Visible to the public Biblio

Filters: Author is Venter, H. S.  [Clear All Filters]
Kebande, V. R., Kigwana, I., Venter, H. S., Karie, N. M., Wario, R. D..  2018.  CVSS Metric-Based Analysis, Classification and Assessment of Computer Network Threats and Vulnerabilities. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). :1–10.

This paper provides a Common Vulnerability Scoring System (CVSS) metric-based technique for classifying and analysing the prevailing Computer Network Security Vulnerabilities and Threats (CNSVT). The problem that is addressed in this paper, is that, at the time of writing this paper, there existed no effective approaches for analysing and classifying CNSVT for purposes of assessments based on CVSS metrics. The authors of this paper have achieved this by generating a CVSS metric-based dynamic Vulnerability Analysis Classification Countermeasure (VACC) criterion that is able to rank vulnerabilities. The CVSS metric-based VACC has allowed the computation of vulnerability Similarity Measure (VSM) using the Hamming and Euclidean distance metric functions. Nevertheless, the CVSS-metric based on VACC also enabled the random measuring of the VSM for a selected number of vulnerabilities based on the [Ma-Ma], [Ma-Mi], [Mi-Ci], [Ma-Ci] ranking score. This is a technique that is aimed at allowing security experts to be able to conduct proper vulnerability detection and assessments across computer-based networks based on the perceived occurrence by checking the probability that given threats will occur or not. The authors have also proposed high-level countermeasures of the vulnerabilities that have been listed. The authors have evaluated the CVSS-metric based VACC and the results are promising. Based on this technique, it is worth noting that these propositions can help in the development of stronger computer and network security tools.

Adeyemi, I. R., Razak, S. A., Venter, H. S., Salleh, M..  2017.  High-Level Online User Attribution Model Based on Human Polychronic-Monochronic Tendency. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). :445–450.

User attribution process based on human inherent dynamics and preference is one area of research that is capable of elucidating and capturing human dynamics on the Internet. Prior works on user attribution concentrated on behavioral biometrics, 1-to-1 user identification process without consideration for individual preference and human inherent temporal tendencies, which is capable of providing a discriminatory baseline for online users, as well as providing a higher level classification framework for novel user attribution. To address these limitations, the study developed a temporal model, which comprises the human Polyphasia tendency based on Polychronic-Monochronic tendency scale measurement instrument and the extraction of unique human-centric features from server-side network traffic of 48 active users. Several machine-learning algorithms were applied to observe distinct pattern among the classes of the Polyphasia tendency, through which a logistic model tree was observed to provide higher classification accuracy for a 1-to-N user attribution process. The study further developed a high-level attribution model for higher-level user attribution process. The result from this study is relevant in online profiling process, forensic identification and profiling process, e-learning profiling process as well as in social network profiling process.

Mohlala, M., Ikuesan, A. R., Venter, H. S..  2017.  User Attribution Based on Keystroke Dynamics in Digital Forensic Readiness Process. 2017 IEEE Conference on Application, Information and Network Security (AINS). :124–129.

As the development of technology increases, the security risk also increases. This has affected most organizations, irrespective of size, as they depend on the increasingly pervasive technology to perform their daily tasks. However, the dependency on technology has introduced diverse security vulnerabilities in organizations which requires a reliable preparedness for probable forensic investigation of the unauthorized incident. Keystroke dynamics is one of the cost-effective methods for collecting potential digital evidence. This paper presents a keystroke pattern analysis technique suitable for the collection of complementary potential digital evidence for forensic readiness. The proposition introduced a technique that relies on the extraction of reliable behavioral signature from user activity. Experimental validation of the proposition demonstrates the effectiveness of proposition using a multi-scheme classifier. The overall goal is to have forensically sound and admissible keystroke evidence that could be presented during the forensic investigation to minimize the costs and time of the investigation.

Ikuesan, A. R., Venter, H. S..  2017.  Digital Forensic Readiness Framework Based on Behavioral-Biometrics for User Attribution. 2017 IEEE Conference on Application, Information and Network Security (AINS). :54–59.

Whilst the fundamental composition of digital forensic readiness have been expounded by myriad literature, the integration of behavioral modalities have not been considered. Behavioral modalities such as keystroke and mouse dynamics are key components of human behavior that have been widely used in complementing security in an organization. However, these modalities present better forensic properties, thus more relevant in investigation/incident response, than its deployment in security. This study, therefore, proposes a forensic framework which encompasses a step-by-step guide on how to integrate behavioral biometrics into digital forensic readiness process. The proposed framework, behavioral biometrics-based digital forensics readiness framework (BBDFRF) comprised four phases which include data acquisition, preservation, user-authentication, and user pattern attribution phase. The proposed BBDFRF is evaluated in line with the ISO/IEC 27043 standard for proactive forensics, to address the gap on the integration of the behavioral biometrics into proactive forensics. BBDFRF thus extends the body of literature on the forensic capability of behavioral biometrics. The implementation of this framework can be used to also strengthen the security mechanism of an organization, particularly on continuous authentication.

Kebande, V. R., Karie, N. M., Venter, H. S..  2017.  Cloud-Centric Framework for Isolating Big Data as Forensic Evidence from IoT Infrastructures. 2017 1st International Conference on Next Generation Computing Applications (NextComp). :54–60.

Cloud computing paradigm continues to revolutionize the way business processes are being conducted through the provision of massive resources, reliability across networks and ability to offer parallel processing. However, miniaturization, proliferation and nanotechnology within devices has enabled digitization of almost every object which eventually has seen the rise of a new technological marvel dubbed Internet of Things (IoT). IoT enables self-configurable/smart devices to connect intelligently through Radio Frequency Identification (RFID), WI-FI, LAN, GPRS and other methods by further enabling timeously processing of information. Based on these developments, the integration of the cloud and IoT infrastructures has led to an explosion of the amount of data being exchanged between devices which have in turn enabled malicious actors to use this as a platform to launch various cybercrime activities. Consequently, digital forensics provides a significant approach that can be used to provide an effective post-event response mechanism to these malicious attacks in cloud-based IoT infrastructures. Therefore, the problem being addressed is that, at the time of writing this paper, there still exist no accepted standards or frameworks for conducting digital forensic investigation on cloud-based IoT infrastructures. As a result, the authors have proposed a cloud-centric framework that is able to isolate Big data as forensic evidence from IoT (CFIBD-IoT) infrastructures for proper analysis and examination. It is the authors' opinion that if the CFIBD-IoT framework is implemented fully it will support cloud-based IoT tool creation as well as support future investigative techniques in the cloud with a degree of certainty.

Masvosvere, D. J. E., Venter, H. S..  2015.  A model for the design of next generation e-supply chain digital forensic readiness tools. 2015 Information Security for South Africa (ISSA). :1–9.

The internet has had a major impact on how information is shared within supply chains, and in commerce in general. This has resulted in the establishment of information systems such as e-supply chains amongst others which integrate the internet and other information and communications technology (ICT) with traditional business processes for the swift transmission of information between trading partners. Many organisations have reaped the benefits of adopting the eSC model, but have also faced the challenges with which it comes. One such major challenge is information security. Digital forensic readiness is a relatively new exciting field which can prepare and prevent incidents from occurring within an eSC environment if implemented strategically. With the current state of cybercrime, tool developers are challenged with the task of developing cutting edge digital forensic readiness tools that can keep up with the current technological advancements, such as (eSCs), in the business world. Therefore, the problem addressed in this paper is that there are no DFR tools that are designed to support eSCs specifically. There are some general-purpose monitoring tools that have forensic readiness functionality, but currently there are no tools specifically designed to serve the eSC environment. Therefore, this paper discusses the limitations of current digital forensic readiness tools for the eSC environment and an architectural design for next-generation eSC DFR systems is proposed, along with the system requirements that such systems must satisfy. It is the view of the authors that the conclusions drawn from this paper can spearhead the development of cutting-edge next-generation digital forensic readiness tools, and bring attention to some of the shortcomings of current tools.