Cyber Warfare Research Articles

A Novel Security Risk Analysis Using the AHP Method in Smart Railway Systems

Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will cause a damaged corporate reputation and revenue losses. In this study, cybersecurity attack methods in smart rail systems were determined, and cyber events occurring worldwide through these technologies were analyzed. Risk analysis in terms of transportation safety in smart rail systems was determined by considering the opinions of 10 different experts along with the Analytic Hierarchical Process (AHP) performance criteria. Informatics experts were selected from a group of people with at least 5–15 years of experience. According to these risk analysis calculations, cybersecurity stood out as the most critical security risk at 27.74%. Other risky areas included physical security, calculated at 14.59%, operator errors at 16.20%, and environmental security at 10.93%.

A novel framework of DDoS attack detection in network using hybrid heuristic deep learning approaches with attention mechanism

The “Distributed Denial of Service (DDoS)” threats have become a tool for the hackers, cyber swindlers, and cyber terrorists. Despite the high amount of conventional mitigation mechanisms that are present nowadays, the DDoS threats continue to enhance in severity, volume, and frequency. The DDoS attack has highly affected the availability of the networks for the previous years and still, there is no efficient defense technique against it. Moreover, the new and complex DDoS attacks are increasing on a daily basis but the traditional DDoS attack detection techniques cannot react to these threats. On the other hand, the hackers are employing very innovative strategies to initiate the threats. But, the traditional methods can become effective and reliable when combined with the deep learning-aided approaches. To solve these certain issues, a framework detection mechanism for DDoS attacks utilizes an attention-aided deep learning methodology. The primary thing is the acquisition of data from standard data online sources. Further, from the garnered data, the significant features are drawn out from the “Deep Weighted Restricted Boltzmann Machine (RBM)” using a “Deep Belief Network (DBN)”, in which the parameters are tuned by employing the recommended Enhanced Gannet Optimization Algorithm (EGOA). This feature extraction operation increases the network performance rate and also diminishes the dimensionality issues. Lastly, the acquired features are transferred to the model of “Attention and Cascaded Recurrent Neural Network (RNN) with Residual Long Short Term Memory (LSTM) (ACRNN-RLSTM)” blocks for the DDoS threat detection purpose. This designed network precisely identifies the complex and new attacks, thus it increases the trustworthiness of the network. In the end, the performance of the approach is contrasted with other traditional algorithms. Hence, the simulation outcomes are obtained that prove the system’s efficiency. Also, the outcomes displayed that the designed system overcame the conventional threat detection techniques.

Third-party countries in cyber conflict: Public opinion and conflict spillover in cyberspace

The transnational nature of cyberspace alters the role of third-party countries (TPCs) in international conflict. In the conventional environment, military operations are primarily confined to the boundaries of the combatants or a designated war zone. However, during cyber conflicts, operations may occur on the digital infrastructure of states not otherwise involved in the dispute. Nevertheless, within the cyber conflict literature, little is said about TPCs who, by virtue of interconnectivity, may find themselves involved in a conflict not of their own making. Consequently, we examine the political and diplomatic hazards of cyber operations involving these actors. Through survey experiments involving participants from the United Kingdom and Canada, we assess the public opinion impact of an offensive cyber operation’s revelation on a TPC population. We find that while these incidents are viewed negatively, prior authorization and the involvement of an ally reduces this tendency. Such conditions lead the public to perceive these operations as corresponding with their national interest while suppressing fears of the possible consequences following their indirect involvement.

Evaluate The Role of The Community in Intelligence Gathering on Local Terrorism Activities

Terrorism poses a significant national security threat in Indonesia, exacerbated by the clandestine nature of terrorist networks within society. Harnessing community involvement is pivotal in combating terrorism effectively. This study evaluates the community's role in intelligence gathering pertaining to local terrorist activities. Utilizing qualitative research methods, particularly literature review, the study analyzes data through three key stages: data reduction, presentation, and conclusion drawing. Findings underscore the indispensable contribution of communities in gathering local terrorism intelligence. Unlike conventional channels, communities offer unique insights into terrorist activities inaccessible to law enforcement agencies. However, leveraging community involvement faces challenges and limitations. These include the need for heightened education and training initiatives to enhance community awareness about terrorism. Additionally, fostering trust between communities and law enforcement entities emerges as a critical endeavor. Furthermore, ensuring the safety and protection of individuals reporting terrorism-related information remains imperative for sustained community participation in intelligence-gathering efforts.

Analysis and Prevention of AI-Based Phishing Email Attacks

Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.

A vehicle firmware security vulnerability: an IVI exploitation

In the last years, the increasing vehicular technology has led to a surge in cybersecurity attacks, particularly regarding connected vehicles and their vulnerable infotainment systems. This paper explores the vulnerabilities within an In-Vehicle Infotainment (IVI) system firmware, focusing on the Gen5W_L multimedia device utilized in Hyundai, Kia, and Genesis vehicles. Leveraging reverse engineering techniques, the study uncovers several security issues within the firmware, allowing for the creation and installation of custom firmware. The paper introduces Chimaera, a reverse engineering attack targeting the IVI system firmware, which exploits a printf vulnerability introduced by statically compiling the mbedtls library. After a reverse engineering analysis and injecting malicious assembly code directly in the IVI firmware, the attack enables the extraction of sensitive information from the firmware files like the cryptographic keys to install a customized firmware. The research shows that exploiting these vulnerabilities facilitates the installation of insecure firmware, granting attackers unauthorized access to vehicle functionalities. This includes the possibility to inject CAN bus messages, potentially compromising critical vehicle systems such as radio controls and safety features. The findings underscore the need for robust cybersecurity measures within automotive systems and highlight the significance of vulnerability assessment to mitigate security risks in connected vehicles.

Light-speed, contemporary war, and Australia’s national defence strategic review

In our hyperconnected contemporary world, military and civilian digital and cyber technologies rely upon uncontested and uncongested access to frequencies in the electromagnetic spectrum (EMS). This reliance enables light-speed signalic connectivity, interconnectivity, operability, and interoperability of the systems and devices that perpetuate twenty-first century modes of information, remote, hybrid, and digital warfare. Informed by cultural theorist Paul Virilio’s commentaries on speed, light-speed, and war, this article examines speed as an inflection that insidiously underpins the Australian Government’s 2023 National Defence: Defence Strategy Review (DSR) (public version). This is not an exhaustive study of light-speed or the DSR. Rather, this article aims to show how speed and light-speed, used as investigatory lenses, can provide critical insights into relationships between contemporary technology, and war. To this end, the article refers to US and UK defence and government electromagnetic spectrum policy statements, interpolating them into motivations for AUKUS, and the DSR’s positioning.

CYBER SECURITY ATTACK DETECTION MODEL USING SEMI-SUPERVISED LEARNING

The increasing digitalization of our society has brought about numerous benefits, enabling seamless communication, convenient transactions, and efficient operations. However, with this growing reliance on interconnected systems and information technology, the risk of cyber-attacks has also surged. Cyber threats, such as data breaches, ransomware, and sophisticated malware, have become more prevalent, threatening the confidentiality, integrity, and availability of critical data and services. Organizations across industries face the daunting challenge of defending against a wide array of cyber-attacks that continue to evolve in complexity and stealth. In response to this ever-changing cyber threat landscape, Cyber Security Risk Management (CSRM) and attack detection have become critical components of any comprehensive cybersecurity strategy. The ability to identify and mitigate cyber risks and swiftly detect malicious activities is paramount for safeguarding sensitive information, preserving business continuity, and maintaining the trust of customers and stakeholders. A novel approach to Cyber Security Risk Management through an Attack Detection Model that utilizes Semi-Supervised Learning Auto-Encoders in conjunction with Probabilistic Bayesian Networks. The study compares the performance of Multi Connect Variational Auto-Encoder (MC-VAE), Probabilistic Bayesian Networks (PBN), and a combined model of MC-VAE and PBN. The study employs the NUSW-NB15_GT dataset for training and evaluation purposes. Notably, the Semi-Supervised Learning with Probabilistic Bayesian Networks (SSL-PBN) model demonstrates exceptional results, achieving a precision rate of 94% and a recall rate of 90%. The F1 score of 0.9191 highlights the SSL-PBN model's efficacy in achieving a balanced trade-off between precision and recall, critical for minimizing false positives and false negatives...

Prevention and Mitigation of Disruptions in Medical Device Supply Chains: A Policy Perspective

New technologies and their connectivity to the internet render medical devices and their supply chains a target for worldwide criminal attacks. Disruptions to a medical device supply chain (MDSC), including cybersecurity attacks, have increased rapidly, with some sophisticated attacks compromising the availability and operation of life-critical devices. Understanding the impact of disruptions on MDSCs is difficult due to the attendant supply chain complexity. The lack of a systematic classification of disruptions to MDSCs limits the transferability of mitigation strategies. Thus, there is a need for evidence-based, actionable policy guidance for corporations and government agencies that address disruption risks to MDSCs. This paper first presents a disruption classification scheme based on historical and anticipated disruptions to MDSCs. Then, it introduces a model, dubbed the Focal-firm Supply-chain Integrated Model (FSIM), that integrates a focal-firm view of supply chains and attendant disruptions, illustrated through an MDSC example. Based on lessons learned from constructing a disruption classification and creating an FSIM map, we describe the following actionable insights: a) implementing procedures and technologies that enable early-detection of disruptions, b) accurate and timely information flows, c) continual monitoring of physical inventory levels and comparing them with digital data, d) enhanced and repeated testing at various supply chain testing sites, e) standardized metrics that measure the impact of disruptions, f) developing in advance a preparedness strategy and a planning process to allocate resources effectively, equitably, and transparently during disruptions, and g) the need for a common framework that bridges the medical device regulatory gaps across countries. These insights can guide MDSC stakeholders, including manufacturers, suppliers, service providers, healthcare providers, policymakers, and government agencies.

Cyberbiosecurity in the new normal: Cyberbio risks, pre-emptive security, and the global governance of bioinformation

Abstract The Covid-19 pandemic saw a surge in cyber attacks targeting pharmaceutical companies and research organisations working on vaccines and treatments for the virus. Such attacks raised concerns around the (in)security of bioinformation (e.g. genomic data, epidemiological data, biomedical data, and health data) and the potential cyberbio risks resulting from stealing, compromising, or exploiting it in hostile cyber operations. This article critically investigates threat discourses around bioinformation as presented in the newly emerging field of ‘cyberbiosecurity’. As introduced by scholarly literature in life sciences, cyberbiosecurity aims to understand and address cyber risks engendered by the digitisation of biology. Such risks include, for example, embedding malware in DNA, corrupting gene-sequencing, manipulating biomedical materials, stealing epidemiological data, or even developing biological weapons and spreading diseases. This article brings the discussion on cyberbiosecurity into the realms of International Relations and Security Studies by problematising the futuristic threat discourses co-producing this burgeoning field and the pre-emptive security measures it advocates, specifically in relation to bioinformation. It analyses how cyberbiosecurity as a concept and field of policy analysis influences the existing securitised governance of bioinformation, the global competition to control it, and the inequalities associated with its ownership and dissemination. As such, the article presents a critical intervention in current debates around the intersection between biological dangers and cyber threats and in the calls for ‘peculiar’ policy measures to defend against cyberbio risks in the ‘new normal’.

Indonesia's Digital Security Strategy: Countering the Threats of Cybercrime and Cyberterrorism

Cybercrime is becoming a very serious threat to Indonesia's national security. The pervasive nature of cyberattacks raises questions about the effectiveness of current regulatory measures. This research aims to analyze how the government addresses security challenges in cyberspace in a sustainable manner. A descriptive qualitative method was used to evaluate the impact of cybercrime in Indonesia and its relationship with highly interrelated regulations. The results show that the threat of cybercrime and cyber-terrorism, in both physical and digital forms, has increased alarmingly in recent years. The impact of universal access to all relevant information has several consequences, including the spread of false information and the development of cybercrime networks. Significant measures are needed to disrupt the cyber terrorism industry to create a safe environment in Indonesia. The success of regulatory measures in Indonesia relies on a holistic approach involving the active participation of the government, society, and the private sector.

A method of network attack-defense game and collaborative defense decision-making based on hierarchical multi-agent reinforcement learning

Faced with the challenges of security strategy design brought about by the complexity of attack behavior and the dynamism of network structure, dynamic hierarchical intelligent defense methods have shown their effectiveness. However, in complex network environments, their application requires a higher level of coordination mechanisms. Therefore, this paper proposes a hierarchical multi-agent reinforcement learning network attack and defense game and cooperative defense decision-making method, which autonomously and efficiently completes the formulation of defense strategies and defense behavior responses. Firstly, we construct a Stackelberg hypergame model of cyberspace conflicts, and under the condition of information loss, characterize the multi-layer dynamic defense coordination response mechanism. Secondly, By utilizing a hierarchical multi-agent reinforcement learning method as the driving force for game evolution, we sequentially solve the Nash equilibrium of the game, and form a dynamic autonomous defense strategy. Finally, we construct a hierarchical multi-agent reinforcement learning framework, which decouples the defense decision problem, reduces the dimension of the defense action space and the exploration difficulty of the strategy space, and learns coordinated defense strategies more efficiently. We used the CybORG (Cyber Operations Research Gym) environment for simulation. We compared and analyzed the autonomously generated cyber defense strategies with other related works, verifying the superior coordination performance of our method in defense strategy generation and control.

Cyber security attack recognition on cloud computing networks based on graph convolutional neural network and graphsage models

In this paper, the modeling of the network attacks of cloud computing through Graph Neural Networks is considered. Based on structural features and relationships between neighboring nodes and the edges of the cloud ecosystem a cyberattack detection method is proposed. A simulation dataset is created on the CSE-CIC-IDS2018 dataset to train and test the proposed graph neural network based models. In a comparative analysis of the suggested method with the existing one superior results are obtained from the model constructed on the GraphSAGE algorithm. Thus in the recognition of dataset samples, the model obtained a value of 0.97739 according to the accuracy metric. The values obtained by the algorithm on precision, recall, and F1-score metrics were also higher compared to the Graph Convolutional Neural Network model.

Artificial Intelligence Computer Weapon Based True Research on Corona Virus [Covid-19

Electronic torture, Electromagnetic radiation torture, or Psychotronic torture are terms used by individuals who are targeted by this wireless Synthetic telepathic technology or Direct Energy Weapon [Electromagnetic Radiation weapon]. Wireless Synthetic telepathy technology or Super artificial intelligence computers Criminal operators, often actors or often government employees, agents or crime syndicates, use transmitted electromagnetic radiation (Such as microwave listening effects), satellite technology and surveillance techniques. Wireless Remote Neural Monitoring: Bridging the gap between brain and technology In the field of neuroscience and technological advancement, the concept of deep tech satellite wireless neural monitoring technology has emerged as an unprecedented innovation weapon. Which has been developed to read and understand the human brain and to interact with the human brain and understand the human body, it is also a wireless direct energy weapon. These deep tech satellite surveillance criminal operators are using this Neurotechnology and wireless Direct Energy Weapons on a large scale against civilians. This true research has been done to protect citizens from illegal deep tech satellite surveillance weapon technology and cyber terrorism. The researcher has presented his research to the whole world in very simple words and in an easy manner. So that the world can easily understand this research and the governments of the countries of the world can protect their citizens from future man-made diseases and viruses.

Operational cyber incident coordination revisited: providing cyber situational awareness across organizations and countries

ABSTRACT Cyber situational awareness (CSA) is a prerequisite for justified decision-making and to maintain cyber security. This becomes particularly complex when establishing inter-organizational awareness across sectors. For example, computer security incident response teams (CSIRTs) and national cyber security centers need to establish CSA among countries when coordinating regional cyber incident response. Today’s state of the art of information sharing across larger numbers of organizations is often still the least common denominator in the shape of web-based forms and email reports. These are easily applicable by almost everyone who wants to report findings even in stressful situations. However, these do not prove to be efficient for the coordinator that aggregates and merges the data. Therefore, a cyber coordination platform using online surveys is proposed. This approach uses surveys to collect, aggregate and visualize data in a dashboard to support cyber coordination and knowledge management. Furthermore, the online surveys are easy to use and respond to and therefore simplify the participation of stakeholders. We propose an architecture and implement a prototype using popular web application frameworks. The evaluation in a user study revealed promising results with respect to increased efficiency and decreased resource requirements for establishing situational awareness.

PENYADAPAN DALAM TINDAK PIDANA TERORISME DI INDONESIA DAN FILIPINA DITINJAU DARI PERSPEKTIF HAK ASASI MANUSIA

Terrorism in Indonesia and the Philippines is growing rapidly. Wiretapping is used as a method of investigating terrorists. The purpose of this study is to find out and examine whether wiretapping of terrorism suspects according to the terrorism law is with human rights and the reasons why wiretapping terrorism in Indonesia takes a longer period of time than in the Philippines. Normative research method with statutory approach and comparative law. Sources of data in the form of secondary data consisting of primary legal materials and secondary legal materials. Wiretapping has not been specifically regulated in the law, while in practice it has caused controversy over the wiretapping procedure. Wiretapping is a problem because it violates the privacy rights of citizens. Wiretapping must be carried out in line with human rights principles including technical and licensing so that it can prove that wiretapping is certainly intended to provide protection. Wiretapping in Indonesia takes longer because terrorism networks in Indonesia are not centered on one area, so it takes a relatively long time to conduct wiretapping.

Cyber Strategy in Practice

National cyber security strategies had already undergone a period of evolution before Russia’s 2022 invasion of Ukraine, ostensibly in preparation for just such a catalyser of major cyber conflagration. But the cyber war never came. Tom Johansmeyer, Gareth Mott and Jason R C Nurse analyse the national cyber security strategies (NCSS) of the US, Ukraine and Russia – as significant cyber domain stakeholders in the conflict – and how aligned these strategies were for the cyber component of the Ukrainian war. In addition, they consider areas of reflection for the future evolution of NCSS documents, particularly in light of the current state of play with cyber operations. ■

Cyber AI Research Trends

Today day to day there is an increase in cyber threat agents who are continuously coming with strategies which will help them evade the usual defences and end up obtaining or compromising vital information. AI use in monitoring tools by now can count among crucial techniques that help business entities to prevent these dangers. The propelled novel algorithms have replaced ‘human-in-the-loop’ method performing actions to assess security threats making the mechanisms much better. Such a piece dives into the area of AI robots that are utilized in threat intelligence, in order for machines to quickly discover all loop holes that attackers could use to break into a network. The application of Artificial Intelligence by businesses can in advance of the attacks, rather than as a response after the event, thwart them by the use of algorithms and predictive analytics to spot patterns of anomalies or suspicious activities. AI-powered models covering neural-network based threat identification to natural language processing belong to the spectrum of solutions implementing machine learning trained on datasets and cutting-edge algorithms. AI-based threat intelligence was presented to public as in line with practical coverage and real life issues and especially through examples. This technical issue might solve the inefficiency of identifying threats soon and thus increase their accuracy once again, in fact, it will use immediate reaction well too, like it used to. An exploration of computer ethics will touch on the topics of cyber security and data handling among others. As for their practical aspects, they have to be derived from moral righteousness founded on inner values. On the other hand, utilizing AI in threat intelligence processes calls for training of skilled workforce who will, in the long run, be able to face not only today’s cyber threats but also the future ones. In the past ten years reacting was the major issue in analysing cyber intelligence. Through the use of machine learning, enterprises across cyberspace can foresee probabilities of the most burdensome situations for them. Thus, making friendly organizations is the work of great threat intelligence in the 21st century

Cyber security analysis of connected vehicles

AbstractThe sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

Do We Know Whether to Laugh or Cry? User Responses to @Ukraine’s Dark-humour Meme

This study investigates international users’ reception of a dark-humour meme tweeted by Ukraine’s governmental Twitter (X) account on 7 December 2021 as part of its exceptional cultural practice of posting humorous memes. Tweeters’ responses to the ‘headaches meme’ are examined through a discourse-analytic lens. An emphasis is placed on the appreciation of the humour (emanating from the meme or its contextual embedding) and other forms of humour support demonstrated by its online indicators. The overarching aim is to distil and compare user reactions to the meme in two sociopolitical contexts—before and after the invasion of Ukraine that Russia mounted on 24 February 2022—based on two equal quote tweet samples. While the findings reveal a relative decrease in humour appreciation after the invasion, the same diversified reactions encompassing humour support or its lack and politically polarised discourses making for participatory digital warfare are detected in both parts of the dataset.