Biblio

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.

Impactful data breaches that exposed the online accounts and financial information of billions of individuals have increased recently because of the digitization of numerous industries. As a result, the need for comprehensive cybersecurity measures has risen, particularly with regard to the safekeeping of user passwords. Strong password storage security ensures that even if an attacker has access to compromised data, they are unable to utilize the passwords in attack vectors like credential-stuffing assaults. Additionally, it will reduce the risk of threats like fraudulent account charges or account takeovers for users. This study compares the performance of several hashing algorithms, including Bcrypt, SHA-256 and MD5 and how bcrypt algorithm outperforms the other algorithms. Reversal of each of the results will be attempted using Rainbow Tables for better understanding of hash reversals and the comparisons are tabulated. The paper provides a detail implementation of bcrypt algorithm and sheds light on the methodology of BCRYPT hashing algorithm results in robust password security. While SHA-256 hashing algorithms are, easily susceptible to simple attacks such as brute force as it a fast algorithm and making bcrypt more favorable.

Cyber threats can cause severe damage to computing infrastructure and systems as well as data breaches that make sensitive data vulnerable to attackers and adversaries. It is therefore imperative to discover those threats and stop them before bad actors penetrating into the information systems.Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. The accuracy of the AI/ML algorithms could be affected by many factors, from algorithm, data, to prejudicial, or even intentional. As a result, AI/ML applications need to be non-biased and trustworthy.In this research, we developed a machine learning-based cyber threat detection and assessment tool. It uses two-stage (both unsupervised and supervised learning) analyzing method on 822,226 log data recorded from a web server on AWS cloud. The results show the algorithm has the ability to identify the threats with high confidence.

In this work, we discuss data breaches based on the “2012 SocialArks data breach” case study. Data leakage refers to the security violations of unauthorized individuals copying, transmitting, viewing, stealing, or using sensitive, protected, or confidential data. Data leakage is becoming more and more serious, for those traditional information security protection methods like anti-virus software, intrusion detection, and firewalls have been becoming more and more challenging to deal with independently. Nevertheless, fortunately, new IT technologies are rapidly changing and challenging traditional security laws and provide new opportunities to develop the information security market. The SocialArks data breach was caused by a misconfiguration of ElasticSearch Database owned by SocialArks, owned by “Tencent.” The attack methodology is classic, and five common Elasticsearch mistakes discussed the possibilities of those leakages. The defense solution focuses on how to optimize the Elasticsearch server. Furthermore, the ElasticSearch database’s open-source identity also causes many ethical problems, which means that anyone can download and install it for free, and they can install it almost anywhere. Some companies download it and install it on their internal servers, while others download and install it in the cloud (on any provider they want). There are also cloud service companies that provide hosted versions of Elasticsearch, which means they host and manage Elasticsearch clusters for their customers, such as Company Tencent.

The 2021 T-Mobile breach conducted by John Erin Binns resulted in the theft of 54 million customers' personal data. The attacker gained entry into T-Mobile's systems through an unprotected router and used brute force techniques to access the sensitive information stored on the internal servers. The data stolen included names, addresses, Social Security Numbers, birthdays, driver's license numbers, ID information, IMEIs, and IMSIs. We analyze the data breach and how it opens the door to identity theft and many other forms of hacking such as SIM Hijacking. SIM Hijacking is a form of hacking in which bad actors can take control of a victim's phone number allowing them means to bypass additional safety measures currently in place to prevent fraud. This paper thoroughly reviews the attack methodology, impact, and attempts to provide an understanding of important measures and possible defense solutions against future attacks. We also detail other social engineering attacks that can be incurred from releasing the leaked data.

In this paper, we proposed a data security model of a big data analytical environment in the financial sector. Big Data can be seen as a trend in the advancement of technology that has opened the door to a new approach to understanding and decision making that is used to describe the vast amount of data (structured, unstructured and semi-structured) that is too time consuming and costly to load a relational database for analysis. The increase in cybercriminal attacks on an organization’s assets results in organizations beginning to invest in and care more about their cybersecurity points and controls. The management of business-critical data is an important point for which robust cybersecurity controls should be considered. The proposed model is applied in a datalake and allows the identification of security gaps on an analytical repository, a cybersecurity risk analysis, design of security components and an assessment of inherent risks on high criticality data in a repository of a regulated financial institution. The proposal was validated in financial entities in Lima, Peru. Proofs of concept of the model were carried out to measure the level of maturity focused on: leadership and commitment, risk management, protection control, event detection and risk management. Preliminary results allowed placing the entities in level 3 of the model, knowing their greatest weaknesses, strengths and how these can affect the fulfillment of business objectives.

Most of the existing identity systems are built on top of centralized storage systems. Storing identity data on these types of centralized storage platforms(e.g cloud storage, central servers) becomes a major privacy concern since various types of attacks and data breaches can happen. With this research, we are proposing blockchain and self-sovereign identity based digital identity (KYC - Know Your Customer) platform “Casper” to address the issues on centralized identity systems. “Casper ” is an Android/iOS based mobile identity wallet application that combines the integration of blockchain and a self-sovereign identity-based approach. Unlike centralized identity systems, the actual identities of the customer/users are stored in the customers’ mobile wallet application. The proof of these identities is stored in the blockchain-based decentralized storage as a self-sovereign identity proof. Casper platforms’ Self-Sovereign Identity(SSI)-based system provides a Zero Knowledge Proof(ZKP) mechanism to verify the identity information. Casper platform can be adopted in various domains such as healthcare, banking, government organization etc. As a use case, we have discussed building a digital identity wallet for banking customers with the Casper platform. Casper provides a secure, decentralized and ZKP verifiable identity by using blockchain and SSI based approach. It addresses the common issues in centralized/cloud-based identity systems platforms such as the lack of data immutability, lack of traceability, centralized control etc.

Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development.In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools’ performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.

USB flash drives are used widely to store or transfer data among the employees in the company. There was greater concern about leaks of information especially company crown jewel or intellectual property data inside the USB flash drives because of theft, loss, negligence or fraud. This study is a real case in XYZ company which aims to find remaining the company’s crown jewel or intellectual property data inside the USB flash drives that belong to the employees. The research result showed that sensitive information (such as user credentials, product recipes and customer credit card data) could be recovered from the employees’ USB flash drives. It could obtain a high-risk impact on the company as reputational damage and sabotage product from the competitor. This result will help many companies to increase security awareness in protecting their crown jewel by having proper access control and to enrich knowledge regarding digital forensic for investigation in the company or enterprise.

Software vulnerabilities are weaknesses in software systems that can have serious consequences when exploited. Examples of side effects include unauthorized authentication, data breaches, and financial losses. Due to the nature of the software industry, companies are increasingly pressured to deploy software as quickly as possible, leading to a large number of undetected software vulnerabilities. Static code analysis, with the support of Static Analysis Tools (SATs), can generate security alerts that highlight potential vulnerabilities in an application's source code. Software Metrics (SMs) have also been used to predict software vulnerabilities, usually with the support of Machine Learning (ML) classification algorithms. Several datasets are available to support the development of improved software vulnerability detection techniques. However, they suffer from the same issues: they are either outdated or use a single type of information. In this paper, we present a methodology for collecting software vulnerabilities from known vulnerability databases and enhancing them with static information (namely SAT alerts and SMs). The proposed methodology aims to define a mechanism capable of more easily updating the collected data.

This Wiping Tool is an anti-forensic tool that is built to wipe data permanently from laptop's storage. This tool is capable to ensure the data from being recovered with any recovery tools. The objective of building this wiping tool is to maintain the confidentiality and integrity of the data from unauthorized access. People tend to delete the file in normal way, however, the file face the risk of being recovered. Hence, the integrity and confidentiality of the deleted file cannot be protected. Through wiping tools, the files are overwritten with random strings to make the files no longer readable. Thus, the integrity and the confidentiality of the file can be protected. Regarding wiping tools, nowadays, lots of wiping tools face issue such as data breach because the wiping tools are unable to delete the data permanently from the devices. This situation might affect their main function and a threat to their users. Hence, a new wiping tool is developed to overcome the problem. A new wiping tool named Data Wiping tool is applying two wiping techniques. The first technique is Randomized Data while the next one is enhancing wiping technique, known as ByteEditor. ByteEditor is a combination of two different techniques, byte editing and byte deletion. With the implementation of Object-Oriented methodology, this wiping tool is built. This methodology consists of analyzing, designing, implementation and testing. The tool is analyzed and compared with other wiping tools before the designing of the tool start. Once the designing is done, implementation phase take place. The code of the tool is created using Visual Studio 2010 with C\# language and being tested their functionality to ensure the developed tool meet the objectives of the project. This tool is believed able to contribute to the development of wiping tools and able to solve problems related to other wiping tools.

The risk posed by high-profile data breaches has raised the stakes for adhering to data access policies for many organizations, but the complexity of both the policies themselves and the applications that must obey them raises significant challenges. To mitigate this risk, fine-grained audit of access to private data has become common practice, but this is a costly, time-consuming, and error-prone process.We propose an approach for automating much of the work required for fine-grained audit of private data access. Starting from the assumption that the auditor does not have an explicit, formal description of the correct policy, but is able to decide whether a given policy fragment is partially correct, our approach gradually infers a policy from audit log entries. When the auditor determines that a proposed policy fragment is appropriate, it is added to the system's mechanized policy, and future log entries to which the fragment applies can be dealt with automatically. We prove that for a general class of attribute-based data policies, this inference process satisfies a monotonicity property which implies that eventually, the mechanized policy will comprise the full set of access rules, and no further manual audit is necessary. Finally, we evaluate this approach using a case study involving synthetic electronic medical records and the HIPAA rule, and show that the inferred mechanized policy quickly converges to the full, stable rule, significantly reducing the amount of effort needed to ensure compliance in a practical setting.

Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients’ end. This creates significant overhead for the patient, who must authorize every access of their health record. It is also not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization.

The ever-evolving computers has its implications on the data and information and the threats that they are exposed to. With the exponential growth of internet, the chances of data breach are highly likely as unauthorized and ill minded users find new ways to get access to the data that they can use for their plans. Most of the systems today have well designed measures that examine the information for any abnormal behavior (Zero Day Attacks) compared to what has been seen and experienced over the years. These checks are done based on a predefined identity (signature) of information. This is being termed as Intrusion Detection Systems (IDS). The concept of IDS revolves around validation of data and/or information and detecting unauthorized access attempts with an intention of manipulating data. High Performance Soft Computing (HPSC) aims to internalize cumulative adoption of traditional and modern attempts to breach data security and expose it to high scale damage and altercations. Our effort in this paper is to emphasize on the multifaceted tactic and rationalize important functionalities of IDS available at the disposal of HPSC.

The intranets in modern organizations are facing severe data breaches and critical resource misuses. By reusing user credentials from compromised systems, Advanced Persistent Threat (APT) attackers can move laterally within the internal network. A promising new approach called deception technology makes the network administrator (i.e., defender) able to deploy decoys to deceive the attacker in the intranet and trap him into a honeypot. Then the defender ought to reasonably allocate decoys to potentially insecure hosts. Unfortunately, existing APT-related defense resource allocation models are infeasible because of the neglect of many realistic factors.In this paper, we make the decoy deployment strategy feasible by proposing a game-theoretic model called the APT Deception Game to describe interactions between the defender and the attacker. More specifically, we decompose the decoy deployment problem into two subproblems and make the problem solvable. Considering the best response of the attacker who is aware of the defender’s deployment strategy, we provide an elitist reservation genetic algorithm to solve this game. Simulation results demonstrate the effectiveness of our deployment strategy compared with other heuristic strategies.

This paper is focused on the topic of the use of biometrics framework and strategy for secure access identity management of cloud computing services. This paper present's a description of cloud computing security issues and explored a review of previous works that represented various ideas for a cloud access framework. This paper discusses threats like a malicious insider, data breaches, and describes ways to protect them. It describes an innovative way portrayed a framework that fingerprint access-based authentication to protect Cloud services from unauthorized access and DOS, DDoS attacks. This biometrics-based framework as an extra layer of protection, added then it can be robust to prevent unauthorized access to cloud services.

Most cyber-attacks and data breaches in cloud infrastructure are due to human errors and misconfiguration vulnerabilities. Cloud customer-centric tools are imperative for mitigating these issues, however existing cloud security models are largely unable to tackle these security challenges. Therefore, novel security mechanisms are imperative, we propose Risk-driven Fault Injection (RDFI) techniques to address these challenges. RDFI applies the principles of chaos engineering to cloud security and leverages feedback loops to execute, monitor, analyze and plan security fault injection campaigns, based on a knowledge-base. The knowledge-base consists of fault models designed from secure baselines, cloud security best practices and observations derived during iterative fault injection campaigns. These observations are helpful for identifying vulnerabilities while verifying the correctness of security attributes (integrity, confidentiality and availability). Furthermore, RDFI proactively supports risk analysis and security hardening efforts by sharing security information with security mechanisms. We have designed and implemented the RDFI strategies including various chaos engineering algorithms as a software tool: CloudStrike. Several evaluations have been conducted with CloudStrike against infrastructure deployed on two major public cloud infrastructure: Amazon Web Services and Google Cloud Platform. The time performance linearly increases, proportional to increasing attack rates. Also, the analysis of vulnerabilities detected via security fault injection has been used to harden the security of cloud resources to demonstrate the effectiveness of the security information provided by CloudStrike. Therefore, we opine that our approaches are suitable for overcoming contemporary cloud security issues.

It is important to address the physical and cyber security needs of critical infrastructures, including systems, facilities, and assets. Security requirements may include capabilities to prevent and protect against both physical and digital intrusion, hazards, threats, and incidents, and to expeditiously recover and reconstitute critical services. Energy security has serious repercussions for mission critical facilities. Mission critical facilities do not have the luxury of being able to shut down or run at a reduced capacity during outages, whether they last minutes, hours, or days. Disaster recovery plans are a necessity for mission critical facilities, involving the proper training of business continuity personnel to enact enterprise-level plans for business resiliency. Steps need to be taken to improve information security and mitigate the threat of cyber-attacks. The Smart Grid is the convergence of electric distribution systems and modern digital information technology.

The Edward Snowden data breach of 2013 clearly illustrates the damage that insiders can do to an organization. An insider's knowledge of an organization allows them legitimate access to the systems where valuable information is stored. Because they belong within an organizations security perimeter, an insider is inherently difficult to detect and prevent information leakage. To counter this, proactive measures must be deployed to limit the ability of an insider to steal information. Email monitoring at the edge is can easily be monitored for large file exaltation. However, USB drives are ideally suited for large-scale file extraction in a covert manner. This work discusses a process for disabling write-access to USB drives while allowing read-access. Allowing read-access for USB drives allows an organization to adapt to the changing security posture of the organization. People can still bring USB devices into the organization and read data from them, but exfiltration is more difficult.

Electronic crimes like data breaches in healthcare systems are often a fundamental failures of access control mechanisms. Most of current access control systems do not provide an accessible way to engage users in decision making processes, about who should have access to what data and when. We advocate that a policy ontology can contribute towards the development of an effective access control system by attributing responsibility for data breaches. We propose a responsibility attribution model as a theoretical construct and discuss its implication by introducing a cost model for data breach countermeasures. Then, a policy ontology is presented to realize the proposed responsibility and cost models. An experimental study on the performance of the proposed framework is conducted with respect to a more generic access control framework. The practicality of the proposed solution is demonstrated through a case study from the healthcare domain.

The increased use of cloud services and its various security and privacy challenges such as identity theft, data breach, data integrity and data confidentiality has made trust management, which is one of the most multifaceted aspect in cloud computing, inevitable. The growing reputation of cloud computing technology makes it immensely important to be acquainted with the meaning of trust in the cloud, as well as identify how the customer and the cloud service providers establish that trust. The traditional trust management mechanisms represent a static trust relationship which falls deficit while meeting up the dynamic requirement of cloud services. In this paper, a conceptual zero trust strategy for the cloud environment has been proposed. The model offers a conceptual typology of perceptions and philosophies for establishing trust in cloud services. Further, importance of trust establishment and challenges of trust in cloud computing have also been explored and discussed.

Personally identifiable information (PII) has become a major target of cyber-attacks, causing severe losses to data breach victims. To protect data breach victims, researchers focus on collecting exposed PII to assess privacy risk and identify at-risk individuals. However, existing studies mostly rely on exposed PII collected from either the dark web or the surface web. Due to the wide exposure of PII on both the dark web and surface web, collecting from only the dark web or the surface web could result in an underestimation of privacy risk. Despite its research and practical value, jointly collecting PII from both sources is a non-trivial task. In this paper, we summarize our effort to systematically identify, collect, and monitor a total of 1,212,004,819 exposed PII records across both the dark web and surface web. Our effort resulted in 5.8 million stolen SSNs, 845,000 stolen credit/debit cards, and 1.2 billion stolen account credentials. From the surface web, we identified and collected over 1.3 million PII records of the victims whose PII is exposed on the dark web. To the best of our knowledge, this is the largest academic collection of exposed PII, which, if properly anonymized, enables various privacy research inquiries, including assessing privacy risk and identifying at-risk populations.

A security breach often makes companies react by changing their attitude and approach to security within the organization. This paper presents an in-depth case study of post-breach security changes made by a company and the consequences of those changes. We employ the principles of participatory action research and humble inquiry to conduct a long-term study with employee interviews while embedded in the organization's security division. Despite an extremely high level of financial investment in security, and consistent attention and involvement from the board, the interviews indicate a significant level of friction between employees and security. In the main themes that emerged from our data analysis, a number of factors shed light on the friction: fear of another breach leading to zero risk appetite, impossible security controls making non-compliance a norm, security theatre underminining the purpose of security policies, employees often trading-off security with productivity, and as such being treated as children in detention rather than employees trying to finish their paid jobs. This paper shows that post-breach security changes can be complex and sometimes risky due to emotions often being involved. Without an approach considerate of how humans and security interact, even with high financial investment, attempts to change an organization's security behaviour may be ineffective.

Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.

The widespread adoption of social networking and cloud computing has transformed today's Internet to a trove of personal information. As a consequence, data breaches are expected to increase in gravity and occurrence. To counteract unintended data disclosure, a great deal of effort has been dedicated in devising methods for uncovering privacy leaks. Existing solutions, however, have not addressed the time- and data-intensive nature of leak detection. The shift from hardware-specific implementation to software-based solutions is the core idea behind the concept of Network Function Virtualization (NFV). On the other hand, the Software Defined Networking (SDN) paradigm is characterized by the decoupling of the forwarding and control planes. In this paper, an SDN/NFV-enabled architecture is proposed for improving the efficiency of leak detection systems. Employing a previously developed identification strategy, Personally Identifiable Information detector (PIID) and load balancer VNFs are packaged and deployed in OpenStack through an NFV MANO. Meanwhile, SDN controllers permit the load balancer to dynamically redistribute traffic among the PIID instances. In a physical testbed, tests are conducted to evaluate the proposed architecture. Experimental results indicate that the proportions of forwarding and parsing on total overhead is influenced by the traffic intensity. Furthermore, an NFV-enabled system with scalability features was found to outperform a non-virtualized implementation in terms of latency (85.1%), packet loss (98.3%) and throughput (8.41%).