Intrusion Detection Systems

An Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our disposal, many solutions have been found to create many intrusion detection systems. However, it is difficult to identify the perfect solution from the vast options we have available. Hence, motivated by the need of a better security system, this paper presents a survey of different published solutions that have been developed and/or researched on the topic of intrusion detection techniques during the period from 2000 to 2019, including the accuracy of the output. With the help of this survey, an all-inclusive view of the different papers would be at one’s disposal.

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A Review of Intrusion Detection Datasets and Techniques

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v6i3.277 ◽

2020 ◽

Vol 6 (3) ◽

pp. 14-22

Author(s):

Sadhana Patidar ◽

Priyanka Parihar ◽

Chetan Agrawal

Keyword(s):

Network Security ◽

Intrusion Detection ◽

Computer Security ◽

Network Activity ◽

Intrusion Detection Systems ◽

Unauthorized Access ◽

Detection Techniques ◽

Detection Systems ◽

Network Applications ◽

Speed And Accuracy

As network applications grow rapidly, network security mechanisms require more attention to improve speed and accuracy. The evolving nature of new types of intrusion poses a serious threat to network security: although many network securities tools have been developed, the rapid growth of intrusive activities is still a serious problem. Intrusion detection systems (IDS) are used to detect intrusive network activity. In order to prevent and detect the unauthorized access of any computer is a concern of Computer security. Hence computer security provides a measure of the level associated with Prevention and Detection which facilitate to avoid suspicious users. Deep learning have been widely used in recent years to improve intrusion detection in networks. These techniques allow the automatic detection of network traffic anomalies. This paper presents literature review on intrusion detection techniques.

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A Review of Intrusion Detection Systems in Cloud Computing

Cloud Security ◽

10.4018/978-1-5225-8176-5.ch003 ◽

2019 ◽

pp. 54-83

Author(s):

Chiba Zouhair ◽

Noreddine Abghour ◽

Khalid Moussaid ◽

Amina El Omri ◽

Mohamed Rida

Keyword(s):

Cloud Computing ◽

Intrusion Detection ◽

Detection System ◽

Security Requirements ◽

Intrusion Detection Systems ◽

Cloud Environment ◽

Detection Techniques ◽

Detection Systems ◽

Cloud Architecture ◽

Data Source

Security is a major challenge faced by cloud computing (CC) due to its open and distributed architecture. Hence, it is vulnerable and prone to intrusions that affect confidentiality, availability, and integrity of cloud resources and offered services. Intrusion detection system (IDS) has become the most commonly used component of computer system security and compliance practices that defends cloud environment from various kinds of threats and attacks. This chapter presents the cloud architecture, an overview of different intrusions in the cloud, the challenges and essential characteristics of cloud-based IDS (CIDS), and detection techniques used by CIDS and their types. Then, the authors analyze 24 pertinent CIDS with respect to their various types, positioning, detection time, and data source. The analysis also gives the strength of each system and limitations in order to evaluate whether they carry out the security requirements of CC environment or not.

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Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers

Electronics ◽

10.3390/electronics9122006 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2006

Author(s):

Malek Al-Zewairi ◽

Sufyan Almajali ◽

Moussa Ayyash

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Type A ◽

Intrusion Detection Systems ◽

Classification Error ◽

Type B ◽

Detection Techniques ◽

Detection Systems ◽

Network Intrusion ◽

Security Attack

Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks constitutes an eccentric challenge for all types of intrusion detection systems. Additionally, the lack of a standard definition of what constitutes an unknown security attack in the literature and the industry alike adds to the problem. In this paper, the researchers reviewed the studies on detecting unknown attacks over the past 10 years and found that they tended to use inconsistent definitions. This formulates the need for a standard consistent definition to have comparable results. The researchers proposed a new categorisation of two types of unknown attacks, namely Type-A, which represents a completely new category of unknown attacks, and Type-B, which represents unknown attacks within already known categories of attacks. The researchers conducted several experiments and evaluated modern intrusion detection systems based on shallow and deep artificial neural network models and their ability to detect Type-A and Type-B attacks using two well-known benchmark datasets for network intrusion detection. The research problem was studied as both a binary and multi-class classification problem. The results showed that the evaluated models had poor overall generalisation error measures, where the classification error rate in detecting several types of unknown attacks from 92 experiments was 50.09%, which highlights the need for new approaches and techniques to address this problem.

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MULTI-GIGABIT PATTERN FOR DATA IN NETWORK SECURITY

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2013.1204 ◽

2013 ◽

pp. 249-256

Author(s):

Praveen Kumar . Ch ◽

Prof.P.Vijai Bhaskar ◽

Ravi. Ch ◽

B.Rambhupal Reddy

Keyword(s):

Network Security ◽

Intrusion Detection ◽

Pattern Matching ◽

Intrusion Detection Systems ◽

Search Pattern ◽

Detection Systems ◽

Network Intrusion ◽

Matching Techniques ◽

Detection Search ◽

Perfect Hashing

In the current scenario network security is emerging the world. Matching large sets of patterns against an incoming stream of data is a fundamental task in several fields such as network security or computational biology. High-speed network intrusion detection systems (IDS) rely on efficient pattern matching techniques to analyze the packet payload and make decisions on the significance of the packet body. However, matching the streaming payload bytes against thousands of patterns at multi-gigabit rates is computationally intensive. Various techniques have been proposed in past but the performance of the system is reducing because of multi-gigabit rates.Pattern matching is a significant issue in intrusion detection systems, but by no means the only one. Handling multi-content rules, reordering, and reassembling incoming packets are also significant for system performance. We present two pattern matching techniques to compare incoming packets against intrusion detection search patterns. The first approach, decoded partial CAM (DpCAM), pre-decodes incoming characters, aligns the decoded data, and performs logical AND on them to produce the match signal for each pattern. The second approach, perfect hashing memory (PHmem), uses perfect hashing to determine a unique memory location that contains the search pattern and a comparison between incoming data and memory output to determine the match. The suggested methods have implemented in vhdl coding and we use Xilinx for synthesis.

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A Review of Intrusion Detection Systems in Cloud Computing

Security and Privacy in Smart Sensor Networks - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-5736-4.ch012 ◽

2018 ◽

pp. 253-283 ◽

Cited By ~ 4

Author(s):

Chiba Zouhair ◽

Noreddine Abghour ◽

Khalid Moussaid ◽

Amina El Omri ◽

Mohamed Rida

Keyword(s):

Cloud Computing ◽

Intrusion Detection ◽

Detection System ◽

Security Requirements ◽

Intrusion Detection Systems ◽

Cloud Environment ◽

Detection Techniques ◽

Detection Systems ◽

Cloud Architecture ◽

Data Source

Security is a major challenge faced by cloud computing (CC) due to its open and distributed architecture. Hence, it is vulnerable and prone to intrusions that affect confidentiality, availability, and integrity of cloud resources and offered services. Intrusion detection system (IDS) has become the most commonly used component of computer system security and compliance practices that defends cloud environment from various kinds of threats and attacks. This chapter presents the cloud architecture, an overview of different intrusions in the cloud, the challenges and essential characteristics of cloud-based IDS (CIDS), and detection techniques used by CIDS and their types. Then, the authors analyze 24 pertinent CIDS with respect to their various types, positioning, detection time, and data source. The analysis also gives the strength of each system and limitations in order to evaluate whether they carry out the security requirements of CC environment or not.

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Framework for Threat Analysis and Attack Modelling of Network Security Protocols

Securing the Internet of Things ◽

10.4018/978-1-5225-9866-4.ch007 ◽

2020 ◽

pp. 110-124

Author(s):

Nachiket Athavale ◽

Shubham Deshpande ◽

Vikash Chaudhary ◽

Jatin Chavan ◽

S. S. Barde

Keyword(s):

Network Security ◽

Intrusion Detection ◽

Detection System ◽

Security Protocols ◽

Intrusion Detection Systems ◽

Sensitive Information ◽

Detection Systems ◽

Threat Analysis ◽

Warning Message ◽

System Administrator

Nowadays everything is computerized including banking and personal records. Also, to boost business profits, businessmen have changed their way of operations from physical way to electronic way, for example Flipkart. But as these developments benefit the developer they also increase the chance of exposing all of customer's personal details to malicious users. Hackers can enter into the system and can steal crucial or sensitive information about other authentic users and in case of banks leads to frauds. Security thus, becomes an important issue for all companies and banks. Intrusion detection systems help such companies by detecting in real time whether an intrusion is carried on or not. Here the authors are developing a signature based intrusion detection system which will scan incoming packets and send a warning message to system administrator. Also, the authors are implementing a framework and provide it to all the users so that developing intrusion detection based system similar to ours. The advantage of using framework is that it can be upgraded and re-defined whenever it is needed.

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A Survey of parallel intrusion detection on graphical processors

Open Computer Science ◽

10.2478/s13537-014-0213-6 ◽

2014 ◽

Vol 4 (4) ◽

Cited By ~ 3

Author(s):

Liberios Vokorokos ◽

Michal Ennert ◽

Marek >Čajkovský ◽

Ján Radušovský

Keyword(s):

Intrusion Detection ◽

Pattern Matching ◽

Graphics Processing Units ◽

Academic Research ◽

General Purpose ◽

Intrusion Detection Systems ◽

Detection Techniques ◽

Detection Systems ◽

Common Technique ◽

Graphics Processing

AbstractIntrusion detection is enormously developing field of informatics. This paper provides a survey of actual trends in intrusion detection in academic research. It presents a review about the evolution of intrusion detection systems with usage of general purpose computing on graphics processing units (GPGPU). There are many detection techniques but only some of them bring advantages of parallel computing implementation to graphical processors (GPU). The most common technique transformed into GPU is the technique of pattern matching. There is a number of intrusion detection tools using GPU tested in real network traffic.

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