Diverging deep learning cognitive computing techniques into cyber forensics (original) (raw)

A Review on Application of Deep Learning in Cyber Forensics

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

In recent times, most of the data such as books, personal materials and genetic information digitally. This transformation gives rise to a field of Cyberspace. This newly created space, gave rise to a new set of crime Cybercrime, this lead to the development of securing the cyberspace and protecting against cybercrime. As more people started using cyberspace, more number of cybercrime were registered. As the number of crime increases, we are in need of help from Machine Learning. Machine Learning in the field of Cyber forensics, is a boon. In this paper we have an overview of Machine Learning in the field of Cyber Forensics and various method of it implementation. I. INTRODUCTION 1) Deep Learning and Machine Learning: The field of Artificial Intelligence and Machine Learning has been around since a long time but it is now that we have enough computational power to effectively develop strong artificial neural networks (ANN) in a reasonable time frame with the help of strong hardware and software support. The most important aspect of Cyber security involves protecting key data and devices from cyber threats. It's an important part of corporations that collect and maintain large databases of client data, social platforms wherever personal information were submitted and also the government organizations wherever secret, political and defence information comes into measure. Unlike the traditional machine learning algorithm that uses feature engineering and illustration ways. They will chose the best options by themselves. 2) Cyber Security: Cyber security is that the set of framework and processes designed to protect computers, networks, programs, and data from attack, unauthorized access, change, or destruction. These frameworks are consist of network security and host security systems, every of those has a minimum firewall, antivirus computer code, associated an intrusion detection system. 3) Deep Learning and Cyber Security: This survey summarizes the association of cyber security and Deep learning techniques (DL). Deep learning technique are being used by researchers in recent days. Deep learning can be used alongside the prevailing automation ways like rule and heuristics based and machine learning techniques. This survey helps is understand the benefits of deep learning algorithms to classify and tackle malicious activities that perceived from the varied sources like DNS, email, URLs etc. In recent days, non-public firms and public establishments are dealing with constant and complicated cyber threats and cyberattacks. As a precaution, organizations should build and develop a cybersecurity culture and awareness so as to defend against cyber criminals. 4) Shared Task: In this shared task conference, the train data set will be distributed among the participants and the train model will be evaluated based on the test data set. This is most common in NLP area recently shared task on identifying phishing email has been organized by. The details of the submitted runs are available throughout. Followed by shared task on detecting malicious domain organized intruders. These two shared tasks enables the participants to share their approach through working notes or system description paper. Each year there is one more shared task conducted by CDMC. But they don't provide us an option to submit system description papers. But recently they are giving an option to submit system description papers (CDMC 2018). One significant issue was that the available data sets are very old and each data set has their own limitations. The main issue we face now is due to the non-maintenance of Cybercrime data. To overcome such issues a brief investigative issue made to understand the need of Security domain, datasets and key feature of data sciences is discussed in for problems employing the data science towards cyber security. The need for such dataset in to be promoted.

A comprehensive analysis of the role of AI and ML in modern digital forensics and incident response

Forensic Science International, 2024

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.

Cybersecurity and Cyber Forensics: Machine Learning Approach

Semiconductor Science and Information Devices, 2020

We live in a connected world of digital devices which include mobile devices, workstations, control systems, transportation systems, base stations, satellites of different interconnected networks, Global positioning system (GPS) with their associated e-services in which internet provide platform for the connection of this devices worldwide. cyber forensics as a sub-branch of computer security that uses software and predefined techniques which is aim at extracting evidences from any form of digital device and can be presented to a court of law for criminal and/or civil proceedings provided that it satisfy this three conditions; comprehensiveness, authenticity and objectivity. Cyber space is recently considered a domain worth exploring and investigating and securing after lithosphere, hydrosphere, biosphere and atmosphere. Cyber threats, attacks and breaches have become a normal incident in day-to-day life of internet users. However, it is noted that cybersecurity is based on confiden...

Review on effectiveness of deep learning approach in digital forensics

Photosynthesis optimization, 2022

Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.

"The Evolving Roles and Applications of Artificial Intelligence in Digital Forensics"

International Journal of All Research Education and Scientific Methods, 2021

Today it is globally known that it is a modern era of Artificial Intelligence. There is a great impact of Artificial Intelligence on the society as we are surrounded by technology. It is being observed that in Today's time, Artificial Intelligence has turned out to be technically friendly due to increasing demand in the Tech Industry. Artificial intelligence may greatly increase the efficiency of the existing economy. But it may have an even larger impact by serving as a new general-purpose "method of invention" that can reshape the nature of the innovation process and the organization of R&D. Artificial Intelligence holds the ability to search, accumulate, analyse, manipulate and make decisions based on interpretations done. Digital Forensics is now adopting this technology to spot the patterns of online security attacks and threats that can be caused to the user browsing online on servers. These patterns may include Browsing History of servers, Credit Checks, Purchasing Histories, Online Payments and Transactions made by the user etc. To secure the users from various cyber threats and attacks we need to move to a new Tech Gateway for Cyber Forensics-"Artificial Intelligence". Today various Government Organizations like Police Cyber Cell, Joint Cipher Bureau, CBI Cyber Cell etc. are standing on the bleeding fringe of AI. The current globalization and technological advancements have meant that individual users, companies and organizations frequently need to adapt their internal cyber structure and operating cyber processes, to demonstrate efficiency and effectiveness of AI, especially when considering aspects that are beyond Tech Crimes, such as Cyber Hacking, User Account Penetration etc. To cope with that, this work proposes an alternative to model the evolution of AI in the field of Cyber Forensics. This paper highlights the role of Artificial Intelligence particularly in field of Digital Forensics, its application in different areas of Digital Forensic Processes and statistical facts relevant to this domain.

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

KI - Künstliche Intelligenz

The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengt...