Figure 1 - uploaded by Goni Ibrahim
Content may be subject to copyright.
![Digital forensics science [50].](profile/Goni-Ibrahim/publication/354657827/figure/fig1/AS:1069052583743488@1631893406651/Digital-forensics-science-50.png)
Digital forensics science [50].
![Figure 1. Digital forensics science [50].](profile/Goni-Ibrahim/publication/354657827/figure/fig1/AS:1069052583743488@1631893406651/Digital-forensics-science-50_Q320.jpg)
Similar publications
Cyber attacks on the internet have become increasingly sophisticated and frequent, posing significant challenges to cybersecurity. Traditional rule-based methods for detecting these attacks often struggle to keep pace with the evolving tactics of malicious actors. In this context, machine learning (ML) techniques have emerged as a promising approac...
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we co...
Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance,...
The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessi...
Citations
... Future studies could investigate a broader range of physical-world attacks to enhance the universality and relevance of these insights in different contexts. Goni et al. (2020) present a systematic review focusing on the application of machine learning (ML) algorithms in cybersecurity and cyber forensics. Their work highlights the critical aspects of confidentiality, integrity, and validity of data within cybersecurity. ...
In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.
... Supervised learning excels in classification and regression issues. The goal of supervised learning is to make meaning out of data in the context of a given topic [13]. Supervised learning was proposed by some researchers to improve DFIs in smart environments, as mentioned in Section 3. ...
... In contrast to supervised learning, unsupervised learning is presented with unlabelled data and is designed to detect patterns or similarities on its own. In other words, unsupervised learning techniques include two types: clustering and association, which find all kinds of unknown patterns in data and help to find features that can be useful for categorisation [13]. ...
Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, they significantly slow down or even incapacitate the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine-learning (ML) techniques might be a viable option in smart environments, this paper presents the integration of ML into DF, through reviewing the most recent papers concerned with the applications of ML in DF, specifically within smart environments. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process.
... Collaboration between stakeholders is necessary to overcome challenges and further progress in the field of ML in cybersecurity. Goni, Ibrahim, et al (2020) conducted research on the Machine Learning Approach to Cybersecurity and Cyber Forensics. With the widespread adoption of cloud computing and the internet of things, nations worldwide have become increasingly connected through global networks. ...
Artificial intelligence (AI) has the potential to change the world of cybersecurity by delivering automated incident response capabilities. AI algorithms can process massive volumes of data in real-time, discover abnormalities and potential risks, and respond to occurrences, saving human security analysts time and effort. AI-based incident response systems can be trained to recognize patterns of behavior that indicate a security breach and respond appropriately, decreasing the risk of damage to the enterprise. The application of AI in incident response can also increase the accuracy and speed of event investigations, allowing security professionals to quickly contain and resolve occurrences. This research will examine the advantages of employing AI for automated incident response in cybersecurity, as well as the hurdles that must be solved.
... Supervised learning excels in classification and regression issues. The goal of supervised learning is to make meaning of data in the context of a given topic [10]. Supervised learning was proposed by some researchers to improve DFIs in smart environments, as mentioned in section 3. ...
... In contrast to supervised learning, unsupervised learning is presented with unlabelled data and is designed to detect patterns or similarities on its own. In other words, unsupervised learning techniques include two types: clustering and association, which find all kinds of unknown patterns in data and help to find features that can be useful for categorisation [10]. ...
According to the wide variety of internet of things (IoT) devices within smart environments, many challenges face conventional digital forensic investigation (DFI) in smart environments. Challenges in this environment include heterogeneity, distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, it significantly slows down or even incapacitates the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine learning (ML) techniques might be a viable option in certain situations, this paper presents the integration of ML into DF. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process.
... However, several organizations, including the United Nations, have begun to define cybercrime (United Nations). Cybercrime is defined by the United Nations as any illegal behavior committed through the provision of a computer system or system or network, including crimes such as illegal possession, provision, or distribution of information via a computer system or network [48,49]. Cybercrime is defined as a crime committed with the use of information technology as an instrument or target, and digital forensics essentially answers the following questions: when, what, who, where, how, and why it is committed [25]. ...
... Future studies could investigate a broader range of physical-world attacks to enhance the universality and relevance of these insights in different contexts. Goni et al. (2020) present a systematic review focusing on the application of machine learning (ML) algorithms in cybersecurity and cyber forensics. Their work highlights the critical aspects of confidentiality, integrity, and validity of data within cybersecurity. ...
... Costantini et al. [7] state that the use of machine learning classifications enables us to analyze interference obtained from electronic devices. Goni et al. [8] discuss machine learning as cyber security tool to deal with network security, data security, endpoint security, identity access security, cloud security, IoT security as well as Fog security. ...
... The highest average predictive performance obtained by the proposed scheme is 94.43%. M.A. Tocoglu et al. [38] proposed a sentimental analysis method to extract subjective information in the source material. Using this method, it encountered an overwhelming amount of data available. ...
The crime rate in India is considerably increasing day by day. Consequently, the data associated with crime is also increasing, opening doors for data-driven approaches to these data to extract insightful knowledge, which can help police and other law enforcement organizations of the country in crime control and prevention. Crime prediction using machine learning algorithms on crime data can predict region-wise crime counts. In this paper, a machine learning-based soft computing regression analysis approach for Indian Crime Data Analysis (ICDA) is proposed. Different regression algorithms, namely, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Random Forest Regression (RFR) are uses to build regression models.
