Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
The use of deception systems is a viable option in reducing the never-ending tussle between the a... more The use of deception systems is a viable option in reducing the never-ending tussle between the attackers and the defenders. The deception systems give the defenders an edge over their counterparts since they provide the platform to learn the methods and techniques the attackers use. However, the effectiveness of the deception system is highly dependent on how they truly hide their identity. A deceptive honeypot has the capacity to persuade and change the cognitive behavior of an attacker. An attacker whose cognitive behavior has been altered by the deception capabilities of a honeypot is more likely to reveal his attack methods; hence, the defenders are able to learn how to defend against those future attacks.
Background The high rise in electronic devices in modern-day society has resulted in crimes in cy... more Background The high rise in electronic devices in modern-day society has resulted in crimes in cyber-related crimes as criminals resort to hacking, illegal use of these devices. This is primarily due to perceived high rewards and low chances of being apprehended. The rise in cyber crimes poses a significant challenge to forensic investigators as now they have to process huge volumes of data from a variety of sources within a limited time. This results in investigators taking longer to process cases and in some instances missing links as they deal with data from a variety of sources. Findings In this paper, we provide a definition of big data forensics, and then we discuss the challenges associated with digital forensics investigations when dealing with big data. We provide details on how volume, variety, and velocity all pose a huge challenge in digital forensics investigations. We then discuss how a novel solution called Forensic Cloud Environment (FCE) leverages the power of Hadoo...
The security of knowledge is of extreme importance to any organisation, as organizations are incr... more The security of knowledge is of extreme importance to any organisation, as organizations are increasingly becoming dependent on knowledge assets as a primary source of competitive advantage. Howeve ...
Machine learning has been the corner stone in analysing and extracting information from data and ... more Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluat...
Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
The use of deception systems is a viable option in reducing the never-ending tussle between the a... more The use of deception systems is a viable option in reducing the never-ending tussle between the attackers and the defenders. The deception systems give the defenders an edge over their counterparts since they provide the platform to learn the methods and techniques the attackers use. However, the effectiveness of the deception system is highly dependent on how they truly hide their identity. A deceptive honeypot has the capacity to persuade and change the cognitive behavior of an attacker. An attacker whose cognitive behavior has been altered by the deception capabilities of a honeypot is more likely to reveal his attack methods; hence, the defenders are able to learn how to defend against those future attacks.
Background The high rise in electronic devices in modern-day society has resulted in crimes in cy... more Background The high rise in electronic devices in modern-day society has resulted in crimes in cyber-related crimes as criminals resort to hacking, illegal use of these devices. This is primarily due to perceived high rewards and low chances of being apprehended. The rise in cyber crimes poses a significant challenge to forensic investigators as now they have to process huge volumes of data from a variety of sources within a limited time. This results in investigators taking longer to process cases and in some instances missing links as they deal with data from a variety of sources. Findings In this paper, we provide a definition of big data forensics, and then we discuss the challenges associated with digital forensics investigations when dealing with big data. We provide details on how volume, variety, and velocity all pose a huge challenge in digital forensics investigations. We then discuss how a novel solution called Forensic Cloud Environment (FCE) leverages the power of Hadoo...
The security of knowledge is of extreme importance to any organisation, as organizations are incr... more The security of knowledge is of extreme importance to any organisation, as organizations are increasingly becoming dependent on knowledge assets as a primary source of competitive advantage. Howeve ...
Machine learning has been the corner stone in analysing and extracting information from data and ... more Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluat...
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