3 research outputs found
Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network
No full textThe modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks and attacks. A well-known technique of detecting these attacks and unusual events is by using intrusion detection systems. Anomalies in the network occur at unknown points and produce abrupt changes in the statistical features of the message stream. In this paper, we propose an anomaly-based intrusion detection approach using the cumulative sum (CUSUM) change-point detection algorithm to detect data injection attacks on the controller area network (CAN) bus. We leverage the parameters required for the change-point algorithm to reduce false alarm rate and detection delay. Using real dataset generated from a car in normal operation, we evaluate our detection approach on three different kinds of attack scenarios.This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. Olufowobi, Habeeb, Uchenna Ezeobi, Eric Muhati, Gaylon Robinson, Clinton Young, Joseph Zambreno, and Gedare Bloom. "Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network." (2019). DOI: 10.1145/3309171.3309178.</p
Factors affecting cyber-security in Kenya – A Case of Small Medium Enterprises
No full textSubmitted in Partial Fulfillment of the Requirements for The Degree of Master of Business Administration.Computer systems have translated data to be world’s new basis for competitive advantage, a platform threatened by cyber-crime. With increased cyber-threats, cyber-security is no longer frivolous and requires attention from both large and small enterprises. Small Medium Enterprises (SMEs) are worse hit by cyber-crimes due to limited resources addressing emerging cyber-threats. Economics place challenges facing cyber-security into perspective better than pure technical approaches, with studies indicating cyber-security responsibilities and liabilities heavily plagued by leadership and legislation.
This study sought to explore coherent factors influencing business strategies across different SME industries trying to fix cyber-insecurity. In pursuing this goal, the study assessed economic factors considered critical for creating a safe and secure cyber-space business environment for SMEs including leadership and government policies. The researcher hopes the research results will provide better understanding of SME cyber behaviors and guide the development of appropriate solutions to SMEs cyber-space challenges.
The study is a descriptive research with surveys on components showing cyber-security factors contributing, or not contributing to reduced cyber-threat effects. Analysis was done using the statistical software-SPSS. The study targeted a 95% confidence interval for the analyzed sample. Further, a Cronbach Alpha test was used to assess reliability and correlational analysis while testing for significant relationships from data collected
Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
No full textThe exponential growth in data volumes, combined with the inherent complexity of network algorithms, has drastically affected network security. Data activities are producing voluminous network logs that often mask critical vulnerabilities. Although there are efforts to address these hidden vulnerabilities, the solutions often come at high costs or increased complexities. In contrast, the potential of open-source tools, recognized for their security analysis capabilities, remains under-researched. These tools have the potential for detailed extraction of essential network components, and they strengthen network security. Addressing this gap, our paper proposes a data analytics-driven network anomaly detection model, which is uniquely complemented with a visualization layer, making the dynamics of cyberattacks and their subsequent defenses distinctive in near real-time. Our novel approach, based on network scanning tools and network discovery services, allows us to visualize the network based on how many IP-based networking devices are live, then we implement a data analytics-based intrusion detection system that scrutinizes all network connections. We then initiate mitigation measures, visually distinguishing malicious from benign connections using red and blue hues, respectively. Our experimental evaluation shows an F1 score of 97.9% and a minimal false positive rate of 0.3% in our model, demonstrating a marked improvement over existing research in this domain
