Scielo RSS <![CDATA[SAIEE Africa Research Journal]]> vol. 108 num. 2 lang. en <![CDATA[SciELO Logo]]> <![CDATA[<b>Using automated keyword extraction to facilitate team discovery in a digital forensic investigation of electronic communications</b>]]> A major problem that often occurs in Digital Forensics (DF) is the huge volumes of data that has to be searched, filtered, and indexed to discover patterns that could lead to forensic evidence. The nature of, and the process by which the data gets collected, implies that the data also contain information about persons that are not implicated, or only incidentally involved in the crime under investigation. Privacy is therefore an important issue that needs to be managed in a DF investigation. This paper shows that techniques used in the Team Formation (TF) task can be successfully applied to address both the problems of data volume and privacy. The TF task can be re-formulated to fit the DF arena: to commit a crime, the culprit(s) may require the assistance of several other individuals, which implies that a team of some sort gets established. During a post-mortem DF analysis, an investigator may only have one, or a few names to start with. One of the key challenges is finding possible co-conspirators. From a TF point of view, the culprit is trying to find the best team to commit the crime, given some constraints. The TF task in DF requires the recording of skill-sets, and the generation and/or discovery of a graph depicting interaction between candidates. If the data consist of an email corpus and peoples' roles in an organisation (such as in the Enron data), both of these are readily available. In this paper we consider the TF problem in general and extend it to the DF arena by considering the information that an investigator may have access to during the investigation. We also show that simple information retrieval and keyword extraction techniques (such as RAKE) can be used to automatically discover potential teams from the data, while preserving privacy; results from a series of experiments (using the new definitions of TF and the proposed information retrieval techniques) on the Enron data is then presented. <![CDATA[<b>Personal information and regulatory requirements for direct marketing: a South African insurance industry experiment</b>]]> The processing of personal information by companies should be in line with ethical and regulatory requirements. Whilst respecting the right to privacy, personal information can be used to create value in the economy as well as on an individual level by tailoring and targeting services. However, personal information should not be processed under false pretences for the purposes of direct marketing. Data protection regulations, such as the Protection of Personal Information Act (PoPI) 2013, regulate the processing of personal information. Accordingly, companies domiciled in South Africa have to comply with the conditions of PoPI and must process personal information in line with the agreed purpose. PoPI will have an impact on direct marketing and certain conditions will apply to protect individuals' personal information, as well as how and by whom it is used. This research sets out to investigate whether companies in the insurance industry are complying with the direct marketing conditions of PoPI pertaining to opt in and opt out preferences as well as a few other aspects. An experiment was conducted in South Africa whereby two new cellphone numbers and six new e-mail addresses were deposited in the economy by requesting online insurance quotes from twenty different insurance companies. For half of the online insurance quotes the researchers elected to opt in for direct marketing and for the other half to opt out. Any communication received on the cellphone numbers or e-mail addresses was recorded and analysed to establish if the preferences expressed were being complied with. The results indicate that data was shared and possibly leaked; this finding was based on the number of contacts received from companies that were not part of the sample. It was found that opt out preferences for direct marketing were not honoured by some companies. Other aspects, such as the availability of the option to opt in or opt out for direct marketing when depositing personal information on websites, secure processing of personal information and the use of privacy disclaimers, were also found to be lacking in some instances. This indicates that the insurance industry in South Africa might not yet be fully compliant with the requirements for direct marking, as required by PoPI and the Consumer Protection Act (CPA). The results of the research can be used to improve direct marketing interactions with consumers, helping to ensure not only compliance with PoPI, but also the maintenance of a trusting relationship by respecting privacy. <![CDATA[<b>Specific emitter identification for enhanced access control security</b>]]> The application of specific emitter identification (SEI) to access control using radio-frequency (RF) access remotes is presented. Existing RF access remotes are vulnerable to a number of attacks including replay attacks due to their reliance on digital codes. SEI can overcome many vulnerabilities by exploiting the effect of hardware tolerances on the analogue signals transmitted by access remotes. A proof-of-concept SEI system was developed to investigate whether it is possible to distinguish between the RF signals produced by nominally-identical access remotes. It was determined that it is possible to distinguish between the access remotes with an accuracy of 98% with no false positives, even when tested against unknown remotes with the correct digital code and replay attacks. <![CDATA[<b>MANET reactive routing protocols node mobility variation effect in analysing the impact of black hole attack</b>]]> MANETs are exp osed to numerous security threats due to their characteristic features, which include absence of centralised control unit, open communication media, infrastructure-less and dynamic topology. One of commonest attack is known as black hole attack, which mostly targets the MANETs reactive routing protocols, such as AODV and DSR. Simulation scenarios of AODV and DSR based MANET were conducted using Network Simulator 2 (NS-2) and NS-3, while introducing the black hole attack in each of the scenarios, to analyse the protocols' performances. The different scenarios are generated by changing the mobility (locations) of the nodes. The performance metrics that are used to do the analysis are throughput, end-to-end delay and packet delivery ratio. The simulation results showed that the performance of both AODV and DSR degrades in the presence of black hole attack. Throughput and packet delivery ratio decrease when the network is attacked by black hole, because the malicious node absorbs or discards some of the packets. End-to-end delay is also reduced in the presence of a black hole attack because a malicious node pretends to have a valid route to a destination without checking the routing table, and therefore shortens the route discovery process. The results also showed that throughput decreases slightly when mobility of the nodes is increased in the network. The increase in the speed of the nodes decreases both packet delivery ratio and end-to-end delay. The closer the black hole node was to the source node requesting the transmission, the worse the impact. A focused analysis on AODV indicates that, even with the introduction of relatively few black hole nodes to the network, there still exist a potential to bring significant disruptions to communication.