Multifractal Singularity Spectrum for Cognitive Cyber Defence in Internet Time Series

2015 ◽  
Vol 7 (3) ◽  
pp. 17-45 ◽  
Author(s):  
Muhammad Salman Khan ◽  
Ken Ferens ◽  
Witold Kinsner
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Cyber Security ◽  
Intrusion Detection Systems ◽  
Financial Fraud ◽  
The Internet ◽  
Singularity Spectrum ◽  
Security Systems ◽  
Detection Systems ◽  
Computing Platforms

Growing global dependence over cyberspace has given rise to intelligent malicious threats due to increasing network complexities, inherent vulnerabilities embedded within the software and the limitations of existing cyber security systems to name a few. Malicious cyber actors exploit these vulnerabilities to carry out financial fraud, steal intellectual property and disrupt the delivery of essential online services. Unlike physical security, cyberspace is very difficult to secure due to the replacement of traditional computing platforms with sophisticated cloud computing and virtualization. These complex systems exhibit an increasing degree of complexity in tracking an attack or monitoring possible threats which is becoming intractable with the existing security firewalls and intrusion detection systems. In this paper, authors present a novel complexity detection technique using generalized multifractal singularity spectrum which is able to not only capture the growing complexity of the internet time series but also distinguishes the presence of an attack accurately.

A Review of Intrusion Detection Systems in Cloud Computing

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.

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 TC-CNN: A Tree Classified model using AI for identifying malware intrusions in open Networks

2021 ◽  
Vol 58 (2) ◽  
pp. 6561-6573
Author(s):  
P. Ramachandran , Dr. R. Balasubramanian
Keyword(s):  
Information And Communications Technology ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Threat Detection ◽  
Communications Technology ◽  
Detection Systems ◽  
Open Networks ◽  
Multiple Devices ◽  
Internet Medium ◽  
Network Server

Proliferation of the internet by multiple devices has led to dramatic increases in network traffic.  The Internet medium has also been growing with this usage, but this fast growth has also resulted in new threats making networks vulnerable to intruders and attackers or malicious users. This has made network security an important factor due to excessive usage of ICT (Information and Communications Technology) as threats to IVTs has also grown manifold. Securing data is a major issue, especially when they are transmitted across open networks. IDSs (Intrusion Detection Systems)  are methods or techniques or algorithm which cater to detection of intrusions while on transit. IDSs are useful in identifying harmful operations. Secure automated threat detection and prevention is a more effective procedure to reduce workloads of monitors by scanning the network, server functions and inform monitors on suspicious activity. IDSs monitor systems continually in the angle of threat. This paper’s proposed technique detects suspicious activities using AI (Artificial Intelligence) and analyzes networks concurrently for defense from harmful activities. The proposed algorithm’s experimental results conducted on the UNSW_NB15_training-set shows good performances in terms of accuracy clocking above 96%. 

scholarly journals Aggregating IDS Alerts Based on Time Threshold: Testing and Results

2013 ◽  
Vol 11 (2) ◽  
pp. 2216-2225
Author(s):  
Homam Reda El-Taj
Keyword(s):  
Intrusion Detection ◽  
Threshold Value ◽  
Intrusion Detection Systems ◽  
Security Analysts ◽  
Security Systems ◽  
Detection Systems ◽  
Aggregation Methods ◽  
Extra Effort ◽  
Time Threshold ◽  
Threshold Testing

Every secure system has the possibility to fail. Therefore, extra effort should be taken to protect these systems. Intrusion Detection Systems (IDSs) had been proposed with the aim of providing extra protection to security systems. These systems trigger thousands of alerts per day, which prompt security analysts to verify each alert for relevance and severity based on an aggregation criterion. Several aggregation methods have been proposed to collect these alerts. This paper presents our threshold aggregation system (TAS). Results shows that TAS aggregates IDS alerts accurately based on user demands and threshold value.

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