Future Attacks Research Articles

Cyber attacks, discretionary loan loss provisions, and banks’ earnings management

Cyber threats to banks can indicate high operational risks and a weak internal control system. Our paper is the first to study the impact of banks’ discretionary loan loss provisions on cyber attacks. We investigate whether discretionary loan loss provision of banks can predict a cyber attack and whether banks conduct less or more earnings management following a cyber attack. We find that discretionary loan loss provisions are positively associated with future cyber attacks. We also find that banks will engage in less earnings management following such incidents. We establish the close link between banks’ earnings management and cyber attacks.

An In-depth Study of Java Deserialization Remote-Code Execution Exploits and Vulnerabilities

Nowadays, an increasing number of applications use deserialization. This technique, based on rebuilding the instance of objects from serialized byte streams, can be dangerous since it can open the application to attacks such as remote code execution (RCE) if the data to deserialize is originating from an untrusted source. Deserialization vulnerabilities are so critical that they are in OWASP’s list of top 10 security risks for web applications. This is mainly caused by faults in the development process of applications and by flaws in their dependencies, i.e., flaws in the libraries used by these applications. No previous work has studied deserialization attacks in-depth: How are they performed? How are weaknesses introduced and patched? And for how long are vulnerabilities present in the codebase? To yield a deeper understanding of this important kind of vulnerability, we perform two main analyses: one on attack gadgets, i.e., exploitable pieces of code, present in Java libraries, and one on vulnerabilities present in Java applications. For the first analysis, we conduct an exploratory large-scale study by running 256515 experiments in which we vary the versions of libraries for each of the 19 publicly available exploits. Such attacks rely on a combination of gadgets present in one or multiple Java libraries. A gadget is a method which is using objects or fields that can be attacker-controlled. Our goal is to precisely identify library versions containing gadgets and to understand how gadgets have been introduced and how they have been patched. We observe that the modification of one innocent-looking detail in a class – such as making it public – can already introduce a gadget. Furthermore, we noticed that among the studied libraries, 37.5% are not patched, leaving gadgets available for future attacks. For the second analysis, we manually analyze 104 deserialization vulnerabilities CVEs to understand how vulnerabilities are introduced and patched in real-life Java applications. Results indicate that the vulnerabilities are not always completely patched or that a workaround solution is proposed. With a workaround solution, applications are still vulnerable since the code itself is unchanged.

Current and Future Applications of Artificial Intelligence in Cardiac CT.

In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications. Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calciumon cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.

Beneficial Microorganisms as a Sustainable Alternative for Mitigating Biotic Stresses in Crops

Nowadays, population growth, the global temperature increase, and the appearance of emerging diseases in important crops generate uncertainty regarding world food security. The use of agrochemicals has been the “go-to” solution for the control of phytopathogenic microorganisms, such as Magnaporte oryzae, causing blast disease in rice and other cereals; Botrytis cinerea, causing gray mold in over 500 plant species; and Puccinia spp., causing rust in cereals. However, their excessive use has harmed human health, as well as ecosystems (contaminating water, and contributing to soil degradation); besides, phytopathogens can develop resistance to them. The inoculation of plant growth-promoting microorganisms (PGPMs) to crops is a sustainable strategy for increasing the yield and quality of crops and mitigating biotic stresses. Likewise, PGPMs, such as Pseudomonas, Bacillus, and Trichoderma, can trigger a series of signals and reactions in the plant that lead to the induction of systemic resistance, a mechanism by which plants react to microorganism stimulation by activating their defense system, resulting in protection against future pathogen attack. These plant defense mechanisms help to mitigate biotic stresses that threaten global food security. Thus, the study of these mechanisms at molecular, transcriptomic, and metabolomic levels is indispensable to elucidate how stresses affect globally important crops.

Terrorist Attacks Against Firefighters, 1970-2019.

Firefighters are a critical component of the emergency response system and therefore a potential target for organizations seeking to disrupt this system. Terrorist organizations may deliberately attack firefighters to both increase the devastation of an attack and impair the affected community's ability to respond to an attack. We performed a focused search of the Global Terrorism Database to identify terrorist attacks against firefighters worldwide. The database includes incidents from 1970 through 2019, with a total of 201,183 entries. These entries were searched for incidents involving firefighters or fire trucks. We analyzed trends in the number of incidents occurring per year, regions of the world impacted, methods employed, and number of casualties inflicted. A total of 42 attacks involving firefighters were identified in the Global Terrorism Database resulting in 26 deaths and 95 wounded. Of the 42 attacks, 12 (28.6%) were secondary attacks, where firefighters responding to an initial attack were themselves targeted. The most common method for both primary and secondary attacks was the use of a bomb or explosive. Although attacks against firefighters are uncommon, they highlight both the strategic value and vulnerability of firefighters to terrorist attacks. Increased efforts must be made to protect firefighters from future terrorist attacks.

ISF: Security Analysis and Assessment of Smart Home IoT-based Firmware

The applications and services offered by the Internet of Things (IoT) have grown significantly during the past few years. Device makers and corporate suppliers have taken notice of this, which has led to a sudden inflow of new-age firms. Confidential data and information are involved as IoT device use rises. IoT device security has emerged as a major issue and is becoming more and more significant. Appropriate security measures are needed to prevent dangers and hazards associated with the adoption of smart technology in smart cities and houses that run IoT devices, according to security evaluations. In order to safeguard the smart home environment, our research focuses on IoT device firmware. The security methodology presented in this research may be used to analyze and investigate IoT firmware, revealing sensitive data and hardcoded user IDs and passwords that can be used in future attacks and breach of IoT devices. The authors put out an idea for how real-time datasets produced by IoT search engines may be analyzed using keywords according to different device kinds, locations, and manufacturers. The results showed that it took device owners 11–13 months to upgrade the firmware. Only HP and Cisco routinely provided firmware updates to protect IoT devices among IoT device makers.

How will the conflict in Ukraine affect the system of international information security?

The article is devoted to the consideration of the main changes taking place in the system of ensuring international information security after the start of a special military operation in Ukraine. An attempt is made to predict the challenges and risks that the IIS system will face in the future. The priority transformations are connected with the negotiation process within the UN, which, in the context of increasing political antagonism between the key subjects of the dialogue, is becoming increasingly politicized and is beginning to experience some difficulties. It is assumed that in the conditions of the new political reality, the issue of “comprehensive” counteraction to international cybercrime will remain unresolved. There is some fragmentation of the international information security system itself, which is experiencing centrifugal tendencies in terms of promoting alternative approaches to solving key issues in this sphere. Finally, it can be expected that the United States, in interaction with Russia, will more actively engage in a proactive strategy of so-called defend forward, which should be implemented through the concept of persistent engagement. The preliminary conclusions demonstrated by the actions in Ukraine regarding the role and place of cyber attacks directly within the framework of the armed conflict are also considered. It is assumed that in the near future cyber attacks will not be considered as a means of achieving real strategic effects on the battlefield, where conventional weapons play a dominant role. An attempt is made to give some recommendations on maintaining a certain dynamic of the development of the IIS system.

Partially Deep-Learning Encryption Technique

The biggest problem facing the world is information security in the digital era. Information protection and integrity are hot topics at all times, so many techniques have been introduced to transmit and store data securely. The increase in computing power is increasing the number of security breaches and attacks at a higher rate than before on average. Thus, a number of existing security systems are at risk of hacking. This paper proposes an encryption technique called Partial Deep-Learning Encryption Technique (PD-LET) to achieve data security. PD-LET includes several stages for encoding and decoding digital data. Data preprocessing, convolution layer of standard deep learning algorithm, zigzag transformation, image partitioning, and encryption key are the main stages of PD-LET. Initially, the proposed technique converts digital data into the corresponding matrix and then applies encryption stages to it. The implementation of encrypting stages is frequently changed. This collaboration between deep learning and zigzag transformation techniques provides the best output result and transfers the original data into a completely undefined image which makes the proposed technique efficient and secure via data encryption. Moreover, its implementation phases are continuously changed during the encryption phase, which makes the data encryption technique more immune to some future attacks because breaking this technique needs to know all the information about the encryption technique. The security analysis of the obtained results shows that it is computationally impractical to break the proposed technique due to the large size and diversity of keys and PD-LET has achieved a reliable security system.

Analysis of the Medical Consequences of Global Terrorist Attacks in Turkic States in the Last 50 Years by Weapon and Attack Type.

This research aimed to conduct an epidemiological analysis of the terrorist attacks, which took place in the Turkic states between 1970 and 2019, and their medical consequences in terms of weapons and attack types. The data collected from this research will be valuable for the development of preventive systems against attacks on Turkic states and offer insights on how to effectively prepare for potential future attacks. The population of the research consisted of the weapons and types of attacks of the terrorist attacks in the Turkic states drawn from the Global Terrorism Database provided free of charge by START. The number of deaths, injuries, property damage, primary weapons, and types of attacks were analyzed by country. Between 1970 and 2019, 4629 terrorist incidents occurred and 7496 people lost their lives and 10 928 people were injured. Among the types of weapons, the number of people who lost their lives was mostly in firearms, whereas the number of the injured was mostly in explosive weapons. Among the types of attacks, the number of people who lost their lives was mostly observed in the armed attack, whereas the injuries occurred mostly in the bombing attacks. Among the Turkic states, Turkey is the country most affected in terms of medical outcomes. The terrorist attacks in the Turkic states reached their maximum number in the last 10 years. It is predicted that this number will increase further in the next years and affect more people medically.

IDENTIFICATION OF UNSATURATED ATTACKS IN VIRTUALIZED INFRASTRUCTURES WITH BIG DATA ANALYTICS IN CLOUD COMPUTING

Security systems to protect virtualized cloud architecture typically include two types of malware detection and security analysis. Detecting malware typically involves two steps, monitoring the hotspots at various points in the virtualized infrastructure, and then using a regularly updated attack signature database to detect the presence of malware. 'Attack. It allows real-time detection of attacks, the use of special signature databases that are vulnerable to zero- day attacks that do not have attack signatures, and therefore traditional infrastructure. cannot detect complex attacks on virtualized infrastructure. Similarly, security analysis eliminates the need for signature databases using event correlation to detect previously undetected attacks, which are often unmanaged, and the current implementation is scalable in nature. In this article, we recommend BDSA's approach to establish a three-tier system for the continuous detection of future attacks. Initially, network logs from the visiting virtual machine and client application logsare sometimes collected from the visiting virtual machines and stored in HDFS. At this point, the strengths of the attack are removed with a connection scheme and a Map Reduce analyzer. Our BDSA approach uses HDFS distribution management and Spark's map-reduction display capability to address security and speed and volume issues.

Fronesis: Digital Forensics-Based Early Detection of Ongoing Cyber-Attacks

Traditional attack detection approaches utilize predefined databases of known signatures about already-seen tools and malicious activities observed in past cyber-attacks to detect future attacks. More sophisticated approaches apply machine learning to detect abnormal behavior. Nevertheless, a growing number of successful attacks and the increasing ingenuity of attackers prove that these approaches are insufficient. This paper introduces an approach for digital forensics-based early detection of ongoing cyber-attacks called Fronesis. The approach combines ontological reasoning with the MITRE ATT&CK framework, the Cyber Kill Chain model, and the digital artifacts acquired continuously from the monitored computer system. Fronesis examines the collected digital artifacts by applying rule-based reasoning on the Fronesis cyber-attack detection ontology to identify traces of adversarial techniques. The identified techniques are correlated to tactics, which are then mapped to corresponding phases of the Cyber Kill Chain model, resulting in the detection of an ongoing cyber-attack. Finally, the proposed approach is demonstrated through an email phishing attack scenario.

Botnet Detection Using Artificial Intelligence

In the year 2021 more than 80 million data were breached by cyber attackers. Most of these attacks were executed as a type of ransomware attack and cyber-attack. There are different methods performed by attackers to target individual user, but to breach an organization, they use Botnet forces. A botnet (robot network), is malware infected network that is controlled by a single attacker called bot-herder. Cyber - attacker attacks many users with their malware script by different mediums like emails, spams and takes command and control (CC) of the victim device. Using these devices attacker forms a network and performs large attack like distributed denial-of-service (DDoS), attack on an organization to breach data. The complex analysis for cybersecurity analyst is to find bot-herder and the infected network. The structures of the botnets are become very different now days. Botnets can be found with its peer-peer (p2p) structure, signature detection, behavioral analysis, domain names (DNS) and network traffic. To make this different feature analysis easier, the usage of artificial intelligence (AI) is introduced in cyber security. Data from previous attacks are collected, trained using a model which helps in prediction of future attacks. Detection of DNS of core CC servers using AI are widely used nowadays. This research mainly focuses on detection of botnet malware from the net flows of malware packets. The botnet attack data set are collected from resources like Czech - university (CTU-13), Information security and object technology (ISOT). The bi-directional net flow data and the calculation of the network packets are used. Using algorithms like support vector machine (SVM), decision tree and multi-layer perceptron, the data set is trained and tested. After the training and testing, the decision tree model has good accuracy and performance metrics of 92%. This model is considered as a best fit model and helps in detection the of malware packets. The research's objective is to build an alerting system which reports once a malware packet is intruded into a network.

ARTIFICIAL INTELLIGENCE FACE RECOGNITION: SECURITY SYSTEM

Face recognition has recently been a popular study topic as a result of the growing demand for security. Security systems, identity verification, access control, surveillance systems, and social networks are just a few of the applications that can be expanded from facial recognition. However, as passwords and fingerprint scanners are supplanted by improved computer technology and more precise algorithms, facial recognition is becoming increasingly popular. Facial recognition technology is the simplest and most coherent of all the techniques. Face recognition does not necessitate an individual’s active participation. Face recognition systems are theoretically capable of minimizing risk and eventually avoiding future attacks in places where identity identification is necessary, such as airports and border crossings. If there are criminals on the loose, the same justification can be made as with surveillance devices. Surveillance cameras with face recognition capabilities will aid in the identification of these individuals. Alternatively, these surveillance systems can be used to track down the whereabouts of missing persons, although this is contingent on the use of powerful facial recognition algorithms and a fully built facial database. Face detection, facial feature extraction (image normalization), face identification, and results are the major operations to be conducted for face recognition.

On the Effectiveness of BGP Hijackers That Evade Public Route Collectors

Routing hijack attacks have plagued the Internet for decades. After many failed mitigation attempts, recent Internet-wide BGP monitoring infrastructures relying on distributed route collection systems, called route collectors, give us hope that future monitor systems can quickly detect and ultimately mitigate hijacks. In this paper, we investigate the effectiveness of public route collectors with respect to future attackers deliberately engineering longer hijacks to avoid being recorded by route collectors. Our extensive simulations (and attacks we device) show that monitor-based systems may be unable to observe many carefully crafted hijacks diverting traffic from thousands of ASes. Hijackers could predict whether their attacks would propagate to some BGP feeders (i.e., monitors) of public route collectors. Then, manipulate BGP route propagation so that the attack never reaches those monitors. This observation remains true when considering plausible future Internet topologies, with more IXP links and up to 4 times more monitors peering with route collectors. We then evaluate the feasibility of performing hijacks not observed by route collectors in the real-world.We experiment with two classifiers to predict the monitors that are dangerous to report the attack to route collectors, one based on monitor proximities (i.e., shortest path lengths) and another based on Gao-Rexford routing policies.We show that a proximity-based classifier could be sufficient for the hijacker to identify all dangerous monitors for hijacks announced to peer-to-peer neighbors. For hijacks announced to transit networks, a Gao-Rexford classifier reduces wrong inferences by ≥ 91% without introducing new misclassifications for existing dangerous monitors.

Захист Осіріса. Формальні міркування щодо висловів 1076 та 1077 текстів саркофагів

One of the main episodes of the Osirian myth is the infamous assault of Seth on his brother Osiris. Their father, the god Geb, took all the precautions to protect the body of his murdered son from any future attack by Seth. A well-protected building housed the dead body, where Geb appointed powerful guards to protect the body of Osiris. Fortunately, few coffins from the Middle Kingdom (2055–1650 BC) preserved for us a strikingly interesting record of these apotropaic beings both in word and image. The standard publication of the Coffin Texts gathered the names of these guardians in two spells with numbers 1076 and 1077. Yet, lack of sufficient documentation and predominant textual bias in previous studies caused a great deal of confusion regarding these guardians as far as their number and visual representation are concerned. This contribution addresses these formal issues and analyzes the arrangement of the names and depictions of the guardians in these coffins. The close scrutiny revealed some hitherto undocumented features in the original sources.

Improved Modeling of Maintenance for Future Vertical Lift

The Future Vertical Lift (FVL) Cross Functional Team (CFT) required analysis on the sustainment performance of FVL aircraft operating in Multi-Domain Operations (MDO). A study from last year forecasted the sustainment performance of FVL’s Future Attack Reconnaissance Aircraft (FARA) based upon historical performance of the enduring AH-64 Apache infused with emerging technologies. This study improved last year’s model by refining key assumptions. First, a Forward Maintenance Team in MDO conducted limited maintenance that was constrained by an austere environment. Second, the assumption of constant component failure rates was updated with increasing hazard rates to reflect fatigue in mechanical systems like the transmission and rotors. Third, technological risk was captured by accounting for the uncertainty associated with infused technologies based on the Technology Readiness Level. Finally, the historical failure rates of the enduring fleet of AH-64, UH-60, and CH-47 aircraft guided reliability forecasts for FARA increments over its life cycle.

Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart Patients Using Multiple Machine Learning Models

About 26 million people worldwide experience its effects each year. Both cardiologists and surgeons have a tough time determining when heart failure will occur. Classification and prediction models applied to medical data allow for enhanced insight. Improved heart failure projection is a major goal of the research team using the heart disease dataset. The probability of heart failure is predicted using data mined from a medical database and processed by machine learning methods. It has been shown, through the use of this study and a comparative analysis, that heart disease may be predicted with high precision. In this study, researchers developed a machine learning model to improve the accuracy with which diseases like heart failure (HF) may be predicted. To rank the accuracy of linear models, we find that logistic regression (82.76 percent), SVM (67.24 percent), KNN (60.34 percent), GNB (79.31 percent), and MNB (72.41) perform best. These models are all examples of ensemble learning, with the most accurate being ET (70.31%), RF (87.03%), and GBC (86.21%). DT (ensemble learning models) achieves the highest degree of precision. CatBoost outperforms LGBM, HGBC, and XGB, all of which achieve 84.48% accuracy or better, while XGB achieves 84.48% accuracy using a gradient-based gradient method (GBG). LGBM has the highest accuracy rate (86.21 percent) (hypertuned ensemble learning models). A statistical analysis of all available algorithms found that CatBoost, random forests, and gradient boosting provided the most reliable results for predicting future heart attacks.

Malware development threats with modern technologies

Despite a significant increase in the level of defence strategies across the enterprise, cyberattacks continue to have a damaging impact on organisations. Due to insufficient threat intelligence capabilities established in many organisations, attackers use this weakness to port their attack procedure and plan future attacks. From highlighting the problem to solving it, this paper explores possible attack procedures and builds awareness to shortcut the risk and reduce the possibility of compromise. It describes a case study of cyberattacks to illustrate the pros and cons of advanced detection and prevention systems.

An Analysis of Cybersecurity Policies and Practices in Public Administration

The purpose of this research was to assess the strengths and weaknesses of a public administration organization's hacking policies and practices. According to the findings, the company has a solid cybersecurity base thanks to its comprehensive policies and ongoing training and awareness programs. Nonetheless, there were also opportunities for development, such as in access controls and vulnerability evaluations. A more thorough cybersecurity policy, the use of role-based access control, and more frequent and thorough vulnerability assessments and penetration testing were among the best practices and improvement suggestions uncovered by the research. The company will be better able to safeguard sensitive data and prevent future attacks by implementing these suggestions. This research serves as a useful reminder of the significance of cybersecurity in public administration and emphasizes the need for constant vigilance and advancement in order to remain one step ahead of new threats.

Precisely predicting the 1H and 13C NMR chemical shifts in new types of nerve agents and building spectra database

Following the recent terrorist attacks using Novichok agents and the subsequent decomposition operations, understanding the chemical structures of nerve agents has become important. To mitigate the ever-evolving threat of new variants, the Organization for the Prohibition of Chemical Weapons has updated the list of Schedule 1 substances defined by the Chemical Weapons Convention. However, owing to the several possible structures for each listed substance, obtaining an exhaustive dataset is almost impossible. Therefore, we propose a nuclear magnetic resonance-based prediction method for 1H and 13C NMR chemical shifts of Novichok agents based on conformational and density functional study calculations. Four organophosphorus compounds and five G- and V-type nerve agents were used to evaluate the accuracy of the proposed procedure. Moreover, 1H and 13C NMR prediction results for an additional 83 Novichok candidates were compiled as a database to aid future research and identification. Further, this is the first study to successfully predict the NMR chemical shifts of Novichok agents, with an exceptional agreement between predicted and experimental data. The conclusions enable the prediction of all possible structures of Novichok agents and can serve as a firm foundation for preparation against future terrorist attacks using new variants of nerve agents.