Intrusion Detection Systems

The IDS described in [32] presents an IDS having a hybrid placement strategy. In this technique, the nodes are required to scan their neighborhood for changes and send the changelog to central modules in the border router of networks. Summerville et al. [34] developed an IDS for IoT based on a deep-packet anomaly detection method. The detection method used here uses bit-pattern matching for feature selection. Network payloads are termed as a sequence of bytes and feature selection operates on overlapping tuples of bytes called n-grams. A match between a bit-pattern and an n-gram occurs when the corresponding bits match all positions. The evaluation of the above approach resulted in very low false-positive rates for four common types of threats. In [35], the authors have represented an IDS for IoT named (Kalis)- “Knowledge-Driven Adaptable Lightweight Intrusion Detection Systems”. This technique employs a centralized placement technique for IDS. This system utilizes

ignores the decision problems. Crucially, these questions concern diversity: defences should be diverse in their weaknesses. Any attack that happens to defeat one defence should with high probability be stopped or detected by another one. Ultimately, diversity and defence in depth are two facets of the same defensive design approach. The important questions are not about defence in depth being "a good idea", but about whether a set of specific defences would improve security more than another set; and about – if possible – quantifying the security gains. In this paper, we present analysis approach to help analysts with these decision problems. We illustrate the use of the approach with data from an empirical study with multiple Intrusion Detection Systems (IDSs). We study the effects that using diverse IDSs has on the detection of attacks (false negatives), and on allowing legitimate traffic to go through (false positives).

Intrusion detection systems (IDS) can be classified into two main categories: misuse and anomaly intrusion detection systems. Misuse refers to the known attacks that make use of the known system vulnerabilities. Mis- use systems define attack signatures, i.e. patterns of ac- tivity that are known to be undesirable. Then misuse systems are monitoring the system activity in order to find out the defined signatures, the presence of which indicates an attack. Each misuse system has several draw-backs. First, it is difficult to create an exhaustive at- tack database and so some attacks might be unrecogn- ised. Furthermore, a small variation of a known attack might differ from the predefined signature put into data- base and the misuse system can miss the attack event entirely.

An intrusion is a sequence of related actions performed by a suspicious adversary, which result in the form of compromise of a target system. These kinds of actions actually violate a certain security policy of the system. Security policy of a system defines which actions are considered to be malicious for the system and should be prevented in order to maintain the security of the system [2]. The process of identifying and responding to suspicious activities of a target system is called Intrusion Detection. It is a complementary approach to security with respect to the mainstream approaches, such as access control and cryptography [2]. Intrusion detection systems are used to monitor computer systems, as well as the network and to raise alarms when some intrusive activities are detected.

Intrusion detection systems, alongside firewalls and gateways, represent the first line of defense against computer network attacks. There are various commercial or open source intrusion detection systems in the market; nevertheless they do not perform well in various situations including novel attacks, user activity detection, generating in some cases false positive or negative alerts. The reason behind such performance is probably due to the implementation of merely signature based checks and a high degree of dependence on human interaction. On the other hand, a neural network approach might be the right one to tackle these issues. Neural networks have already been applied successfully to solve many problems related to pattern recognition, data mining, data compression and research is still underway with regards to intrusion detection systems. Unsupervised learning and fast network convergence are some features that can be integrated into an IDS system using neural networks. The networks can be designed to process a variety of data, although there are some constraints regarding input formatting. For this reason, data encoding represents a challenging task in the integration process since it needs to be optimised for the IDS domain. This paper will discuss the integration of IDS and neural networks, including data encoding and performance issues.

Intrusion Detection Systems identify attacks against a system or users per- forming illegitimate actions. Using a common analogy, having an Intrusion De- tection System is like having a ”burglar alarm” in your house. The alarm will not prevent the burglar from breaking into your house, but it will detect and warn you of the problem. Following the publication of the first research in Intrusion Detection Systems, a large number of diverse applications have been developed. One method of accomplishing this type of detection is the use of file system integrity tools. When a system is compromised, an attacker will often alter cer- tain key files to provide continued access and to prevent detection. The changes could target any portion of the system software, e.g. the kernel, libraries, log files, or other sensitive files. File system integrity checkers detect those changes and trigger a corresponding alert. To guarantee the integrity of the file system, two approaches can be followed.

Traditional Intrusion Detection Systems (IDSs) use signatures where attacks are defined as a sequence of events to match with network traffic [86]. This approach is accurate as long as the list of attacks is known in advance and signatures are defined before deploying an IDS such as Snort [87] and Bro [88]. There has been little effort to develop signature-based IDS for web applications. Moreover, they rely on regular expressions to detect attacks. For example, a script created to use a PHPIDS [89] allows attack signatures to be expressed using a set of regular expressions. The burden is on the user to keep up with new expressions. To address this limitation of a signature- based IDS, in this paper, we propose to develop a Genetic Algorithm (GA) based IDS. GA-based approaches have gained the attention of the research community in recent years. In a signature- based attack detection approach, the network traffic is monitored and the IDS searches for malicious behaviors that match the known signatures [4]. Any signatures with even minor deviations from the attack descriptions would not set off any security alarms, which may leave a system vulnerable [38]. However, a GA-based approach can address this limitation by generating new signatures from existing signatures. We explored this idea and carried out a case study [42] to exemplify how a GA can improve attack detection rates as well.

Today it is very important to provide a high-level security to protect highly sensitive and private information. Intrusion Detection System is an essential technology in Network Security. Nowadays researchers have interested on intrusion detection system using Data mining techniques as an artful skill. IDS is a software or hardware device that deals with attacks by collecting information from a variety of system and network sources, then analyzing symptoms of security problems. This paper includes an overview of intrusion detection systems and introduces the reader to some fundamental concepts of IDS methodology. We also discuss the primary intrusion detection techniques. In this paper, we emphasizes data mining algorithms to implement IDS such as Support Vector Machine, Kernelized support vector machine, Extreme Learning Machine and Kernelized Extreme Learning Machine.

this, a system is never completely safe. In the real world this has been known since the dawn of time and guards have been hired to protect important things, in software security the Intrusion Detection Systems(IDS) have taken their role. One of the major problems with NIDS is how to get useful information from it, the sheer amount of information produced by the systems can often overwhelm a human. This is where correlation of events in order to find attacks comes as a solution and this is what this report will discuss. Another problem comes when deploying NIDS and that is: many organizations underestimate the amount of traffic generated by a NIDS on a large scale network. They go from testing to deployment without a pilot phase, and as a result they get poorly maintained systems that don’t work as intended.[15]

Another scholar, Abhijit Sarmah, also states that Intrusion Detection Systems are becoming more of a future requirement for many businesses as well as companies. Subsequently installing the firewall technology at the network perimeter. Network perimeter IDS’ can therefore provide protection from external and internal hackers, in which traffic does not go past the firewall, under any circumstances provided. Sarmah as well as other authors mention that there must be human intervention. As stated, before in this paper, technology has not yet come to the ultimate peak stage, in which machines as well as technology can independently run tasks by themselves without the requirement of interacting with an individual.

An intrusion attempt or intrusion can be defined as the potential possibility of a deliberate unauthorized attempt or action to access information, manipulate information or render a system unreliable or unusable [3,21]. Intrusion attempt or intrusion activity may come from external or internal. Its ultimate purpose is to violate a system’s integrity, confidentiality and reliability. Intrusion detection system (IDS) is the hardware device or software system which is used in the intrusion detection process to monitor network and host activities including data flows and information accesses etc. and detect suspicious activities. It serves three essential security functions: they monitor, detect, and respond to unauthorized activity by both internal intruders and external intruders. Intrusion detection systems use policies to define certain events that, if detected will issue an alert [1,4, 12, 13, 17].

This survey paper describes special categories of intrusion detection system and best parts of methods of intrusion detection systems. We draw attention to Pattern Matching, Measure Based method, Data Mining method, Machine Learning Method techniques, which is used to execute Intrusion Detection System (IDS). We also describe special types of attack from which we need to take precautions in IDS. We do the comparative analysis of various Intrusion detection approaches. We sure this brief survey is useful for all researchers that want to investigate more efficient methods against intrusions.

Security model with firewalls Intrusion detection systems Intrusion prevention systems How to prevent and detect attacks What is a firewall.. Divides network into two (or more) parts wit[r]

______________________________________________________________________________________________________ Abstract— Network based technology and Cloud Computing is becoming popular day by day as many enterprise applications and data are moving into cloud or Network based platforms. Because of the distributed and easy accessible nature, these services are provided over the Internet using known networking protocols, Protocol standards and Protocol formats under the supervision of different management’s tools and programming language. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion so many Attacks like Denial of Service (DDOS), Buffer overflows, Sniffer attacks and Application-Layer attacks have become a common issue today. Recent security incidents and analysis Have manual response to such attacks and resolve that attacks are no longer feasible. In Internet and Network system application or platform facing various types of attacks in every day. Intrusion Prevention and the IDS tools that are employed to detect these attacks and discuss some open source tools to prevent and detection of intrusion and how can we use Open Source tools in our system. Snort is an open source Network Intrusion Detection System (NIDS) which is available free of cost. NIDS is the type of Intrusion Detection System (IDS) that is used for scanning data flowing on the network. There is also host-based intrusion detection systems, which are installed on a particular host and detect attacks targeted to that host only. Although all intrusion detection methods are still new, Snort is ranked among the top quality systems available today.

Abstract. We present an analysis of the diversity that exists in the rules and blacklisted IP addresses of the Snort and Suricata Intrusion Detection Systems (IDSs). We analysed the evolution of the rulesets and blacklisted IP addresses of these two IDSs over a 5- month period between May and October 2017. We used three different off-the-shelf default configurations of the Snort IDS and the Emerging Threats (ET) configuration of the Suricata IDS. Analysing the differences in these systems allows us to get insights on where the diversity in the behaviour of these systems comes from and how does it evolve over time. This gives insight to Security architects on how they can combine and layer these systems in a defence-in-depth deployment. To the best of our knowledge a similar experiment has not been performed before. We will also show results on the observed diversity in behaviour of these systems, when they analysed the network data of the DMZ network of City, University of London.

Intrusion Detection Systems (IDS) are now becoming one of the essential components in any organization’s network. IDS are designed to detect any intrusion or hostile traffic in a network. With the serious need of such detection systems organizations have been investing to produce a more effective IDS. Intrusion Detection Systems can be implemented as a hardware based or software-based [1, 2]. The later type of IDS is more configurable and easy to update while the hardware based is designed to handle large amount of trafficbut more expensive and require more maintenance. There is therefore a need to evaluate the available software-based IDS. In general, instruction detection systems fall into two main categories; Network based systems and Host based systems [3].

As the focus of computing shifted from mainframe environments to distributed networks of workstations, several prototypes of intrusion-detection systems were developed to accommodate network issues. Here the first step was to get host-based intrusion-detection systems to communicate [29]. In a distributed environment, users hop from one machine to another, possibly changing identities during their moves, and launch their attacks on several systems. Therefore, the local intrusion-detection system on the workstation has to exchange information with its peers. This exchange of information takes place at several levels, either by exchanging a raw audit trail over the network as Stalker [23] does, or by issuing alarms based on a local analysis [51]. Both solutions incur costs: transferring audits can potentially have a huge impact on network bandwidth, whereas processing them locally affects the workstation’s performance.

We develop a formal framework that permits analysis of the detection rules in intrusion detection systems. This analysis can be used to identify errors and improve the quality of the rules. In particular, we focus our effort on misuse detection and specification- based detection, both employing declarative rules to detect intrusive activity. Based on our experience, developing good misuse signatures and good specifications for programs is tricky and error prone. It often requires insights into the attacks and critical security aspects of a system. In addition, one needs to be very careful when writing them to avoid gaps in coverage. In particular, it is hard to judge whether changes to the rules actually improve or degrade the ability to detect new attacks. This research explores the applica- tion of techniques from formal method research to assist the development and analysis of detection rules. We employ verification techniques to prove that a given rule set, together with the operating system, can enforce a given security policy. In addition, we enumerate assumptions of specific security policies to identify possible ways the policies can be violated without being detected by the detection rules. Thus we abstract any special attacks and strict to prove that our IDS will detect any attack that threatening the security policy. The assumptions cover the kind of attacks that cannot or very unlikely to occur.

McAfee IntruShield, a part of Network Associates’ McAfee Network Protection Solutions family of products, is a unique cutting-edge technology that prevents intrusions “on the wire” before they hit critical systems. Highly automated and easily managed, McAfee IntruShield is designed with such flexibility that it can be implemented in a phased approach - that overcomes the false positives inherent with today’s legacy intrusion detection systems - and thus enables you to develop the right policy for blocking in your unique IT infrastructure. For example, you can deploy in-line to notify and block known attacks, and to notify-only on unknown attacks. Or you can implement complete blocking but just for business-critical network segments. IntruShield is delivered in a high-speed appliance which is able to scan traffic and assess threat levels with blinding speed, even on gigabit networks. It can be used at the edge or in front of key “core” resources. IntruShield has been crafted to satisfy both the security and network administrators as it stops a wide range of network attacks but does so with network latencies typically less than 10 milliseconds. IntruShield also looks for anomalous behavior and includes specialized analysis to find new denial of service “mass attacks”.

By using a computer for communication, especially trough a network, identification by wire is lost. Instead a identification by computer or by user ID can take place. Both can be attacked as they are based on transmitted data. Computer networks offer numerous points of attack. Tools for spoofing can be used to act as another calling party. In [ASRS01] security issues are discussed in more detail. Intrusion detection is a widely accepted technology to address security problems in computer networks (see e.g. [SBD+91]). But, compared to most computer networks secured by an intrusion detection system (IDS), a telephony network is much more complex: