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Information Security Education Journal  

Volume: 11, Issue: 1 (  June   2024)
Current Issue
Editorial Message
Research
Increasing the Value of Intrusion Detection Systems (IDS) in High-Load Environments
Michael Vogel and Sebastian Schmerl
Page: 1-7
Abstract_    Full_Text   1.48 MB   Download:   5  times
https://doi.org/10.6025/isej/2024/11/1/1-7
Abstract: Intrusion detection systems (IDS) are valuable tools for addressing current attacks on computers and networks. However, as network and end system performance increases and IT systems become more complex, so does the volume of observed data and large signature bases. As a result, IDS often fail to detect attacks in high-load environments, allowing attackers to act undetected. This work presents an efficient, dynamically adaptive, distributed approach for multi-spatial IDS. Additionally, we discuss first performance evaluations of prototype implementation and propose future research directions that hold great promise for enhancing network security in high-load environments.
Enhancing Web Architecture to Counter Cloud Security Attacks
Vlad Andrei Poenaru, George Suciu, Cristian George Cernat, Gyorgy Todoran and Traian Lucian Militaru
Page: 8-12
Abstract_    Full_Text   1.28 MB   Download:   35  times
https://doi.org/10.6025/isej/2024/11/1/8-12
Abstract: Cloud technology is becoming a prominent tool for delivering web applications. However, research is scarce on the impact of Distributed Denial of Service (DDOS) on clouds and how various attack methods compare to robust cloud designs. Can cloud technology withstand these attacks, or will traditional web architectures, enhanced with efficient caching and reverse proxy solutions, consistently outperform? This study addresses these questions by evaluating two architectural models: SlapOS and a setup featuring Nginx at the front and several web servers behind it, under various attack scenarios such as DDOS, Slowloris, and RA Flood attacks.
A Strategy For Spotting Irregularities in Unix Shell Activities Using a Pre-trained DistilBERT Model in Computer Security
Zefang Liu, John F. Buford
Page: 13-28
Abstract_    Full_Text   3.11 MB   Download:   27  times
https://doi.org/10.6025/isej/2024/11/1/13-28
Abstract: Identifying irregularities in command shell activities is crucial to maintaining computer security. Significant progress has been made in deep learning and natural language understanding, especially with transformer-based models, which have shown considerable potential in tackling intricate security issues. In this study, we present a thorough strategy for spotting irregularities in Unix shell activities using a pre-trained DistilBERT model, combining both unsupervised and supervised learning methods to spot unusual activities while reducing the need for manual labeling of data. The unsupervised technique focuses on understanding Unix shell commands basic structure and grammar, allowing for the recognition of deviations from typical behavior. Trials on a large-scale dataset from real-world systems have proven the effectiveness of our strategy in identifying unusual activities in Unix shell sessions. This research underscores the possibility of using recent breakthroughs in transformer technology to tackle significant security issues in computing.
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