processing costs and network complexity. There- fore, this leads to the fragmented literature available today, in which many works try to summarize everything but are unable to show the bigger picture of the anomaly detection spectrum.

As in [12,13], the focus is just on the most popular tech- niques and methods, such as machine learning, clustering and statistical approaches. Still, surveys such as [14,15] briefly discuss the whole problem statement, setting aside relevant topics such as data set, challenges, and recommen- dations. Marnerides et al. [16] have a reviewed anomaly detection over backbone networks. Although each of those inspected surveys summarizes many important topics per- taining to anomaly detection, they are not entirely complete.

For instance, some of them emphasize anomaly types but do notcoverallkindsofmethodswhileothersresearchuponvast approaches but forget about the basis of intrusion detection systems and data input, and so on. For this reason, in this sur- vey, we present a systematic overview of the whole anomaly detection domain under five dimensions: (i) network anoma- lies, (ii) network data types, (iii) intrusion detection systems overview, (iv) detection methods and systems, and (v) open issues.Table1providesacomparisonbetweensomeanomaly detectionsurveyswithregardtothevarietyoftechniquesthey address.

At last, this survey aims to bring a complete and straight- forward review of state-of-the-art anomaly detection topic. Then, the main contributions of the paper are the following:

• Review the anomaly detection subject under five research directions;

• A detailed study of the most relevant techniques, meth- ods, and systems within the area;

• Address the main drawbacks found in the analyzed sur- veys extracted from the literature;

• Analysis of the four traffic anomaly types categorized by the causal aspect;

• Forward-looking discussion and comparative analysis of other surveys regarding open issues and future trends.

This paper is organized as follows. The introduction presents the overall motivation for developing this survey and a com- parison with other surveys in the literature. Section 2 defines, categorizes, explains, and provides examples of most com- mon types of network anomalies. Section 3 gives a brief explanation of network data types used as input in anomaly detection systems. Section 4 gives a complete overview of intrusion detection systems and the differences between each approach. Section 5 is the core section, which lists many anomaly detection methods and systems using a variety of techniques and algorithms of different nature and purpose. Section 6 summarizes everything discussed in previous sec- tions into some topics considered as open challenges in the anomaly detection domain. Finally, Sect. 7 concludes the survey. Figure 1 shows all contents presented and discussed within the paper.

2 Network traffic anomalies

One of the first tasks envisioned by researchers in creating an anomaly detection model is the correct identification and definition of the problem statement. It means that there must be prior knowledge about what type of anomaly researchers would deal with. There are several types of network traffic anomalies, and each author surveying this topic addresses them differently. For the sake of simplicity, and after analyz-

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A comprehensive survey on network anomaly detection 449

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450 G. Fernandes Jr. et al.

Fig. 1 Paper summary

ing and studying the anomaly context and its categorization, network anomalies can be categorized giving two relevant properties: according to their nature (grouped by how they are characterized, regardless of whether they are malicious or not); and according to their causal aspect (distinguished depending on their cause, regarding either their malicious or non-malicious aspect). Figures 2 and 3 illustrates this cate- gorization and all points that are covered in this section.

2.1 Anomaly categorization based on its nature

The nature of an anomaly is an important aspect of an anomaly detection technique. Depending on the context within which an abnormality is found, or on how it occurred, it can be or not be an abnormality. This aspect can direct how the system will handle and understand mined and detected anomalies. Based on their nature, there are three categories of anomalies: point anomalies, collective anomalies, and con- textual anomalies [9,17,18].

A point anomaly is the deviation of an individual data instance from the usual pattern/behavior. These anomalies are the simplest ones, and because of that, they are the focus of most researchers. For better understanding, suppose that the daily spending of a person is one hundred dollars; then, on aspecificday,theyspendthreehundreddollars.Thissituation characterizes a point anomaly [9,18].

A collective anomaly occurs when only a collection of similar data instances behaves anomalously with reference to the whole dataset. In a collective anomaly, individual anomalous behaviors themselves are not considered anoma- lies; however, their collective occurrence is considered an anomaly. A point anomaly occurring continuously for an extended period or in a cluster amid background data is a collective anomaly. Consider this example: in a sequence of actions in a computer like “…HTTP-web, buffer-overflow, HTTP-web, HTTP-web, FTP, HTTP-web, SSH, HTTP-web, SSH, buffer-overflow…”, the underlined sequence is a col- lective anomaly. The individual events occurring in other positions in the sequence are not anomalies; however, the

123

A comprehensive survey on network anomaly detection 451

Fig. 2 Traffic anomalies categorization

Fig. 3 Network data types categorization

underlined sequence matches a web-based attack by a remote machine followed by the copying of data from the host computer to a remote destination via FTP. Another com- mon example is the ECG exam output, in which low values observed over a long period indicate an anomaly, while one unique low value is not considered abnormal [17,18].

Contextual Anomalies, also called conditional anomalies, are events considered as anomalous depending on the context in which they are found. Two sets of attributes define a con- text (or the condition) for being an anomaly, both of which must be specified during problem formulation. Contextual attributes define the context (or environment); for instance, geographic coordinates in spatial data or time in time-series data specifies the location or position of an instance, respec- tively. On the other hand, behavioral attributes denote the non-contextual features of an instance, i.e., indicators deter- mining whether or not an instance is anomalous in the context [9,18,19]. Consider a time-series data set describing the average bits/s of network traffic in a set of days (contex- tual attribute), in which every day, at 0 h, the server does a regular backup (behavioral attribute). Although the backup generates an outlier in the traffic series, it may not be anoma- lous since it is normal behavior due to a regular backup. However, a similar traffic outlier at 12 h could be considered a contextual anomaly.

2.2 Anomaly categorization based on its causal aspect

The causal aspect distinguishes anomalies depending on their cause, regarding either their malicious or non-malicious

aspect. Anomalies are not always related to attacks intended to harm computer systems or steal information. They can be both events caused by human/hardware failure, bugs or private users when demanding heavy traffic usage, for instance. Thus, as found in Barford et al. [20] and Marnerides et al. [16], anomalies are grouped into four categories: operational/misconfiguration/failure events; flash crowd/legitimate but abnormal use; measurement anomalies; and network abuse anomalies/malicious attacks (or simply, network attacks) [20,21].

Operational events (also called Misconfiguration events or Failures) are non-malicious issues, which may occur in a network system mostly by hardware failures, software bugs or human mistakes. Server crashes, power outages, config- urations errors, traffic congestion, non-malicious large file transfers, inadequate resource configuration, or significant changes in network behavior caused by imposing rate limits or adding new equipment, are all examples of this category of anomaly [7]. Such problems can be perceived visually by nearly abrupt changes in bit rate, which appear steady but occur at a different level over a time period [21].

Legitimate but not abnormal use is commonly referred to as flash crowds. Flash crowds are large floods in traffic, which occur when rapid growth of users attempts to access a specific network resource, causing a dramatic surge in server load. Anomalies in this category consist of legitimate requests, which are usually an aftermath of mutual reaction to hot events but far bigger than the load which the system can handle. Flash crowds may occur when a contest result is pub- lished on a URL, or when an e-commerce website announces a big sale, or even due to software release. Although it is not malicious, if there is not enough time to react and provide the necessary resources to handle overload demand, these flash events can seriously flood or lead to complete web service failure [22,23]. Flash crowd behavior is related to the rapid growth of particular traffic flow types, such as FTP flows, or the gradual fall of a well-known destination over time.

Measurement anomalies are other issues, which are not network infrastructure problems, abnormal usage, or mali- cious attacks. These anomalies are related to collection

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452 G. Fernandes Jr. et al.

infrastructure problems and problems during data collection. Examples are the loss of flow data caused by router overload, or when there is a collection of infrastructure problems and the UDP NetFlow transport to the collector becomes unread- able.

Network abuse anomalies (or network attacks) are a set of malicious actions aiming to disrupt, deny, degrade or destroy information and services from computer network systems, compromising their integrity, confidentiality or availability. Numerous types and classes of attacks currently existing may vary from simple email spam to intrusion attacks on critical network infrastructures. Worms, malicious resource abuse, bug exploits and unauthorized access are some examples of common computer attacks. According to Ghorbani et al. [24], attackers gain access to a system, or limit the availability of that system through some general approaches. These are:

• Social Engineering when an attacker manipulates people to obtain confidential information, making use of hostile persuasion or other interpersonal tactics [25]. Examples are email phishing and email Trojan horses;

• Masquerading thisisatypeofattackinwhichtheattacker uses a fake identity to gain unauthorized access or greater privileges in a system through official access identifica- tion. The attacker illegitimately poses or assumes the identity of another legitimate user, gener- ally by using stolen IDs and passwords [26].

• Implementation Vulnerabilities these are cases in which the attacker exploits software bugs in their targets, such as software, services or applications, in order to gain unauthorized access. Examples are the buffer overflow vulnerability or the mishandling of temporary files.

• Abuse of Functionality malicious activities performed by attackers excessively performing a legal action in order to congest a link or cause a system to fail. A denial-of- service performed on a web-login system by flooding it with valid usernames and arbitrary passwords in order to lock out authentic users, when the allowed login retry limit is exceeded, constitutes an abuse of functionality.

Based on those general approaches of network abuse anoma- lies (network attacks), there are various classes of attacks. Table 2 shows the main attack, which commonly harms com- puter networks and is the major target of anomaly detection mechanisms.

3 Network data types

Another essential step required for building an anomaly detection system is choosing the network data source. The nature of the selected data set may dictate which types of anomalies the system can detect. One needs to choose a data

source correctly depending on what kind of anomalies and IDS approaches are intended as the focus of the research. Because of that, accurate data characterization results in the better performance of the anomaly detection system. This section presents some of the most popular sources used in the anomaly detection subject.

3.1 TCP dump

Tcpdump is a packet analyzer tool used to monitor packets on a computer network. It shows the headers of TCP/IP packets passing through the network interface. It is a tool for network packet capturing and analysis and is recommended to pro- fessionals who need to perform monitoring and maintenance on computer networks, as well as to students who want to understand the operation of the TCP/IP protocol stack. Nev- ertheless, this type of data is not used as much nowadays due to its limited information.

3.2 SNMP

The Simple Network Management Protocol (SNMP) [34] is one of the widely used standards for managing IP net- work components. This protocol has a client-server structure (SNMP managers and SNMP agents) which runs through- out the UDP protocol [35]. SNMP data has been used on intrusion detection systems, since it is useful when it comes to collecting accurate network activity data at a single host level. All collected data are stored, as SNMP objects, in a hierarchical database called MIB (Management Information Base). SNMP objects are summary traffic data constructed by the aggregation of raw data (pcap records) collected mostly by TCP dump tools [16].

Although efficient in their proposals, the works by Cabr- era et al. [36] and Yu et al. [37] are limited to detecting only DoS/DDoS attacks, since these are volume anomalies and SNMP objects rely on volume attributes (bits and packet counts). As presented in Moises et al. [38] and Zarpelao et al. [39], the proposed alarm systems developed over SNMP data have shown high anomaly detection rates by combining clustering and parameterizing techniques. However, none of them had any other information about unknown anomalies, despite the alarms being triggered.

A significant advantage is that SNMP is still a widely deployed protocol with available fine-grained data. It is used in traditional network management tools for measuring per- formance parameters such as error counter interfaces and traffic volume. Packet and bit interface counters are useful; however, nowadays, understanding which IP addresses are the source and destination of traffic and which TCP/UDP ports are generating traffic is vital.

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A comprehensive survey on network anomaly detection 453

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123

454 G. Fernandes Jr. et al.

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A comprehensive survey on network anomaly detection 455

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