Biblio

Access control includes authorization of security administrators and access of users. Aiming at the problems of log information storage difficulty and easy tampering faced by auditing and traceability forensics of authorization and access in cross-domain scenarios, we propose an access control auditing and traceability forensics method based on Blockchain, whose core is Ethereum Blockchain and IPFS interstellar mail system, and its main function is to store access control log information and trace forensics. Due to the technical characteristics of blockchain, such as openness, transparency and collective maintenance, the log information metadata storage based on Blockchain meets the requirements of distribution and trustworthiness, and the exit of any node will not affect the operation of the whole system. At the same time, by storing log information in the blockchain structure and using mapping, it is easy to locate suspicious authorization or judgment that lead to permission leakage, so that security administrators can quickly grasp the causes of permission leakage. Using this distributed storage structure for security audit has stronger anti-attack and anti-risk.

Frame deletion forensics has been a major area of video forensics in recent years. The detection effect of current deep neural network-based methods outperforms previous traditional detection methods. Recently, researchers have used residual features as input to the network to detect frame deletion and have achieved promising results. We propose an IReF (Improved Residual Feature) by analyzing the effect of residual features on frame deletion traces. IReF preserves the main motion features and edge information by denoising and enhancing the residual features, making it easier for the network to identify the tampered features. And the sparse noise reduction reduces the storage requirement. Experiments show that under the 2D convolutional neural network, the accuracy of IReF compared with residual features is increased by 3.81 %, and the storage space requirement is reduced by 78%. In the 3D convolutional neural network with video clips as feature input, the accuracy of IReF features is increased by 5.63%, and the inference efficiency is increased by 18%.

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.

With the advent of information and communication technology, the digital space is becoming a playing ground for criminal activities. Criminals typically prefer darkness or a hidden place to perform their illegal activities in a real-world while sometimes covering their face to avoid being exposed and getting caught. The same applies in a digital world where criminals prefer features which provide anonymity or hidden features to perform illegal activities. It is from this spirit the Darkweb is attracting all kinds of criminal activities conducted over the Internet such as selling drugs, illegal weapons, child pornography, assassination for hire, hackers for hire, and selling of malicious exploits, to mention a few. Although the anonymity offered by Darkweb can be exploited as a tool to arrest criminals involved in cybercrime, an in-depth research is needed to advance criminal investigation on Darkweb. Analysis of illegal activities conducted in Darkweb is in its infancy and faces several challenges like lack of standard operating procedures. This study proposes progressive standard operating procedures (SOPs) for Darkweb forensics investigation. We provide the four stages of SOP for Darkweb investigation. The proposed SOP consists of the following stages; identification and profiling, discovery, acquisition and preservation, and the last stage is analysis and reporting. In each stage, we consider the objectives, tools and expected results of that particular stage. Careful consideration of this SOP revealed promising results in the Darkweb investigation.

Many cyber-related untoward incidents and multiple instances of a data breach of system are being reported. User identity and its usage for valid entry to system depend upon successful authentication. Researchers have explored many threats and vulnerabilities in a centralized system. It has initiated concept of a decentralized way to overcome them. In this work, we have explored application of Self-Sovereign Identity and Verifiable Credentials using decentralized identifiers over cloud.

In this paper, we propose a novel watermarking-based copy deterrence scheme for identifying data leaks through authorized query users in secure image outsourcing systems. The scheme generates watermarks unique to each query user, which are embedded in the retrieved encrypted images. During unauthorized distribution, the watermark embedded in the image is extracted to determine the untrustworthy query user. Experimental results show that the proposed scheme achieves minimal information loss, faster embedding and better resistance to JPEG compression attacks compared with the state-of-the-art schemes.

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

Researchers have investigated the dark web for various purposes and with various approaches. Most of the dark web data investigation focused on analysing text collected from HTML pages of websites hosted on the dark web. In addition, researchers have documented work on dark web image data analysis for a specific domain, such as identifying and analyzing Child Sexual Abusive Material (CSAM) on the dark web. However, image data from dark web marketplace postings and forums could also be helpful in forensic analysis of the dark web investigation.The presented work attempts to conduct image classification on classes other than CSAM. Nevertheless, manually scanning thousands of websites from the dark web for visual evidence of criminal activity is time and resource intensive. Therefore, the proposed work presented the use of quantum computing to classify the images using a Quantum Convolutional Neural Network (QCNN). Authors classified dark web images into four categories alcohol, drugs, devices, and cards. The provided dataset used for work discussed in the paper consists of around 1242 images. The image dataset combines an open source dataset and data collected by authors. The paper discussed the implementation of QCNN and offered related performance measures.

Efficient large-scale biometric identification is a challenging open problem in biometrics today. Adding biometric information protection by cryptographic techniques increases the computational workload even further. Therefore, this paper proposes an efficient and improved use of coefficient packing for homomorphically protected biometric templates, allowing for the evaluation of multiple biometric comparisons at the cost of one. In combination with feature dimensionality reduction, the proposed technique facilitates a quadratic computational workload reduction for biometric identification, while long-term protection of the sensitive biometric data is maintained throughout the system. In previous works on using coefficient packing, only a linear speed-up was reported. In an experimental evaluation on a public face database, efficient identification in the encrypted domain is achieved on off-the-shelf hardware with no loss in recognition performance. In particular, the proposed improved use of coefficient packing allows for a computational workload reduction down to 1.6% of a conventional homomorphically protected identification system without improved packing.

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransom ware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.

There is an increase in interest and necessity for an interoperable and efficient railway network across Europe, creating a key distribution problem between train and trackside entities’ key management centres (KMC). Train and trackside entities establish a secure session using symmetric keys (KMAC) loaded beforehand by their respective KMC using procedures that are not scalable and prone to operational mistakes. A single system would simplify the KMAC distribution between KMCs; nevertheless, it is difficult to place the responsibility for such a system for the whole European area within one central organization. A single system could also expose relationships between KMCs, revealing information, such as plans to use an alternative route or serve a new region, jeopardizing competitive advantage. This paper proposes a scalable and decentralised key management system that allows KMC to share cryptographic keys using transactions while keeping relationships anonymous. Using non-interactive proofs of knowledge and assigning each entity a private and public key, private key owners can issue valid transactions while all system actors can validate them. Our performance analysis shows that the proposed system is scalable when a proof of concept is implemented with settings close to the expected railway landscape in 2030.

With the rapid development of information science and technology, the role of the Internet in daily life is becoming more and more important, but while bringing speed and convenience to the experience, network security issues are endless, and fighting cybercrime will be an eternal topic. In recent years, new types of cyberattacks have made defense and analysis difficult. For example, the memory of network attacks makes some key array evidence only temporarily exist in physical memory, which puts forward higher requirements for attack detection. The traditional memory forensic analysis method for persistent data is no longer suitable for a new type of network attack analysis. The continuous development of memory forensics gives people hope. This paper proposes a network attack detection model based on memory forensic analysis to detect whether the system is under attack. Through experimental analysis, this model can effectively detect network attacks with low overhead and easy deployment, providing a new idea for network attack detection.

SDN represents a significant advance for the telecom world, since the decoupling of the control and data planes offers numerous advantages in terms of management dynamism and programmability, mainly due to its software-based centralized control. Unfortunately, these features can be exploited by malicious entities, who take advantage of the centralized control to extend the scope and consequences of their attacks. When this happens, both the legal and network technical fields are concerned with gathering information that will lead them to the root cause of the problem. Although forensics and incident response processes share their interest in the event information, both operate in isolation due to the conceptual and pragmatic challenges of integrating them into SDN environments, which impacts on the resources and time required for information analysis. Given these limitations, the current work focuses on proposing a framework for SDNs that combines the above approaches to optimize the resources to deliver evidence, incorporate incident response activation mechanisms, and generate assumptions about the possible origin of the security problem.

Malicious software (malware) poses a significant threat to the security of our networks and users. In the ever-evolving malware landscape, Excel 4.0 Office macros (XL4) have recently become an important attack vector. These macros are often hidden within apparently legitimate documents and under several layers of obfuscation. As such, they are difficult to analyze using static analysis techniques. Moreover, the analysis in a dynamic analysis environment (a sandbox) is challenging because the macros execute correctly only under specific environmental conditions that are not always easy to create. This paper presents SYMBEXCEL, a novel solution that leverages symbolic execution to deobfuscate and analyze Excel 4.0 macros automatically. Our approach proceeds in three stages: (1) The malicious document is parsed and loaded in memory; (2) Our symbolic execution engine executes the XL4 formulas; and (3) Our Engine concretizes any symbolic values encountered during the symbolic exploration, therefore evaluating the execution of each macro under a broad range of (meaningful) environment configurations. SYMBEXCEL significantly outperforms existing deobfuscation tools, allowing us to reliably extract Indicators of Compromise (IoCs) and other critical forensics information. Our experiments demonstrate the effectiveness of our approach, especially in deobfuscating novel malicious documents that make heavy use of environment variables and are often not identified by commercial anti-virus software.

Virtual Private Networks (VPNs) have become a communication medium for accessing information, data exchange and flow of information. Many organizations require Intranet or VPN, for data access, access to servers from computers and sharing different types of data among their offices and users. A secure VPN environment is essential to the organizations to protect the information and their IT infrastructure and their assets. Every organization needs to protect their computer network environment from various malicious cyber threats. This paper presents a comprehensive network security management which includes significant strategies and protective measures during the management of a VPN in an organization. The paper also presents the procedures and necessary counter measures to preserve the security of VPN environment and also discussed few Identified Security Strategies and measures in VPN. It also briefs the Network Security and their Policies Management for implementation by covering security measures in firewall, visualized security profile, role of sandbox for securing network. In addition, a few identified security controls to strengthen the organizational security which are useful in designing a secure, efficient and scalable VPN environment, are also discussed.

Forensic Science comprises a set of technical-scientific knowledge used to solve illicit acts. The increasing use of mobile devices as the main computing platform, in particular smartphones, makes existing information valuable for forensics. However, the blocking mechanisms imposed by the manufacturers and the variety of models and technologies make the task of reconstructing the data for analysis challenging. It is worth mentioning that the conclusion of a case requires more than the simple identification of evidence, as it is extremely important to correlate all the data and sources obtained, to confirm a suspicion or to seek new evidence. This work carries out a systematic review of the literature, identifying the different types of existing image acquisition and the main extraction and encryption methods used in smartphones with the Android operating system.

Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.

The popularity of portable web browsers is increasing due to its convenient and compact nature along with the benefit of the data being stored and transferred easily using a USB drive. As technology gets updated frequently, developers are working on web browsers that can be portable in nature with additional security features like private mode browsing, built in ad blockers etc. The increased probability of using portable web browsers for carrying out nefarious activities is a result of cybercriminals with the thought that if they use portable web browsers in private mode it won't leave a digital footprint. Hence, the research paper aims at performing a comparative study of four portable web browsers namely Brave, TOR, Vivaldi, and Maxthon along with various memory acquisition tools to understand the quantity and quality of the data that can be recovered from the memory dump in two different conditions that is when the browser tabs were open and when the browser tabs were closed in a system to aid the forensic investigators.

In this current age, stakeholders exchange legal documents, as well as documents that are official, sensitive and confidential via digital channels[1]. To securely communicate information between stakeholders is not an easy task considering the intentional or unintentional changes and possible attacks that can occur during communication. This paper focuses on protecting and securing data by hiding the data using steganography techniques, after encrypting the data to avoid unauthorized changes or modification made by adversaries to the data through using the Simplified Data Encryption Technique. By leveraging on these two approaches, secret data security intensifies to two levels and a steganography image of high quality is attained. Cryptography converts plaintext into cipher text (unreadable text); whereas steganography is the technique of hiding secret messages in other messages. First encryption of data is done using the Simplified Data Encryption Standard (S-DES) algorithm after which the message encrypted is embedded in the cover image by means of the Least Significant Bit (LSB) approach.

Electromagnetic emissions associated with the transmission of automotive controller area network (CAN) messages within a passenger car have been analysed and used to reconstruct the original CAN messages. Concurrent monitoring of the CAN traffic via a wired connection to the vehicle OBD-II port was used to validate the effectiveness of the reconstruction process. These results confirm the feasibility of reconstructing in-vehicle network data for forensic purposes, without the need for wired access, at distances of up to 1 m from the vehicle by using magnetic field measurements, and up to 3 m using electric field measurements. This capability has applications in the identification and resolution of EMI issues in vehicle data network, as well as possible implications for automotive cybersecurity.

The widespread use of smartphones has made the gadget a prime source of evidence for crime investigators. The cloud-based applications on mobile devices store a rich set of evidence in the cloud servers. The physical acquisition of Android devices reveals only minimal data of cloud-based apps. However, the artifacts collected from mobile devices can be used for data acquisition from cloud servers. This paper focuses on the forensic acquisition and analysis of cloud data of Google apps on Android devices. The proposed methodology uses the tokens extracted from the Android devices to get authenticated to the Google server bypassing the two-factor authentication scheme and access the cloud data for further analysis. Based on the investigation, we have also developed a tool to acquire, preserve and analyze cloud data in a forensically sound manner.

Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline – grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.

The need for cyber resilience is increasingly important in our technology-dependent society, where computing systems, devices and data will continue to be the target of cyber attackers. Hence, we propose a conceptual framework called ‘Human-in-the-Loop Explainable-AI-Enabled Vulnerability Detection, Investigation, and Mitigation’ (HXAI-VDIM). Specifically, instead of resolving complex scenario of security vulnerabilities as an output of an AI/ML model, we integrate the security analyst or forensic investigator into the man-machine loop and leverage explainable AI (XAI) to combine both AI and Intelligence Assistant (IA) to amplify human intelligence in both proactive and reactive processes. Our goal is that HXAI-VDIM integrates human and machine in an interactive and iterative loop with security visualization that utilizes human intelligence to guide the XAI-enabled system and generate refined solutions.

In November 2019, the government of Iran enforced a week-long total Internet blackout that prevented the majority of Internet connectivity into and within the nation. This work elaborates upon the Iranian Internet blackout by characterizing the event through Internet-scale, near realtime network traffic measurements. Beginning with an investigation of compromised machines scanning the Internet, nearly 50 TB of network traffic data was analyzed. This work discovers 856,625 compromised IP addresses, with 17,182 attributed to the Iranian Internet space. By the second day of the Internet shut down, these numbers dropped by 18.46% and 92.81%, respectively. Empirical analysis of the Internet-of-Things (IoT) paradigm revealed that over 90% of compromised Iranian hosts were fingerprinted as IoT devices, which saw a significant drop throughout the shutdown (96.17% decrease by the blackout's second day). Further examination correlates BGP reachability metrics and related data with geolocation databases to statistically evaluate the number of reachable Iranian ASNs (dropping from approximately 1100 to under 200 reachable networks). In-depth investigation reveals the top affected ASNs, providing network forensic evidence of the longitudinal unplugging of such key networks. Lastly, the impact's interruption of the Bitcoin cryptomining market is highlighted, disclosing a massive spike in unsuccessful (i.e., pending) transactions. When combined, these network traffic measurements provide a multidimensional perspective of the Iranian Internet shutdown.

Fake news, frequently making use of tampered photos, has currently emerged as a global epidemic, mainly due to the widespread use of social media as a present alternative to traditional news outlets. This development is often due to the swiftly declining price of advanced cameras and phones, which prompts the simple making of computerized pictures. The accessibility and usability of picture-altering softwares make picture-altering or controlling processes significantly simple, regardless of whether it is for the blameless or malicious plan. Various investigations have been utilized around to distinguish this sort of controlled media to deal with this issue. This paper proposes an efficient technique of copy-move forgery detection using the deep learning method. Two deep learning models such as Buster Net and VGG with FPN are used here to detect copy move forgery in digital images. The two models' performance is evaluated using the CoMoFoD dataset. The experimental result shows that VGG with FPN outperforms the Buster Net model for detecting forgery in images with an accuracy of 99.8% whereas the accuracy for the Buster Net model is 96.9%.