Please cite this article in press as: W. Bul'ajoul et al., Improving network intrusion detection system performance through quality of service configuration and parallel technology, J. Comput. Syst. Sci. (2015), http://dx.

No abstract

This paper presents an investigation, involving experiments, which shows that current network intrusion, detection, and prevention systems (NIDPSs) have several shortcomings in detecting or preventing rising unwanted traffic and have several threats in high-speed environments. It shows that the NIDPS performance can be weak in the face of high-speed and high-load malicious traffic in terms of packet drops, outstanding packets without analysis, and failing to detect/prevent unwanted traffic. A novel quality of service (QoS) architecture has been designed to increase the intrusion detection and prevention performance. Our research has proposed and evaluated a solution using a novel QoS configuration in a multi-layer switch to organize packets/traffic and parallel techniques to increase the packet processing speed. The new architecture was tested under different traffic speeds, types, and tasks. The experimental results show that the architecture improves the network and security performance which is can cover up to 8 Gb/s with 0 packets dropped. This paper also shows that this number (8Gb/s) can be improved, but it depends on the system capacity which is always limited.

Internet of Medical Things is the internet connection of medical devices to perform services and processes to support the healthcare sector. Wearable Technology in Healthcare has seen tremendous growth in recent times. This is due to a global increase in the aging population, the need for disease management, and effective patient monitoring. The prevalent technology of wearable devices is Bluetooth technology due to its low cost, low energy, and size. Despite the growth recorded in the adoption of Bluetooth Wearable IoMT, there are concerns by users and other healthcare stakeholders about security and privacy issues with its adoption. Our paper presents a simulation of passive and active attacks on 3 wearable IoMT devices, followed by analysis and evaluation of the experiment outcomes. Thereafter, countermeasures for the identified weaknesses were provided. It was discovered that some of the standard security features of Bluetooth Technology to mitigate privacy and security issues were not implemented in some of the devices, which can result in data compromise in the devices. A security assessment framework was developed to assess the security of Bluetooth IoMT devices using the Bayesian Network model. This is used to rank devices, identify their vulnerabilities, and apply security measures on the identified vulnerabilities. Our paper further provides recommendations on improving the security of Bluetooth IoMT devices.

The advantages of economic growth and increasing ease of operation afforded by e-business and ecommerce developments are unfortunately matched by growth in cyberattacks. This paper outlines the common attacks faced by e-business and describes the defenses that can be used against them. It also reviews the development of newer security defense methods. These are: (1) biometrics for authentication; parallel processing to increase power and speed of defenses; (2) data mining and machine learning to identify attacks; (3) peer-to-peer security using blockchains; (4) enterprise security modelling and security as a service; and (5) user education and engagement. The review finds overall that one of the most prevalent dangers is social engineering in the form of phishing attacks. Recommended counteractions include education and training, and the development of new machine learning and data sharing approaches so that attacks can be quickly discovered and mitigated.