Journal articles: 'Network forensic framework analysis'

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Qureshi, Sirajuddin, Jianqiang Li, Faheem Akhtar, Saima Tunio, Zahid Hussain Khand, and Ahsan Wajahat. "Analysis of Challenges in Modern Network Forensic Framework." Security and Communication Networks 2021 (August 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/8871230.

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Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.

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Haggerty, John, Alexander J. Karran, David J. Lamb, and Mark Taylor. "A Framework for the Forensic Investigation of Unstructured Email Relationship Data." International Journal of Digital Crime and Forensics 3, no. 3 (July 2011): 1–18. http://dx.doi.org/10.4018/jdcf.2011070101.

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The continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation.

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N, Karthikeyan, and Godwin Ponsam J. "Differentiation of Natural and Maliciously Induced Packet Loss in Wireless Network Using Forensic Analysis." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 354. http://dx.doi.org/10.14419/ijet.v7i4.6.28440.

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Wireless networks are prone to packet loss making it strenuous to differentiate if data leakage is due to the physical nature of wireless networks or from malicious packet loss.All previous experiments were made on utilizing nodes which are part of network to monitor packet loss , a method deployed in passive detection.Due to high levels of interference the likelihood of classifying malicious packet loss from wireless induced packet loss is less probable.In this paper using certain transmission parameters like traffic intensity ,node density and transmission evidence we perform forensic analysis. By using an analytical framework we compute the transmission evidence.We validate our analytical framework via both simulation and wireless test-beds.The analytical framework is then used as a basis for a protocol within a forensic analyser to assess the cause of packet loss and determining the likelihood of forwarding misbehaviours.

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Dumchykov, M. "THE PROCESSES OF DIGITIZATION AND FORENSICS: A RETROSPECTIVE ANALYSIS." Criminalistics and Forensics, no. 65 (May 18, 2020): 100–108. http://dx.doi.org/10.33994/kndise.2020.65.10.

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The article deals with some aspects of digitalization of forensic activity, such as improving the efficiency of search and cognitive activity of an investigator using digital technologies, and effective organization of such activity. Use of computer hardware, software, various computer and digital technologies and scripting to investigate crimes. The definitions of “digital trace” and “virtual trace” demonstrate the complexity of these issues. Certain dynamic movement of criminal activity in the information environment is analyzed. Defines the concept of virtual trace as a specific trace of certain actions in the information space of computer devices and their network systems. In considering the definition of the concept of virtual trace, the views of scientists such as V.A. Meshcheriakov, V.V. Poliakov and Smushkin A.V. The aspects of consideration of forensic digitization are identified, among which the main ones are: – use of digital technologies to increase the effectiveness of search and cognitive activity of the investigator, effective organization of this activity at the present level, optimization of interaction of different bodies, institutions in the investigation of crimes. – the use of information and communication technologies to investigate crimes. – solving didactic tasks in the field of training, retraining, advanced training of investigators, criminal investigators, forensic experts, improvement of their qualification, exchange of experience. The further question of the development of forensic science in the field of information society is investigated. It is determined in what form the information forensics will exist, namely: A new section of classic forensics that focuses on working with digital and virtual footprints. The new science of “digital or virtual forensics”. 3. Independent forensic science within the framework of the general theory of forensic science, which integrates the development of virtual traces, the use of new technical means of finding, fixing, research, use in proving forensically relevant information, technologies that ensure the effectiveness of the investigator.

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Chethana H. T. and Trisiladevi C. Nagavi. "A New Framework for Matching Forensic Composite Sketches With Digital Images." International Journal of Digital Crime and Forensics 13, no. 5 (September 2021): 1–19. http://dx.doi.org/10.4018/ijdcf.20210901.oa1.

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Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.

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Bijalwan, Anchit, Vijender Kumar Solanki, and Emmanuel Shubhakar Pilli. "BOTNET FORENSIC: ISSUES, CHALLENGES AND GOOD PRACTICES." Network Protocols and Algorithms 10, no. 2 (June 28, 2018): 28. http://dx.doi.org/10.5296/npa.v10i2.13144.

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Unethical hacking of sites, probing, click frauds, phishing, denial of services attack and many such malicious practices affects the organizational integrity and sovereignty. Such activities are direct attacks on the safety, security and confidentiality of the organization. These activities put organizational privacy at stake. Botnet forensic is utilized to strengthen the security issues by understanding the modus operandi of the attacks. The available observations can be utilized in future also to prevent a potential threat to network security. This paper enlightens the novel summary of previous survey including life cycle, classification, framework, detection, analysis and the challenges for botnet forensics.

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Khan, Muhammad Naeem Ahmed. "Multi-agent Based Forensic Analysis Framework for Infrastructures Involving Storage Networks." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 89, no. 2 (March 14, 2018): 291–309. http://dx.doi.org/10.1007/s40010-017-0473-3.

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Anton Yudhana, Abdul Fadlil, and Muhammad Rizki Setyawan. "Analysis of Skype Digital Evidence Recovery based on Android Smartphones Using the NIST Framework." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 4 (August 17, 2020): 682–90. http://dx.doi.org/10.29207/resti.v4i4.2093.

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Cybercrime is an activity utilizing electronic devices and network technology as tools or media to commit crimes. One of them uses the Skype application that is installed on the smartphone. In finding evidence from a cybercrime case, a forensic activity known as digital forensic must be carried out. This study aims to recover digital evidence that has been erased using the NIST framework and forensic tools such as Oxygen and Belkasoft. The results of digital evidence recovery from smartphone Samsung J2 in the removal scenario via the application manager, the Oxygen tool cannot recover deleted data and the percentage of success using Belkasoft is 26%. While the results of data recovery with the manual removal method the percentage of success using Oxygen was 63% and Belkasoft was 44%. Digital evidence recovery results from smartphones Andromax A on the erase scenario through the application manager, Oxygen and Belkasoft tools cannot recover deleted data. While manual removal of Oxygen by 61% and Belkasoft cannot restore data. It can be concluded the results of data recovery from both smartphones that are used according to the erasure method through the application manager, Belkasoft has better performance than Oxygen, and data recovery according to the method of erasing manually, Oxygen has better performance than Belkasoft.

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Turner, Adam B., Stephen McCombie, and Allon J. Uhlmann. "Discerning payment patterns in Bitcoin from ransomware attacks." Journal of Money Laundering Control 23, no. 3 (July 5, 2020): 545–89. http://dx.doi.org/10.1108/jmlc-02-2020-0012.

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Purpose The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the authors explore how distinct these patterns are and their potential value for intelligence exploitation in support of countering ransomware attacks. Design/methodology/approach The authors created an analytic framework – the Ransomware–Bitcoin Intelligence–Forensic Continuum framework – to search for transaction patterns in the blockchain records from actual ransomware attacks. Data of a number of different ransomware Bitcoin addresses was extracted to populate the framework, via the WalletExplorer.com programming interface. This data was then assembled in a representation of the target network for pattern analysis on the input (cash-in) and output (cash-out) side of the ransomware seed addresses. Different graph algorithms were applied to these networks. The results were compared to a “control” network derived from a Bitcoin charity. Findings The findings show discernible patterns in the network relating to the input and output side of the ransomware graphs. However, these patterns are not easily distinguishable from those associated with the charity Bitcoin address on the input side. Nonetheless, the collection profile over time is more volatile than with the charity Bitcoin address. On the other hand, ransomware output patterns differ from those associated charity addresses, as the attacker cash-out tactics are quite different from the way charities mobilise their donations. We further argue that an application of graph machine learning provides a basis for future analysis and data refinement possibilities. Research limitations/implications Limitations are evident in the sample size of data taken on ransomware campaigns and the “control” subject. Further analysis of additional ransomware campaigns and “control” subjects over time would help refine and validate the preliminary observations in this paper. Future research will also benefit from the application of more powerful computing resources and analytics platforms that scale with the amount of data being collected. Originality/value This research contributes to the maturity of the field by analysing ransomware-Bitcoin behaviour using the Ransomware–Bitcoin Intelligence–Forensic Continuum. By combining several different techniques to discerning patterns of ransomware activity on the Bitcoin network, it provides insight into whether a ransomware attack is occurring and could be used to trigger alerts to seek additional evidence of attack, or could corroborate other information in the system.

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Reid, Shannon E. "Friendship Group Composition and Juvenile Institutional Misconduct." International Journal of Offender Therapy and Comparative Criminology 61, no. 2 (July 28, 2016): 191–209. http://dx.doi.org/10.1177/0306624x15589737.

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The present study examines both the patterns of friendship networks and how these network characteristics relate to the risk factors of institutional misconduct for incarcerated youth. Using friendship networks collected from males incarcerated with California’s Division of Juvenile Justice (DJJ), latent profile analysis was utilized to create homogeneous groups of friendship patterns based on alter attributes and network structure. The incarcerated youth provided 144 egocentric networks reporting 558 social network relationships. Latent profile analysis identified three network profiles: expected group (67%), new breed group (20%), and model citizen group (13%). The three network profiles were integrated into a multiple group analysis framework to examine the relative influence of individual-level risk factors on their rate of institutional misconduct. The analysis finds variation in predictors of institutional misconduct across profile types. These findings suggest that the close friendships of incarcerated youth are patterned across the individual characteristics of the youth’s friends and that the friendship network can act as a moderator for individual risk factors for institutional misconduct.

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Kim, Hyungchan, Sungbum Kim, Yeonghun Shin, Wooyeon Jo, Seokjun Lee, and Taeshik Shon. "Ext4 and XFS File System Forensic Framework Based on TSK." Electronics 10, no. 18 (September 20, 2021): 2310. http://dx.doi.org/10.3390/electronics10182310.

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Recently, the number of Internet of Things (IoT) devices, such as artificial intelligence (AI) speakers and smartwatches, using a Linux-based file system has increased. Moreover, these devices are connected to the Internet and generate vast amounts of data. To efficiently manage these generated data and improve the processing speed, the function is improved by updating the file system version or using new file systems, such as an Extended File System (XFS), B-tree file system (Btrfs), or Flash-Friendly File System (F2FS). However, in the process of updating the existing file system, the metadata structure may be changed or the analysis of the newly released file system may be insufficient, making it impossible for existing commercial tools to extract and restore deleted files. In an actual forensic investigation, when deleted files become unrecoverable, important clues may be missed, making it difficult to identify the culprit. Accordingly, a framework for extracting and recovering files based on The Sleuth Kit (TSK) is proposed by deriving the metadata changed in Ext4 file system journal checksum v3 and XFS file system v5. Thereafter, by comparing the accuracy and recovery rate of the proposed framework with existing commercial tools using the experimental dataset, we conclude that sustained research on file systems should be conducted from the perspective of forensics.

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Teunis, Peter, Janneke C. M. Heijne, Faizel Sukhrie, Jan van Eijkeren, Marion Koopmans, and Mirjam Kretzschmar. "Infectious disease transmission as a forensic problem: who infected whom?" Journal of The Royal Society Interface 10, no. 81 (April 6, 2013): 20120955. http://dx.doi.org/10.1098/rsif.2012.0955.

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Observations on infectious diseases often consist of a sample of cases, distinguished by symptoms, and other characteristics, such as onset dates, spatial locations, genetic sequence of the pathogen and/or physiological and clinical data. Cases are often clustered, in space and time, suggesting that they are connected. By defining kernel functions for pairwise analysis of cases, a matrix of transmission probabilities can be estimated. We set up a Bayesian framework to integrate various sources of information to estimate the transmission network. The method is illustrated by analysing data from a multi-year study (2002–2007) of nosocomial outbreaks of norovirus in a large university hospital in the Netherlands. The study included 264 cases, the norovirus genotype was known in approximately 60 per cent of the patients. Combining all the available data allowed likely identification of individual transmission links between most of the cases (72%). This illustrates that the proposed method can be used to accurately reconstruct transmission networks, enhancing our understanding of outbreak dynamics and possibly leading to new insights into how to prevent outbreaks.

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Bright, David, and Chad Whelan. "On the relationship between goals, membership and network design in multi-agency “fusion” centres." Policing: An International Journal 42, no. 3 (June 10, 2019): 441–54. http://dx.doi.org/10.1108/pijpsm-05-2018-0070.

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Purpose The purpose of this paper is to adopt an organisational network perspective to examine the structural properties underpinning the design and governance of multi-agency fusion centres and related environments, focussing particularly on how they are formed and internally managed. Design/methodology/approach The authors conducted several focus groups and follow-up interviews with executive and operational members of Australia’s principal fusion centres and related environments. Findings The authors argue that in order to understand the internal dynamics of fusion centres, and the ways in which they form and function, the analysis of interrelationships between partners and potential partners is critical. The authors have demonstrated that a network model can assist in this type of analysis. For example, hub-and-spoke network structures appear to be a particularly effective solution to the centralisation-density trade-off for such inter-agency networks. Originality/value The authors use a novel approach that combines a goal-oriented network framework with the existing literature on fusion centres to synthesise key features of the network structure of fusion centres and associated processes of information sharing.

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Demertzis, Konstantinos, Konstantinos Tsiknas, Dimitrios Takezis, Charalabos Skianis, and Lazaros Iliadis. "Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework." Electronics 10, no. 7 (March 25, 2021): 781. http://dx.doi.org/10.3390/electronics10070781.

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Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

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Akremi, Aymen, Hassen Sallay, Mohsen Rouached, and Rafik Bouaziz. "Applying Digital Forensics to Service Oriented Architecture." International Journal of Web Services Research 17, no. 1 (January 2020): 17–42. http://dx.doi.org/10.4018/ijwsr.2020010102.

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Digital forensics is an emerging research field involving critical technologies for obtaining evidence in digital crime investigations. Several methodologies, tools, and techniques have been developed to deal with the acquisition, preservation, examination, analysis, and presentation of digital evidence from different sources. However, new emerging infrastructures such as service-oriented architecture has brought new serious challenges for digital forensic research to ensure that evidence will be neutral, comprehensive, and reliable in such complex environment is a challenging research task. To address this issue, the authors propose in this article a generic conceptual model for digital forensics methodologies to enable their application in a service-oriented architecture. Challenges and requirements to construct a forensically sound evidence management framework for these environments are also discussed. Finally, the authors show how digital forensics standards and recommendations can be mapped to service-oriented architecture.

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Shao, Chenghui, Qixun Zhang, Yang Song, and Dong Zhu. "Medical Information Forensics System Based on Convolutional Neural Network with Pattern Finding Prior." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1098–104. http://dx.doi.org/10.1166/jmihi.2020.2893.

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Hospital Information System HIS is an indispensable technical support environment and infrastructure for modern hospitals. It reflects the comprehensive management of a modern hospital. Many hospitals have established local networks and implemented some subsystems, such as inpatient toll collection systems and drug management systems, which have yielded some success. Medical information forensics is a comprehensive process of using scientific methods to collect network data, identify intrusion, analyze data, store data, determine the reason of intrusion tp enhance security equipment and trigger alarm procedures. In this paper, we study the concepts and techniques of medical information forensics, and discuss the definition, classification, sources and characteristics of medical information forensics. The deep learning model and the data analytic framework are combined to provide the comprehensive analysis of the research. The technique novelty is validated through the experiment. The simulation compared with the other state-of-the-art methodologies proves the efficiency of the method.

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Axenopoulos, Apostolos, Volker Eiselein, Antonio Penta, Eugenia Koblents, Ernesto La Mattina, and Petros Daras. "A Framework for Large-Scale Analysis of Video \"in the Wild\" to Assist Digital Forensic Examination." IEEE Security & Privacy 17, no. 1 (January 2019): 23–33. http://dx.doi.org/10.1109/msec.2018.2875851.

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Zhang, Chongzhen, Yanli Chen, Yang Meng, Fangming Ruan, Runze Chen, Yidan Li, and Yaru Yang. "A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques." Security and Communication Networks 2021 (January 25, 2021): 1–15. http://dx.doi.org/10.1155/2021/6610675.

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Traditional machine learning-based intrusion detection often only considers a single algorithm to identify intrusion data, lack of the flexibility method, low detection rate, no handing high-dimensional data, and cannot solve these problems well. In order to improve the performance of intrusion detection system, a novel general intrusion detection framework was proposed in this paper, which consists of five parts: preprocessing module, autoencoder module, database module, classification module, and feedback module. The data processed by the preprocessing module are compressed by the autoencoder module to obtain a lower-dimensional reconstruction feature, and the classification result is obtained through the classification module. Compressed features of each traffic are stored in the database module which can both provide retraining and testing for the classification module and restore these features to the original traffic for postevent analysis and forensics. For evaluation of the framework performance proposed, simulation was conducted with the CICIDS2017 dataset to the real traffic of the network. As the experimental results, the accuracy of binary classification and multiclass classification is better than previous work, and high-level accuracy was reached for the restored traffic. At the last, the possibility was discussed on applying the proposed framework to edge/fog networks.

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Keim, Yansi, Yung Han Yoon, and Umit Karabiyik. "Digital Forensics Analysis of Ubuntu Touch on PinePhone." Electronics 10, no. 3 (February 1, 2021): 343. http://dx.doi.org/10.3390/electronics10030343.

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New smartphones made by small companies enter the technology market everyday. These new devices introduce new challenges for mobile forensic investigators as these devices end up becoming pertinent evidence during an investigation. One such device is the PinePhone from Pine Microsystems (Pine64). These new devices are sometimes also shipped with OSes that are developed by open source communities and are otherwise never seen by investigators. Ubuntu Touch is one of these OSes and is currently being developed for deployment on the PinePhone. There is little research behind both the device and OS on what methodology an investigator should follow to reliably and accurately extract data. This results in potentially flawed methodologies being used before any testing can occur and contributes to the backlog of devices that need to be processed. Therefore, in this paper, the first forensic analysis of the PinePhone device with Ubuntu Touch OS is performed using Autopsy, an open source tool, to establish a framework that can be used to examine and analyze devices running the Ubuntu Touch OS. The findings include analysis of artifacts that could impact user privacy and data security, organization structure of file storage, app storage, OS, etc. Moreover, locations within the device that stores call logs, SMS messages, images, and videos are reported. Interesting findings include forensic artifacts, which could be useful to investigators in understanding user activity and attribution. This research will provide a roadmap to the digital forensic investigators to efficiently and effectively conduct their investigations where they have Ubuntu Touch OS and/or PinePhone as the evidence source.

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Kaur, Sukhdilpreet, and Amandeep Verma. "Ontological Engineering Approach Towards Botnet Detection in Network Forensics." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 9 (September 15, 2013): 1990–2001. http://dx.doi.org/10.24297/ijct.v10i9.1390.

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The abundance in the usage of Internet, in every arena of life from social to personal, commercial to domestic and other aspects of life as well, leads the rise in cybercrime at an upsetting speed. More illegal activities as a result of cyber crime, reason to tempts many network attacks and threats. Network forensics is the branch of fornesics that deals in the detection of network attacks. Botnet is one of the most common attacks, but hazardos.Â It Â is a network of hacked computers It Â involves the capturing, storing and then analysis of the network packets, in order to identify the source of the attack. Â Various methods based on this approach for botnet detection are suggested in literature but there is no generalized method to represent the basic methodology used by any of the botnet detection method. With such guidelines, the comparison among the various implementations, a roadmap for the new implementation, development of reusable implementations can be addressed. Accordingly, there is a requirement of a generic framework that can characterize the general methodology followed by any of the botnet detection methods. This paper, review various prevalent methods of botnet detection to extract commonalities among them. A global model for the detection of botnets is represented as ontology. Ontology is used as a means of knowledge representation. The botnet ontology is represented using Web Ontology Language (OWL). OWL is used because it is a language with layered architecture and high expressive power.Â

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El Alaoui El Abdallaoui, Hasna, Abdelaziz El Fazziki, Fatima Zohra Ennaji, and Mohamed Sadgal. "An e-government crowdsourcing framework: suspect investigation and identification." International Journal of Web Information Systems 15, no. 4 (October 7, 2019): 432–53. http://dx.doi.org/10.1108/ijwis-11-2018-0079.

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Purpose The pervasiveness of mobile devices has led to the permanent use of their new features by the crowd to perform different tasks. The purpose of this paper is to exploit this massive consumption of new information technologies supported by the concept of crowdsourcing in a governmental context to access citizens as a source of ideas and support. The aim is to find out how crowdsourcing combined with the new technologies can constitute a great force to enhance the performance of the suspect investigation process. Design/methodology/approach This paper provides a structured view of a suspect investigation framework, especially based on the image processing techniques, including the automatic face analysis. This crowdsourcing framework is mainly based on the personal description as an identification technique to facilitate the suspect investigation and the use of MongoDB as a document-oriented database to store the information. Findings The case study demonstrates that the proposed framework provides satisfying results in each step of the identification process. The experimental results show how the combination between the crowdsourcing concept and the mobile devices pervasiveness has fruitfully strengthened the identification process with the use of automatic face analysis techniques. Originality/value A review of the literature has shown that previous work has focused mainly on the presentation of forensic techniques that can be used in the investigation process steps. However, this paper implements a complete framework whose whole strength is based on the crowdsourcing concept as a new paradigm used by institutions to solve many organizational problems.

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Demertzis, Konstantinos, Nikos Tziritas, Panayiotis Kikiras, Salvador Llopis Sanchez, and Lazaros Iliadis. "The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks." Big Data and Cognitive Computing 3, no. 1 (January 10, 2019): 6. http://dx.doi.org/10.3390/bdcc3010006.

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A Security Operations Center (SOC) is a central technical level unit responsible for monitoring, analyzing, assessing, and defending an organization’s security posture on an ongoing basis. The SOC staff works closely with incident response teams, security analysts, network engineers and organization managers using sophisticated data processing technologies such as security analytics, threat intelligence, and asset criticality to ensure security issues are detected, analyzed and finally addressed quickly. Those techniques are part of a reactive security strategy because they rely on the human factor, experience and the judgment of security experts, using supplementary technology to evaluate the risk impact and minimize the attack surface. This study suggests an active security strategy that adopts a vigorous method including ingenuity, data analysis, processing and decision-making support to face various cyber hazards. Specifically, the paper introduces a novel intelligence driven cognitive computing SOC that is based exclusively on progressive fully automatic procedures. The proposed λ-Architecture Network Flow Forensics Framework (λ-ΝF3) is an efficient cybersecurity defense framework against adversarial attacks. It implements the Lambda machine learning architecture that can analyze a mixture of batch and streaming data, using two accurate novel computational intelligence algorithms. Specifically, it uses an Extreme Learning Machine neural network with Gaussian Radial Basis Function kernel (ELM/GRBFk) for the batch data analysis and a Self-Adjusting Memory k-Nearest Neighbors classifier (SAM/k-NN) to examine patterns from real-time streams. It is a forensics tool for big data that can enhance the automate defense strategies of SOCs to effectively respond to the threats their environments face.

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Demertzis, Konstantinos, Panayiotis Kikiras, Nikos Tziritas, Salvador Sanchez, and Lazaros Iliadis. "The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence." Big Data and Cognitive Computing 2, no. 4 (November 22, 2018): 35. http://dx.doi.org/10.3390/bdcc2040035.

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A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network’s services, but also for attacks identification and for the consequent forensics’ investigations. A serious potential disadvantage of the traditional software solutions used today for computer network monitoring, and specifically for the instances of effective categorization of the encrypted or obfuscated network flow, which enforces the rebuilding of messages packets in sophisticated underlying protocols, is the requirements of computational resources. In addition, an additional significant inability of these software packages is they create high false positive rates because they are deprived of accurate predicting mechanisms. For all the reasons above, in most cases, the traditional software fails completely to recognize unidentified vulnerabilities and zero-day exploitations. This paper proposes a novel intelligence driven Network Flow Forensics Framework (NF3) which uses low utilization of computing power and resources, for the Next Generation Cognitive Computing SOC (NGC2SOC) that rely solely on advanced fully automated intelligence methods. It is an effective and accurate Ensemble Machine Learning forensics tool to Network Traffic Analysis, Demystification of Malware Traffic and Encrypted Traffic Identification.

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Pasko, Roman, and Svitlana Terenchuk. "THE USE OF NEURO-FUZZY MODELS IN EXPERT SUPPORT SYSTEMS FOR FORENSIC BUILDING-TECHNICAL EXPERTISE." ScienceRise 2 (April 30, 2020): 10–18. http://dx.doi.org/10.21303/2313-8416.2020.001278.

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The paper is focused on solving the problem of assessing the impact of repair-building works on the technical condition of objects near which these works were or are being carried out. Particular attention is paid to the analysis of the problems that accompany the creation of expert systems for supporting forensic building-technical expertise. The main aim of the work: conceptual modeling of an expert system for supporting forensic building-technical expertise. Object of research: the process of execution of forensic building-technical expertise and expert research. Solved problem: automation of a system capable of functioning in conditions of fuzzy uncertainty caused by the non-uniformity of the logic of the process of performing forensic building-technical expertise and the ambiguity and inconsistency of the information provided for research. Main scientific results: a model of a knowledge-based system is proposed and the use of neuro-fuzzy networks is justified to solve the problem of supporting the decision to assess the impact of repair-building works on the technical condition of the object, which has become the subject of expertise. Field of practical use of research results: forensic activities in the framework of building-technical expertise to determine the possible causes of deterioration in the technical condition of structural elements of buildings and their individual premises. Innovative technological product: a support system for forensic building-technical expertise based on knowledge and neuro-fuzzy models. Scope of application of an innovative technological product: forensic and investigative practice in resolving issues requiring the use of special knowledge in assessing the impact of repair-building works on the technical condition of nearby facilities.

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CHEN, ZHONGQIANG, ALEX DELIS, and PETER WEI. "IDENTIFICATION AND MANAGEMENT OF SESSIONS GENERATED BY INSTANT MESSAGING AND PEER-TO-PEER SYSTEMS." International Journal of Cooperative Information Systems 17, no. 01 (March 2008): 1–51. http://dx.doi.org/10.1142/s0218843008001750.

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Sessions generated by Instant Messaging and Peer-to-Peer systems (IM/P2Ps) not only consume considerable bandwidth and computing resources but also dramatically change the characteristics of data flows affecting both the operation and performance of networks. Most IM/P2Ps have known security loopholes and vulnerabilities making them an ideal platform for the dissemination of viruses, worms, and other malware. The lack of access control and weak authentication on shared resources further exacerbates the situation. Should IM/P2Ps be deployed in production environments, performance of conventional applications may significantly deteriorate and enterprise data may be contaminated. It is therefore imperative to identify, monitor and finally manage IM/P2P traffic. Unfortunately, this task cannot be easily attained as IM/P2Ps resort to advanced techniques to hide their traces including multiple channels to deliver services, port hopping, message encapsulation and encryption. In this paper, we propose an extensible framework that not only helps to identify and classify IM/P2P-generated sessions in real time but also assists in the manipulation of such traffic. Consisting of four modules namely, session manager, traffic assembler, IM/P2P dissector, and traffic arbitrator, our proposed framework uses multiple techniques to improve its traffic classification accuracy and performance. Through fine-tuned splay and interval trees that help organize IM/P2P sessions and packets in data streams, we accomplish stateful inspection, traffic re-assembly, data stream correlation, and application layer analysis that combined will boost the framework's identification precision. More importantly, we introduce IM/P2Ps "plug-and-play" protocol analyzers that inspect data streams according to their syntax and semantics; these analyzers render our framework easily extensible. Identified IM/P2P sessions can be shaped, blocked, or disconnected, and corresponding traffic can be stored for forensic analysis and threat evaluation. Experiments with our prototype show high IM/P2Ps detection accuracy rates under diverse settings and excellent overall performance in both controlled and real-world environments.

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Hubareva, V. M. "SOME SPECIFICS OF COST DETERMINING OF EQUIPMENT AND MACHINES TAKING INTO ACCOUNT QUALITY DEGRADATION DUE TO WEAR." Theory and Practice of Forensic Science and Criminalistics 18 (December 26, 2018): 498–504. http://dx.doi.org/10.32353/khrife.2018.57.

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One of the objects of merchandising research, which the cost determining has certain features and complexities, are equipment and machines of various purposes especially were in operation and having a certain percentage of wear. Determining of equipment and machines operability, percentage of wear is one of the main factors affecting the formation of the market price and its definition within all approaches to property valuation. According to forensic experts experience while performing this type of research specialists, who involve for determining of a technical condition raise questions: what kind of data should be indicated in the technical condition report in order that experts can use them while performing merchandising examination and the terminology used by specialists, categories of technical condition assessment should coincide with the terminology of appraisers in future. Merchandising experts on the basis of work experience and study of special literature offered the list of such data. It includes: inventory number, name (brand), purpose, completeness (configuration, mass), date of acquisition (release date), service life (operation rate), cost at acquisition date, book value, manufacturer, main technical characteristics, technical condition, functional test, repairs information, repair costs; conclusions about possibility of further exploitation. Merchandising expert uses established percentage of wear in the network of the cost approach that provides for following consecutive actions: determining of replacement cost or reproduction of valuation object and analysis of its technical condition for determining the percentage of wear and further calculations in the network of this approach. Suitability coefficient of valuation object is used by a commodity expert for determining the value of valuation object within framework of comparative approach while selecting comparison objects.

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Leukfeldt, E. R., and Thomas J. Holt. "Examining the Social Organization Practices of Cybercriminals in the Netherlands Online and Offline." International Journal of Offender Therapy and Comparative Criminology 64, no. 5 (December 27, 2019): 522–38. http://dx.doi.org/10.1177/0306624x19895886.

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This study focuses on the organization practices of networks of cybercriminals engaged in serious financial offenses, through a qualitative analysis of the Best and Luckenbill’s sociological framework. The study utilized data collected regarding 18 separate criminals investigations from the Netherlands. The results demonstrate that the participants within these networks operated at various stages of deviant sophistication. Surprisingly, the majority of networks exhibit organizational sophistication based on their division of labor and extended duration over time. In fact, most of this sample could be classified as “teams” or “formal organizations.” Furthermore, in contrast with prior studies, no loners were present and only a few networks could be classified as “colleagues” or “peers.”

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Cohen, M. I. "PyFlag – An advanced network forensic framework." Digital Investigation 5 (September 2008): S112—S120. http://dx.doi.org/10.1016/j.diin.2008.05.016.

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Ferreira, Sara, Mário Antunes, and Manuel E. Correia. "A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing." Data 6, no. 8 (August 5, 2021): 87. http://dx.doi.org/10.3390/data6080087.

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Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

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Khanuja, Harmeet Kaur. "A Framework for Database Forensic Analysis." Computer Science & Engineering: An International Journal 2, no. 3 (June 30, 2012): 27–41. http://dx.doi.org/10.5121/cseij.2012.2303.

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Rahman, Mohammad Arifur, and Huzefa Rangwala. "IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data." Bioinformatics 36, Supplement_1 (July 1, 2020): i39—i47. http://dx.doi.org/10.1093/bioinformatics/btaa477.

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Abstract Motivation The human body hosts more microbial organisms than human cells. Analysis of this microbial diversity provides key insight into the role played by these microorganisms on human health. Metagenomics is the collective DNA sequencing of coexisting microbial organisms in an environmental sample or a host. This has several applications in precision medicine, agriculture, environmental science and forensics. State-of-the-art predictive models for phenotype predictions from metagenomic data rely on alignments, assembly, extensive pruning, taxonomic profiling and reference sequence databases. These processes are time consuming and they do not consider novel microbial sequences when aligned with the reference genome, limiting the potential of whole metagenomics. We formulate the problem of predicting human disease from whole-metagenomic data using Multiple Instance Learning (MIL), a popular supervised learning paradigm. Our proposed alignment-free approach provides higher accuracy in prediction by harnessing the capability of deep convolutional neural network (CNN) within a MIL framework and provides interpretability via neural attention mechanism. Results The MIL formulation combined with the hierarchical feature extraction capability of deep-CNN provides significantly better predictive performance compared to popular existing approaches. The attention mechanism allows for the identification of groups of sequences that are likely to be correlated to diseases providing the much-needed interpretation. Our proposed approach does not rely on alignment, assembly and reference sequence databases; making it fast and scalable for large-scale metagenomic data. We evaluate our method on well-known large-scale metagenomic studies and show that our proposed approach outperforms comparative state-of-the-art methods for disease prediction. Availability and implementation https://github.com/mrahma23/IDMIL.

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LIU, Dai-ping, Ming-wei ZHANG, and Jia CHEN. "Blood examination-based network traffic analysis framework." Journal of Computer Applications 28, no. 12 (February 26, 2009): 3220–23. http://dx.doi.org/10.3724/sp.j.1087.2008.03220.

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Boscolo, R., C. Sabatti, J. C. Liao, and V. P. Roychowdhury. "A Generalized Framework for Network Component Analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 2, no. 4 (October 2005): 289–301. http://dx.doi.org/10.1109/tcbb.2005.47.

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Fu, Zening, Yuhui Du, and Vince D. Calhoun. "The Dynamic Functional Network Connectivity Analysis Framework." Engineering 5, no. 2 (April 2019): 190–93. http://dx.doi.org/10.1016/j.eng.2018.10.001.

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Zhang, Wen Xia, Yan Wang, and Shu Hua Li. "Analysis Framework of Freemodbus." Advanced Materials Research 765-767 (September 2013): 1610–13. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1610.

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Modbus is a popular network protocol in the industrial manufacturing environment. A modbus communication stack requires two layers:the modbus application protocol which defines the data model and functions and a Network layer. The FreeMODBUS provides an implementation of the modbus application protocol and supports RTU/ASCII transmission models. The paper introduces the analysis of FreeMODBUSs whole framework and the serial links implementation. According to the analysis, it is easily to realize the modbus protocol stack on the chip which FreeMODBUS hasnt realized and so quickly to finish the product design.

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Nuttachot Promrit, Anirach Mingkhwan, Supaporn Simcharoen, and Nati Namvong. "Multi-Dimensional Visualization for Network Forensic Analysis." International Journal of Advancements in Computing Technology 4, no. 5 (March 15, 2012): 222–32. http://dx.doi.org/10.4156/ijact.vol4.issue5.27.

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Preszler, Jonathan, David K. Marcus, John F. Edens, and Barbara E. McDermott. "Network analysis of psychopathy in forensic patients." Journal of Abnormal Psychology 127, no. 2 (February 2018): 171–82. http://dx.doi.org/10.1037/abn0000325.

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Zhang, Chuanchao, Jiguang Wang, Chao Zhang, Juan Liu, Dong Xu, and Luonan Chen. "Network stratification analysis for identifying function-specific network layers." Molecular BioSystems 12, no. 4 (2016): 1232–40. http://dx.doi.org/10.1039/c5mb00782h.

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We proposed a novel computational framework, namely network stratification analysis (NetSA), to stratify the whole biological network into various function-specific network layers corresponding to particular functions.

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Lakhal, Salem, Alain Martel, Muhittin Oral, and Benoit Montreuil. "Network companies and competitiveness: A framework for analysis." European Journal of Operational Research 118, no. 2 (October 1999): 278–94. http://dx.doi.org/10.1016/s0377-2217(99)00026-0.

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Martínez, Víctor, Fernando Berzal, and Juan-Carlos Cubero. "NOESIS: A Framework for Complex Network Data Analysis." Complexity 2019 (October 31, 2019): 1–14. http://dx.doi.org/10.1155/2019/1439415.

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Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. The proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. This framework is available under a BSD open-source software license.

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Dalampira, Evropi‐Sofia, and Stefanos A. Nastis. "Mapping Sustainable Development Goals: A network analysis framework." Sustainable Development 28, no. 1 (July 2, 2019): 46–55. http://dx.doi.org/10.1002/sd.1964.

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Kim, Hyun, Tae Kim, and Sang Lee. "Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network." BMC Systems Biology 5, Suppl 2 (2011): S14. http://dx.doi.org/10.1186/1752-0509-5-s2-s14.

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Hamdi, Sally Dakheel, and Abdulkareem Merhej Radhi. "Integrated Cyber Forensic for E-Mail Analysis Framework." Indian Journal of Science and Technology 12, no. 27 (July 1, 2019): 1–13. http://dx.doi.org/10.17485/ijst/2019/v12i27/145999.

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Hadjidj, Rachid, Mourad Debbabi, Hakim Lounis, Farkhund Iqbal, Adam Szporer, and Djamel Benredjem. "Towards an integrated e-mail forensic analysis framework." Digital Investigation 5, no. 3-4 (March 2009): 124–37. http://dx.doi.org/10.1016/j.diin.2009.01.004.

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Arsić, Branko, Ljubiša Bojić, Ivan Milentijević, Petar Spalević, and Dejan Rančić. "SYMBOLS: SOFTWARE FOR SOCIAL NETWORK ANALYSIS." Facta Universitatis, Series: Automatic Control and Robotics 17, no. 3 (January 8, 2019): 205. http://dx.doi.org/10.22190/fuacr1803205a.

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The unique possibilities of the online social networks such as real-time data access, knowledge of users’ changing preferences and access to their statuses provide the possibility for innovation in the analysis of people’s behavior and opinions, when compared to classical offline methods. Literature review shows lack of studies about the use of public Facebook data in Serbia for the improvement of different product sale, political or promotional campaigns, recommender systems, etc. In this paper, we present the way how data from Facebook can be collected in order to gain insight into the individuals’ preferences and statuses, as well as their connection to a company's fan pages. In particular, we present data collection framework – Symbols – used for collecting individual specific data. The framework stores data into local database and involves a module for graph and content-based analysis of these data. The proposed framework for social network analysis can be used as a decision-making system in users’ preferences implementation thus creating a space for business improvements in various areas.

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Zhu, Lei, Yi-Chang Chiu, and Yuche Chen. "Road network abstraction approach for traffic analysis: framework and numerical analysis." IET Intelligent Transport Systems 11, no. 7 (September 1, 2017): 424–30. http://dx.doi.org/10.1049/iet-its.2016.0287.

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Françoisse, Kevin, Ilkka Kivimäki, Amin Mantrach, Fabrice Rossi, and Marco Saerens. "A bag-of-paths framework for network data analysis." Neural Networks 90 (June 2017): 90–111. http://dx.doi.org/10.1016/j.neunet.2017.03.010.

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Doi, Suhail A. R., and Jan J. Barendregt. "A generalized pairwise modelling framework for network meta-analysis." International Journal of Evidence-Based Healthcare 16, no. 4 (December 2018): 187–94. http://dx.doi.org/10.1097/xeb.0000000000000140.

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M, Durai Pandian. "PERFORMANCE ANALYSIS AND QOS FRAMEWORK OF FLY WİRELESS NETWORK." IRO Journal on Sustainable Wireless Systems 01, no. 02 (June 16, 2019): 103–15. http://dx.doi.org/10.36548/jsws.2019.2.003.

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The spread out of wireless mesh network has made possible the extended range of communication network that are impractical due to environmental changes in a wired access point, these wireless mesh network does not require much competence to set it up as it can be set very fast at a cheap rate, and the conveyancing of messages in it happens by selecting the shortest path, these wireless mesh built-in with irrepressible and invulnerable identities come with an endurance to temporary congestion and individual node failure. This results in an architecture providing a better coverage, flaw indulgent with higher bandwidth compared to other wireless distributed systems. But faces the limitation on power conservation. The battery activated mesh nodes loses their resources on perception, processing and transmission of the data’s, though these batteries or accumulators comes with energy regaining capability still draw backs show up as their nature of energy regaining are unexposed. So the performance analysis of fly wireless network which proposes a uninterrupted wireless mesh networks aims at providing a best measure of performance that is the best quality of service on the meshwork by providing an improved energy gleaning using potency segregation (IGPS) which empowers each node to have self- contained accumulation of energy achieving heightened adaption with energy consumption kept at a minimum. The gross functioning of the proposed is examined on the bases of delay and packet loss to prove the quality of service acquired.

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., Rashmi MR. "A NOVEL DISTRIBUTED INTRUSION DETECTION FRAMEWORK FOR NETWORK ANALYSIS." International Journal of Research in Engineering and Technology 04, no. 08 (August 25, 2015): 246–51. http://dx.doi.org/10.15623/ijret.2015.0408043.

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Journal articles on the topic 'Network forensic framework analysis'

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