Intrusion Detection Systems

2021 ◽  
pp. 235-254
Author(s):  
Riya Bilaiya ◽  
Priyanka Ahlawat ◽  
Rohit Bathla
Keyword(s):  
Comparative Analysis ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Security Issues ◽  
Detection Systems ◽  
Current Trends ◽  
Future Challenges

The community is moving towards the cloud, and its security is important. An old vulnerability known by the attacker can be easily exploited. Security issues and intruders can be identified by the IDS (intrusion detection systems). Some of the solutions consist of network firewall, anti-malware. Malicious entities and fake traffic are detected through packet sniffing. This chapter surveys different approaches for IDS, compares them, and presents a comparative analysis based on their merits and demerits. The authors aim to present an exhaustive survey of current trends in IDS research along with some future challenges that are likely to be explored. They also discuss the implementation details of IDS with parameters used to evaluate their performance.

Intrusion detection systems for RPL security: A comparative analysis

2021 ◽  
Vol 104 ◽  
pp. 102219
Author(s):  
George Simoglou ◽  
George Violettas ◽  
Sophia Petridou ◽  
Lefteris Mamatas
Keyword(s):  
Comparative Analysis ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems

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.

Disadvantages of Modern Intrusion Detection Systems

2006 ◽  
Vol 65 (10) ◽  
pp. 929-936
Author(s):  
A. V. Agranovskiy ◽  
S. A. Repalov ◽  
R. A. Khadi ◽  
M. B. Yakubets
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems
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