scholarly journals Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

2021 ◽  
Vol 13 (22) ◽  
pp. 12337
Author(s):  
Abdullah Alharbi ◽  
Adil Hussain Seh ◽  
Wael Alosaimi ◽  
Hashem Alyami ◽  
Alka Agrawal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Security And Privacy ◽  
Intrusion Detection Systems ◽  
Critical Function ◽  
Security Issues ◽  
Detection Systems ◽  
The Impact

Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems.

scholarly journals Machine Learning Approach for Intrusion Detection Systems as a Cyber Security Strategy for Small and Medium Enterprises

2022 ◽  
Vol 19 ◽  
pp. 474-480
Author(s):  
Nevila Baci ◽  
Kreshnik Vukatana ◽  
Marius Baci
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Small And Medium Enterprises ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Original Dataset ◽  
Medium Enterprises

Small and medium enterprises (SMEs) are businesses that account for a large percentage of the economy in many countries, but they lack cyber security. The present study examines different supervised machine learning methods with a focus on intrusion detection systems (IDSs) that will help in improving SMEs’ security. The algorithms that are tested through a real dataset, are Naïve Bayes, Sequential minimal optimization (SMO), C4.5 decision tree, and Random Forest. The experiments are run using the Waikato Environment for Knowledge Analyses (WEKA) 3.8.4 tools and the metrics used to evaluate the results were: accuracy, false-positive rate (FPR), and total time to train and build a classification model. The results obtained from the original dataset with 130 features show a high value of accuracy, but the computation time to build the classification model was notably high for the cases of C4.5 (1 hr. and 20 mins) and SMO algorithm (4 hrs. and 20 mins). the Information Gain (IG) method was used and the result was impressive. The time needed to train the model was reduced in the order of a few minutes and the accuracy was high (above 95%). In the end, challenges that SMEs can have for choosing an IDS such as lack of scalability and autonomic self-adaptation, can be solved by using a correct methodology with machine learning techniques.

scholarly journals A Comprehensive Analysis and Solution of Cyber Attacks using Machine Learning Techniques

2020 ◽  
Vol 8 (12s3) ◽  
pp. 70-74
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Computer Network ◽  
Modern Society ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Human Skills

Cyber security is a major problem of modern society so that Vulnerabilities of computer Network is become easy with the help of technologies and human skills. Now day’s difference type of attacks occurred for example DOS attack, Probing, R2U, R2L virus, port scans, buffer overflow, CGI Attack and flooding etc. We need a platform where a system can be developed for recognition and prevention of these attacks. In This paper, most of the latest methods are summarised to implement IDS for cyber security. Intrusion Detection Systems is a most suitable solution for cyber attacks. Machine learning based Intrusion Detection Systems have high accuracy, in rapidly changing environment. This paper discusses which type of ML techniques has low accuracy, so it explore some research area for researcher.

scholarly journals Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

2021 ◽  
Vol 1 (2) ◽  
pp. 252-273
Author(s):  
Pavlos Papadopoulos ◽  
Oliver Thornewill von Essen ◽  
Nikolaos Pitropakis ◽  
Christos Chrysoulas ◽  
Alexios Mylonas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Learning Models ◽  
Detection Systems ◽  
Network Intrusion ◽  
Robust Model ◽  
Significant Probability ◽  
Adversarial Examples ◽  
Attack Surface

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

scholarly journals Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 315
Author(s):  
Nathan Martindale ◽  
Muhammad Ismail ◽  
Douglas A. Talbert
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of-Things (IoT) devices enter the market. This has motivated research in applying machine learning algorithms that can operate on streams of data, trained online or “live” on only a small amount of data kept in memory at a time, as opposed to the more classical approaches that are trained solely offline on all of the data at once. In this context, one important concept from machine learning for improving detection performance is the idea of “ensembles”, where a collection of machine learning algorithms are combined to compensate for their individual limitations and produce an overall superior algorithm. Unfortunately, existing research lacks proper performance comparison between homogeneous and heterogeneous online ensembles. Hence, this paper investigates several homogeneous and heterogeneous ensembles, proposes three novel online heterogeneous ensembles for intrusion detection, and compares their performance accuracy, run-time complexity, and response to concept drifts. Out of the proposed novel online ensembles, the heterogeneous ensemble consisting of an adaptive random forest of Hoeffding Trees combined with a Hoeffding Adaptive Tree performed the best, by dealing with concept drift in the most effective way. While this scheme is less accurate than a larger size adaptive random forest, it offered a marginally better run-time, which is beneficial for online training.

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