Current Challenges in Intrusion Detection Systems

2009 ◽  
pp. 458-466
Author(s):  
H. Gunes Kayacik ◽  
A. Nur Zincir-Heywood
Keyword(s):  
Intrusion Detection ◽  
Network Management ◽  
Denial Of Service ◽  
Security Management ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Network Information ◽  
Detection Systems ◽  
Timely Response ◽  
Unauthorized Disclosure

Along with its numerous benefits, the Internet also created numerous ways to compromise the security and stability of the systems connected to it. In 1995, 171 vulnerabilities were reported to CERT/CC © while in 2003, there were 3,784 reported vulnerabilities, increasing to 8,064 in 2006 (CERT/CC©, 2006). Operations, which are primarily designed to protect the availability, confidentiality, and integrity of critical network information systems are considered to be within the scope of security management. Security management operations protect computer networks against denial-of-service attacks, unauthorized disclosure of information, and the modification or destruction of data. Moreover, the automated detection and immediate reporting of these events are required in order to provide the basis for a timely response to attacks (Bass, 2000). Security management plays an important, albeit often neglected, role in network management tasks.

Intrusion Detection Systems

2011 ◽  
pp. 494-499
Author(s):  
H. Gunes Kayacik ◽  
A. Nur Zincir-Heywood ◽  
Malcolm I. Heywood
Keyword(s):  
Intrusion Detection ◽  
Network Management ◽  
Denial Of Service ◽  
Security Management ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Network Information ◽  
Detection Systems ◽  
Timely Response ◽  
Unauthorized Disclosure

Along with its numerous benefits, the Internet also created numerous ways to compromise the security and stability of the systems connected to it. In 2003, 137529 incidents were reported to CERT/CC© while in 1999, there were 9859 reported incidents (CERT/CC©, 2003). Operations, which are primarily designed to protect the availability, confidentiality, and integrity of critical network information systems, are considered to be within the scope of security management. Security management operations protect computer networks against denial-of-service attacks, unauthorized disclosure of information, and the modification or destruction of data. Moreover, the automated detection and immediate reporting of these events are required in order to provide the basis for a timely response to attacks (Bass, 2000). Security management plays an important, albeit often neglected, role in network management tasks.

Current Challenges in Intrusion Detection Systems

2009 ◽  
pp. 305-311
Author(s):  
H. Gunes Kayacik
Keyword(s):  
Computer Networks ◽  
Defense Mechanisms ◽  
Computer Network ◽  
Denial Of Service ◽  
Security Management ◽  
Network Information ◽  
Software Vulnerabilities ◽  
Detection Systems ◽  
Software Updates ◽  
Unauthorized Disclosure

Along with its numerous benefits, the Internet also created numerous ways to compromise the security and stability of the systems connected to it. In 1995, 171 vulnerabilities were reported to CERT/CC © while in 2003, there were 3,784 reported vulnerabilities, increasing to 8,064 in 2006 (CERT/CC©, 2006). Operations, which are primarily designed to protect the availability, confidentiality, and integrity of critical network information systems are considered to be within the scope of security management. Security management operations protect computer networks against denial-of-service attacks, unauthorized disclosure of information, and the modification or destruction of data. Moreover, the automated detection and immediate reporting of these events are required in order to provide the basis for a timely response to attacks (Bass, 2000). Security management plays an important, albeit often neglected, role in network management tasks. Defensive operations can be categorized in two groups: static and dynamic. Static defense mechanisms are analogous to the fences around the premises of a building. In other words, static defensive operations are intended to provide barriers to attacks. Keeping operating systems and other software up-to-date and deploying firewalls at entry points are examples of static defense solutions. Frequent software updates can remove the software vulnerabilities, which are susceptible to exploits. Firewalls provide access control at the entry point; they therefore function in much the same way as a physical gate on a house. In other words, the objective of a firewall is to keep intruders out rather than catching them. Static defense mechanisms are the first line of defense, they are relatively easy to deploy and provide significant defense improvement compared to the initial unguarded state of the computer network. Moreover, they act as the foundation for more sophisticated defense mechanisms. No system is totally foolproof. It is safe to assume that intruders are always one step ahead in finding security holes in current systems. This calls attention to the need for dynamic defenses. Dynamic defense mechanisms are analogous to burglar alarms, which monitor the premises to find evidence of break-ins. Built upon static defense mechanisms, dynamic defense operations aim to catch the attacks and log information about the incidents such as source and nature of the attack. Therefore, dynamic defense operations accompany the static defense operations to provide comprehensive information about the state of the computer networks and connected systems.

scholarly journals Recent Advancements in Intrusion Detection Systems for the Internet of Things

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zeeshan Ali Khan ◽  
Peter Herrmann
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Research Work ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Systems ◽  
Iot Applications ◽  
Iot Devices

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

scholarly journals CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 916 ◽  
Author(s):  
Jiyeon Kim ◽  
Jiwon Kim ◽  
Hyunjung Kim ◽  
Minsun Shim ◽  
Eunjung Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
National Defense ◽  
Dos Attacks ◽  
Detection Systems ◽  
Network Intrusion

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

scholarly journals The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2559 ◽  
Author(s):  
Celestine Iwendi ◽  
Suleman Khan ◽  
Joseph Henry Anajemba ◽  
Mohit Mittal ◽  
Mamdouh Alenezi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Ensemble Models ◽  
Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

scholarly journals Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 776
Author(s):  
Marcin Niemiec ◽  
Rafał Kościej ◽  
Bartłomiej Gdowski
Keyword(s):  
Intrusion Detection ◽  
Heuristic Algorithm ◽  
Detection Algorithm ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Detection Thresholds ◽  
Detection Systems ◽  
Different Types ◽  
Ongoing Development ◽  
Network Environments

The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for ensuring security in cyberspace. A crucial cybersecurity solution is intrusion detection systems, which detect attacks in network environments and responds appropriately. This article presents a new multivariable heuristic intrusion detection algorithm based on different types of flags and values of entropy. The data is shared by organisations to help increase the effectiveness of intrusion detection. The authors also propose default values for parameters of a heuristic algorithm and values regarding detection thresholds. This solution has been implemented in a well-known, open-source system and verified with a series of tests. Additionally, the authors investigated how updating the variables affects the intrusion detection process. The results confirmed the effectiveness of the proposed approach and heuristic algorithm.

scholarly journals An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021 ◽  
Vol 13 (18) ◽  
pp. 10057
Author(s):  
Imran ◽  
Faisal Jamil ◽  
Dohyeun Kim
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Detection Accuracy ◽  
Learning Mechanism ◽  
Detection Systems ◽  
Network Applications ◽  
Network Intrusion

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

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