Malicious Hackers Research Articles

Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Ciphertext retrieval under heterogeneous data sets is a critical concern in the cloud-assisted Internet of Things (IoT). Previous ciphertext retrieval schemes eliminate computation burden and security concerns to some extent, but cannot support the encrypted heterogeneous data sets. To solve this problem, in this article we propose a transparent ciphertext retrieval system regardless of programming language, accessible platforms, and heterogeneity of data sets. The retrieval system is a novel combination of the proposed access and integration middleware with a current encrypted cloud database system. The middleware enables cross-language and cross-platform queries over various database systems, besides, it schedules the designed data integration method in heterogeneous databases to realize cross-database queries over the encrypted cloud data sets. The proposed ciphertext retrieval system achieves efficient data sharing among different IoT applications with various database systems. We prove that the system guarantees data security by avoiding malicious hackers. Extensive experiments show that our design is efficient and feasible in practice.

Cyber Security Frameworks

Abstract: In this paper we attempt to explain and establish certain frameworks that can be assessed for implementing security systems against cyber-threats and cyber-criminals. We give a brief overview of electronic signature generation procedures which include its validation and efficiency for promoting cyber security for confidential documents and information stored in the cloud. We strictly avoid the mathematical modelling of the electronic signature generation process as it is beyond the scope of this paper, instead we take a theoretical approach to explain the procedures. We also model the threats posed by a malicious hacker seeking to induce disturbances in the functioning of a power transmission grid via the means of cyber-physical networks and systems. We use the strategy of a load redistribution attack, while clearly acknowledging that the hacker would form its decision policy on inadequate information. Our research indicate that inaccurate admittance values often cause moderately invasive cyber-attacks that still compromise the grid security, while inadequate capacity values result in comparatively less efficient attacks. In the end we propose a security framework for the security systems utilised by companies and corporations at global scale to conduct cyber-security related operations. Keywords: Electronic signature, Key pair, sequence modelling, hacker, power transmission grid, Threat response, framework.

Accessible from the open web: a qualitative analysis of the available open-source information involving cyber security and critical infrastructure

Abstract In order to efficiently manage and operate industrial-level production, an increasing number of industrial devices and critical infrastructure (CI) are now connected to the internet, exposed to malicious hackers and cyberterrorists who aim to cause significant damage to institutions and countries. Throughout the various stages of a cyber-attack, Open-source Intelligence (OSINT) tools could gather data from various publicly available platforms, and thus help hackers identify vulnerabilities and develop malware and attack strategies against targeted CI sectors. The purpose of the current study is to explore and identify the types of OSINT data that are useful for malicious individuals intending to conduct cyber-attacks against the CI industry. Applying and searching keyword queries in four open-source surface web platforms (Google, YouTube, Reddit, and Shodan), search results published between 2015 and 2020 were reviewed and qualitatively analyzed to categorize CI information that could be useful to hackers. Over 4000 results were analyzed from the open-source websites, 250 of which were found to provide information related to hacking and/or cybersecurity of CI facilities to malicious actors. Using thematic content analysis, we identified three major types of data malicious attackers could retrieve using OSINT tools: indirect reconnaissance data, proof-of-concept codes, and educational materials. The thematic results from this study reveal an increasing amount of open-source information useful for malicious attackers against industrial devices, as well as the need for programs, training, and policies required to protect and secure industrial systems and CI.

Exploring novel adversarial attack strategies on android systems

As the proliferation and adoption of mobile phones becomes more and more common, so do the targets for malicious hackers, especially on the widely used android platform. Even the most secure anti-malware systems do not stand a chance against some of the attacks that malware authors develop, despite using machine learning based methods to thwart such attempts. Therefore, to evaluate the vulnerability of machine learning models, we propose two attack scenarios, the first based on reinforcement learning techniques and the second on a deep learning technique, that will perturb malicious samples to appear benign. This will in turn yield greater rate of misclassification. To further distinguish these adversarial examples, we propose one defensive scenario that will help make these classification models more robust against such attacks.We will use three publicly available datasets to benchmark our models.

Sarima-Based Cyber-Risk Assessment and Mitigation Model for A Smart City’s Traffic Management Systems (Scram)

ABSTRACT A malicious hacker can compromise the integrated traffic management systems (ITMS) speed sensors in a smart city, causing a vehicle to misread the speed and result in collision or congestion on the pathways. Based on protection-motivation theory (PMT), we propose a Time series-based Cyber-risk Assessment and Mitigation for smart cities (SCRAM) model comprising three modules for the smart city administrator. In line with the threat appraisal component of PMT, our cyber-risk assessment (CRA) module uses a Seasonal time series forecasting method, takes ITMS average speed as an input, and outputs the probability of anomalies in the average speed of vehicles. Subsequently, based on the concepts of risk theory, the cyber-risk quantification (CRQ) module calculates the expected loss for a smart city due to collision or congestion on the pathways. In line with the coping appraisal component of PMT, our cyber-risk mitigation (CRM) module proposes strategies for the smart-city administrator to reduce cyber-risk using technological means and pass the residual risk to third-party cyber-insurer.

Telemedicine Applications in the Era of COVID-19: Telesurgery Issues.

Telemedicine allows for the effective delivery of health care to patients at a distance through the application of information technology to the field of medicine. This is optimal during the COVID-19 pandemic to reduce interpersonal contact to mitigate contagion. Among the possible Telemedicine applications, there is Telesurgery, which involves more and more surgical specialties thanks to the numerous benefits in quality and cost containment. In the growing field of Telesurgery, its technical and legal implications must be considered. In this study, a traditional review of the scientific literature was carried out to identify the most relevant issues of interest in Telesurgery. The problematic legal aspects identified are mainly related to the difference in legislation between different geographical areas, which is critical in the case of malpractice. In addition, there is the possibility of a malicious hacker attack on the transmitted data stream either to steal sensitive data or to harm the patient. Finally, there are inherent difficulties with the technology used, such as latency issues in data transmission. All these critical issues are currently not adequately addressed by current legislation. Therefore, one can only hope for a legislative action to allow Telesurgery to be used safely.

CYBERCRIME: A MULTIFACETED ONE

Cybercrime has caused widespread damage to personalities, companies, including the city council. Computers have unleashed an era of greater efficiency and creativity. Communication and connectivity have reached new heights in the last twenty years. The Internet started a new revolution, the online revolution. As the masses are accustomed and inclined to their daily activities online, most are looking for easy money and information. This criterion represents modern criminals who, in addition to the availability of extensive equipment to access almost all systems, enjoy the anonymity of boundless cyberspace and therefore take human error and system vulnerabilities for granted. The batch consists of these cyber criminals, malicious hackers, malicious hackers, and spammers. This paper attempts to understand cybercrime on a long continuum, starting with the doctrinal research method part. Research and analysis of the world's leading cybersecurity companies were integrated. Without delving into the actual instruments of manipulation, an attempt was made to visualize all progress as a series of activities.

In situ infrared temperature sensing for real-time defect detection in additive manufacturing

Melt pool temperature is a critical parameter for the majority of additive manufacturing processes. Monitoring of the melt pool temperature can facilitate the real-time detection of various printing defects such as voids, over-extrusion, filament breakage, clogged nozzle, etc. that occur either naturally or as the result of malicious hacking activity. This study uses an in situ, multi-sensor approach for monitoring melt pool temperature in which non-contact infrared temperature sensors with customized field of view move along with the extruder of a fused deposition modeling-based printer and sense melt pool temperature from a very short working distance regardless of its X-Y translational movements. A statistical method for defect detection is developed and utilized to identify temperature deviations caused by intentionally implemented defects. Effective detection for multiple defect types and sizes is demonstrated using both a simple L-shaped test geometry and a more complex industry standard test article. Strengths and limitations of this approach are presented, and the potential for expansion via more advanced data analysis techniques such as machine learning are discussed.

Informing Cyber Threat Intelligence through Dark Web Situational Awareness: The AZSecure Hacker Assets Portal

To increase situational awareness, major cybersecurity platforms offer Cyber Threat Intelligence (CTI) about emerging cyber threats, key threat actors, and their modus operandi. However, this intelligence is often reactive, as it analyzes event log files after attacks have already occurred, lacking more active scrutiny of potential threats brewing in cyberspace before an attack has occurred. One intelligence source receiving significant attention is the Dark Web, where significant quantities of malicious hacking tools and other cyber assets are hosted. We present the AZSecure Hacker Assets Portal (HAP). The Dark Web-based HAP collects, analyzes, and reports on the major Dark Web data sources to offer unique perspective of hackers, their cybercriminal assets, and their intentions and motivations, ultimately contributing CTI insights to improve situational awareness. HAP currently supports 200+ users internationally from academic institutions such as UT San Antonio and National Taiwan University, law enforcement entities such as Calgary and Ontario Provincial Police, and industry organizations including General Electric and PayPal.

Towards a Compressive-Sensing-Based Lightweight Encryption Scheme for the Internet of Things

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the Cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this work, we propose Kryptein, a compressive-sensing-based lightweight encryption scheme for Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Kryptein supports random compressed encryption, statistical computation over cipher, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices. After comparing with other state-of-the-art lightweight ciphers on IoT (Simon and Speck), IoT system with Kryptein is expected to have a much more longevity with about 35 percent extended lifetime. Further, experiments illustrated IoT data variance will not affect Kryptein's accuracy in a long term usage, and Krpytein is also able to support basic analytics tasks like machine learning (e.g., classification).

VG3 Cipher for Secure Image Transmission

The rampant growth of Internet and communication has revolutionized the way people communicate and it has led to rapid enhancement in modern information warfare. The world around us requires the information security more than ever before due to copious reasons primarily being the importance of financial and personal data which can be utilized by the malicious hackers to gain undue advantage. Out of innumerable methodologies available for data safeguarding, cryptography has always been an indispensable tool. However, the changing ways of cyber-attacks demand an upgraded cryptographic version that can handle potential attacks on media of myriad data types. This paper proposes one such encrypting technique which is based on biological methods and used principally for the encoding of digital images based on amalgamation of DNA encryption and Rubik’s cube.

Restrictive deterrence and the scope of hackers’ reoffending: Findings from two randomized field trials

Extensive criminological research has investigated the effectiveness of law enforcement efforts to prevent offenders' reoffending and effect changes in criminal behavior patterns. However, no research has quantified the influence of gossip regarding law enforcement operations and its effect on repeat offending. Moreover, only scant research has studied these relationships in the cyber-environment. The current work addresses this gap by studying the effect of online messages sent to active hackers that describe law enforcement efforts against website defacement activity. In two field experiments, we randomly assigned self-identified hackers with active Facebook accounts into control and treatment groups. We then sent subjects in the treatment group a gossip message (via Facebook) alerting them to law enforcement efforts in cyberspace. Following the intervention, we compared the control and treatment groups in terms of changes in the proportion of reoffending, number of website defacements, and severity of website defacements. Findings reveal that when the gossip was sent to a hacker's private inbox, it prompted a reduction in the proportion of hackers who reoffend, the frequency at which they reoffend, and the severity of attacks they generate. However, posting similar gossip on the hacker's Facebook wall was ineffective in restricting malicious hacking activity. Theoretical implications and practical recommendations for law enforcement operations in cyberspace are discussed.

HackerScope: the dynamics of a massive hacker online ecosystem

One would have thought that hackers would be striving to hide from public view, but we find that this is not the case: they have a public online footprint. Apart from online security forums, this footprint appears also in software development platforms, where authors create publicly accessible malware repositories to share and collaborate. With the exception of a few recent efforts, the existence and the dynamics of this community has received surprisingly limited attention. The goal of our work is to analyze this ecosystem of hackers in order to: (a) understand their collaborative patterns and (b) identify and profile its most influential authors. We develop HackerScope, a systematic approach for analyzing the dynamics of this hacker ecosystem. Leveraging our targeted data collection, we conduct an extensive study of 7389 authors of malware repositories on GitHub, which we combine with their activity on four security forums. From a modelling point of view, we study the ecosystem using three network representations: (a) the author-author network, (b) the author-repository network and (c) cross-platform egonets. Our analysis leads to the following key observations: (a) the ecosystem is growing at an accelerating rate as the number of new malware authors per year triples every 2 years, (b) it is highly collaborative, more so than the rest of GitHub authors, and (c) it includes influential and professional hackers. We find 101 authors maintain an online “brand” across GitHub and our online forums. Our study is a significant step towards using public online information for understanding the malicious hacker community.

Design and Implementation of Active Access Control System by Using NFC-based EAP-AKA Protocol

NFC (Near Field Communication) is widely used in day-to-day applications such as in credit cards and smartphones. thus, RFID (Radio Frequency Identification) is being replaced by NFC in many applications. However, the access control system operates on RFID. The existing RFID-based access control systems provide only passive communication, and the authentication process is fragile and vulnerable to malicious hacking because the user information is stored by the reader. Therefore, in this study, we designed and implemented an access control system that uses the NFC-based active authentication technique. To evaluate the system implemented in this study, we performed comparison analysis with the existing NFC security authentication protocols based on whether they provided mutual authentication and whether they could respond to major security threats. The evaluation results showed that the proposed protocol normally supported mutual authentication functions between NFC devices, and ensured secure wireless communications by tackling major security threats that might occur in NFC security authentication protocols.

Research on System Data Security Under Network Technology

Today’s society is a digital society, all data can be stored digitally, which leads to the explosion of data growth. However, the security storage of cloud data is the primary problem that restricts the development of cloud storage. Malicious hackers, software and hardware vulnerabilities and so on will pose a serious threat to users’ data stored in the cloud. This paper combines the typical big data system technical framework and builds the big data security technical framework around the security requirements of big data. Under this framework, the research status of key technologies of big data security is systematically summarized from multiple aspects, including the main security mechanisms involved in the technical framework of big data system. Finally, the core problems and development trends of big data security technology to be solved are summarized.

A Cyber-Security Methodology for a Cyber-Physical Industrial Control System Testbed

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems in different critical infrastructures, addressing cyber-security challenges of these systems is vital for assuring their reliability and secure operation in presence of malicious cyber attacks. Towards this end, developing a testbed to generate real-time data-sets for critical infrastructure that would be utilized for validation of real-time attack detection algorithms are indeed highly needed. This paper investigates and proposes the design and implementation of a cyber-physical industrial control system testbed where the Tennessee Eastman process is simulated in real-time on a PC and the closed-loop controllers are implemented on the Siemens PLCs. False data injection cyber attacks are injected to the developed testbed through the man-in-the-middle structure where the malicious hackers can in real-time modify the sensor measurements that are sent to the PLCs. Furthermore, various cyber attack detection algorithms are developed and implemented in real-time on the testbed and their performance and capabilities are compared and evaluated.

The Impact of Ransomware, Patterns and Types of Attacks and Organizational Measures Applied to Mitigate Ransomware Attacks

Malicious hackers (Black Hat Hackers) are targeting business and industrial environments with a wide of variety malware attacks such as: worms, viruses, Trojan Horses and ransomware which disrupts or shuts down systems to obtain financial gains, power of knowledge, revenge, or any other purposes. Currently, one of the highest attacks comes from ransomware which numbers of submissions a year have significantly grown over the past few years where criminals look on security vulnerabilities opened due to the rise in remote working to obtain financial gains. Especially in this COVID-19 times where it the number of people working from home is rising more business have been left at risk from ransomware. Thereby, the height alert in preventing ransomware attacks is paramount to educate yourself, in addition, to an adjustment security policy in business organizations. Ransomware is not only created by cybercriminal for the encryption the networks with malware and to demand hundreds of thousands of millions of dollars in different payment methods such as bitcoin but also threaten to leak stolen sensitive data if the perpetrators are not pleased with their ransoms where the law enforcement plays a crucial role to track this criminal’s money flow. This article aims an overall view about ransomware core concepts, the impact generated in current society and the role social engineering tactic plays for ransomware attackers. Lastly, we will discuss about the role of deterrence through security measures that will give insight to assure confidentiality, integrity, and availability to our data against ransomware attacks.

Forensic Issues and Techniques to Improve Security in SSD With Flex Capacity Feature

Over-provisioning technology is typically introduced as a means to improve the performance of storage systems, such as databases. The over-provisioning area is both hidden and difficult for normal users to access. This paper focuses on attack models for such hidden areas. Malicious hackers use advanced over-provisioning techniques that vary capacity according to workload, and as such, our focus is on attack models that use variable over-provisioning technology. According to these attack models, it is possible to scan for invalid blocks containing original data or malware code that is hidden in the over-provisioning area. In this paper, we outline the different forensic processes performed for each memory cell type of the over-provisioning area and disclose security enhancement techniques that increase immunity to these attack models. This leads to a discussion of forensic possibilities and countermeasures for SSDs that can change the over-provisioning area. We also present information-hiding attacks and information-exposing attacks on the invalidation area of the SSD. Our research provides a good foundation upon which the performance and security of SSD-based databases can be further improved.

Internet of Things Security: A Review of Enabled Application Challenges and Solutions

The Internet of Things (IoT) has been widely used in every aspect of life. The rapid development of IoT technologies raises concerns regarding security and privacy. IoT security is a critical concern in the preservation of the privacy and reliability of users’ private information. The privacy concern becomes the biggest barrier to further usage of IoT technology. This paper presents a review of IoT application areas in smart cities, smart homes, and smart healthcare that leverage such techniques from the point of view of security and privacy and present relevant challenges. In addition, we present potential tools to ensure the security and preservation of privacy for IoT applications. Furthermore, a review of relevant research studies has been carried out and discusses the security of IoT infrastructure, the protocols, the challenges, and the solutions. Finally, we provide insight into challenges in the current research and recommendations for future works. The reviewed IoT applications have made life easier, but IoT devices that use unencrypted networks are increasingly coming under attack by malicious hackers. This leads to access to sensitive personal data. There is still time to protect devices better by pursuing security solutions with this technology. The results illustrate several technological and security challenges, such as malware, secure privacy management, and non-security infrastructure for cloud storage that still require effective solutions.

Adversarial Online Learning With Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity

We extend the adversarial/non-stochastic multi-play multi-armed bandit (MPMAB) to the case where the number of arms to play is variable. The work is motivated by the fact that the resources allocated to scan different critical locations in an interconnected transportation system change dynamically over time and depending on the environment. By modeling the malicious hacker and the intrusion monitoring system as the attacker and the defender, respectively, we formulate the problem for the two players as a sequential pursuit-evasion game. We derive the condition under which a Nash equilibrium of the strategic game exists. For the defender side, we provide an exponential-weighted based algorithm with sublinear pseudo-regret. We further extend our model to heterogeneous rewards for both players, and obtain lower and upper bounds on the average reward for the attacker. We provide numerical experiments to demonstrate the effectiveness of a variable-arm play.