Potential Attacks Research Articles

Mechanism of Strategic Decision Making in Emergency Situations of Terrorist Attacks in Indonesia

Terrorism is a global phenomenon experienced by all countries including Indonesia, manifested in terrorist attacks that involve violence as a main element with the motivation to achieve political, ideological, and certain belief goals. The threat of potential massive terrorist attacks continues to evolve to this day. Each era shows a tendency in terms of ideology and also types of terrorist attacks. In different times, it is not impossible to have the potential for attacks in different forms. Terrorist attacks that occur in a country can pose a threat to the stability of all aspects of life, thus requiring competent handling by looking at how the mechanism of strategic decision making in Indonesia faces emergency situations of terrorist attacks. Reflecting on the experience of handling the Bali Bombing I, this research was conducted with a qualitative approach based on literature studies and interviews with sources consisting of academics, practitioners, and strategic observers in the issue of terrorism using rational choice theory and criminal policy as the basis for writing. Through the study conducted, the authors found that although Indonesia has experience in handling terrorist actions, the readiness from the capacity aspect needs to be matured, especially in dealing with terrorist attacks. Therefore, a strategic and uncomplicated decision-making mechanism needs to be formulated immediately through a clear command flow or delegation of authority that applies when emergency conditions of terrorist attacks occur.

A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures

The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that attackers can exploit with unpredictable consequences. Against this background, this paper provides a comprehensive review on security of UAV swarm networks. We begin by briefly introducing the dominant UAV swarm technologies, followed by their civilian and military applications. We then present and categorize various potential attacks that UAV swarm networks may encounter, such as denial-of-service attacks, man-in-the-middle attacks and attacks against Machine Learning (ML) models. After that, we introduce security technologies that can be utilized to address these attacks, including cryptography, physical layer security techniques, blockchain, ML, and intrusion detection. Additionally, we investigate and summarize mitigation strategies addressing different security threats in UAV swarm networks. Finally, some research directions and challenges are discussed.

An efficient certificateless anonymous signcryption communication scheme for vehicular adhoc network

Certificateless public key infrastructure (PKI) avoids the key escrow problem associated with identity-based PKI and has recently been widely employed in anonymous communication schemes for vehicular adhoc networks (VANETs). In existing certificateless anonymous signcryption schemes for VANETs, vulnerabilities such as potential attacks involving the substitution of pseudonyms and the forging of pseudonymous public-private key pairs exist due to the lack of “identity-key binding” and “non-linearity processing of public-private key pairs.” To address this issue, we propose an improved certificateless anonymous signcryption scheme based on elliptic curve cryptography. The scheme incorporates bilinear pairing as one of the authentication mechanisms, designs pseudonym generation algorithms and public-private key pair structures, and introduces a pseudonym verification mechanism. The correctness of the scheme is proven under the random oracle model, and its security is extensively demonstrated through detailed discussions on its confidentiality, authentication, unforgeability, anonymity, and traceability. Furthermore, the time and space complexity of the scheme are calculated. By comparing with recently published certificateless signcryption schemes, it is shown that the proposed scheme offers higher security with smaller computational and communication overheads. This certificateless vehicular network signcryption algorithm provides an efficient encryption solution for anonymous communication in vehicular networks, thereby ensuring the rapid development of secure technology for intelligent connected vehicle super terminals.

Exploring the ALNS method for improved cybersecurity: A deep learning approach for attack detection in IoT and IIoT environments

With the emergence of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the flow of data across the world is experiencing a rapid expansion. Unfortunately, this exponential growth is accompanied by a proportional increase in cyber threats, jeopardizing the security and integrity of computer systems. In this context, intrusion detection becomes a necessity to protect networks and systems against potential attacks, ensuring their proper functioning and reliability. In this paper, we propose a deep learning-based model for attack detection. This model utilizes a convolutional neural network to train the datasets, which are first cleaned and preprocessed. The model inputs are selected using an optimization method called adaptive large neighborhood search. The results obtained for the four datasets used - CICIDS2017, Edge-IIoTset, ToN-IoT windows7, and ToN-IoT windows10 - demonstrate the model’s effectiveness for both multi-class and binary classification cases. In the binary case, the accuracy reaches 99.85%, 100%, 99.97%, and 100%, respectively, and in the multi-class case, it stands at 99.81%, 94.98%, 99.92%, and 99.84%, respectively.

FedGen: Personalized federated learning with data generation for enhanced model customization and class imbalance

Federated learning has emerged as a prominent solution for the collaborative training of machine learning models without exchanging local data. However, existing approaches often impose rigid constraints on model heterogeneity, limiting the ability of clients to customize unique models and increasing the vulnerability of models to potential attacks. This paper presents FedGen, a novel personalized federated learning framework based on generative adversarial networks (GANs). FedGen shifts the focus from training task-specific models to generating data, especially for minority classes with imbalanced data. With FedGen, clients can gain knowledge from others by training generators, while maintaining a heterogeneous local model and avoiding sharing model information with other participants. Moreover, to address challenges arising from imbalanced data, we propose AT-GAN, a novel generative model incorporating pseudo augmentation and differentiable augmentation modules to foster healthy competition between the generator and discriminator. To evaluate the effectiveness of our approach, we conduct extensive experiments on real-world tabular datasets. The experimental results demonstrate that FedGen significantly enhances the performance of local models, achieving improvements of up to 11.92% in F1 score and up to 9.14% in MCC score compared to existing methods.

Integrated physical safety–cyber security risk assessment based on layers of protection analysis

The extensive application of information technology in process industries has increased production efficiency but has also introduced new risks. Therefore, it is necessary to systematically analyse the risks within factories to ensure the stable operation of their production systems. This study proposes an integrated risk assessment method based on layers of protection analysis (LOPA), which combines physical safety and cyber security analyses to provide comprehensive risk assessments for the process industry. The method first identifies the hazardous scenarios and protection layers relevant to a process facility. It then identifies potential cyberattack types and existing countermeasures. Subsequently, the functional impacts of attacks on protection layers and potential coupling relationships are discussed. Using common vulnerability scoring system (CVSS) and semi-quantitative methods, the probability of attack is determined to optimize the probability of failure on demand (PFD) of the protection layers. Finally, a case study of a steam separator in a catalytic cracking unit is used to quantitatively explore the potential attacks and risks of coupled protection layers. The application of Bayesian network (BN) is used for further validation of the method. This study offers a novel quantitative tool for risk assessment in the process industry, which can enhance the security and reliability of industrial production and control systems.

[formula omitted]: A blockchain-based secure access control management for the Internet of Things

The Internet of Things (IoT) paradigm has widespread applications across many fields in which private and sensitive user or environmental data are sensed and shared. Most present-day IoT applications depend on centralized cloud servers for authentication and access control. Validating the identity of a user and determining the legitimacy of his/her access requests require multiple rounds of data communications over the untrusted Internet, exposing sensitive data to potential attacks. Thus, protecting these data from security and privacy attacks and ensuring legitimate access is imperative. To address this challenge, we adopt an emerging technology called blockchain to propose a decentralized security framework called BloAC. It ensures secure access control in IoT networks without the intervention of the back-end cloud. We have used the Hyperledger Fabric, an open-source, permissioned blockchain platform, for implementing a prototype system using customized attribute-based access control (ABAC) policies. We have performed simulated and real test bed-based experiments to illustrate that BloAC outperforms the cloud–server-based access control in latency and scalability, significantly reducing latency by up to 42.45% compared to cloud-based solutions. Finally, we conduct a security analysis to formally verify the ABAC policies used in BloAC and establish its robustness against attacks theoretically and using the AVISPA tool.

Exploring the Security Challenges of an E-Voting System (EVS): A Case Study in the Bolgatanga Senior High School

The integration of electronic voting systems (EVS) into electoral processes promises increased efficiency, speed, and transparency. However, this technology also introduces a variety of security challenges that must be addressed to ensure the integrity of the voting process. This study explores the security vulnerabilities of an EVS, focusing on its implementation at Bolgatanga Senior High School (BIGBOSS). Through a case study approach, this research examines the potential threats to the administration of EVS, including issues related to data privacy, system integrity, authentication protocols, and susceptibility to cyber-attacks. A mixed-methods strategy was employed, utilizing surveys, interviews, and system tests to assess both technical vulnerabilities and user experiences. Key findings revealed Potential Voter Manipulation where unauthorized access could allow individuals to cast votes fraudulently; Insider Threats where trusted users exploiting their access could manipulate election outcomes; Weak Authentication; Lack of Robust Verification; Potential Physical Attack where individuals could vandalize machines and Vulnerabilities in Encryption which may expose votes, compromising ballot secrecy. The study recommends the implementation a blockchain-based technology , such as cryptographic techniques, consensus mechanisms, use of biometric verification to ensure the identity of voters as well as the use of digital signatures to verify the authenticity of electronic ballots and ensure that they haven't been altered during transmission, implementation of real-time monitoring of the e-voting system to detect and respond to security threats promptly” and also provide ongoing cybersecurity education and training to election officials, staff, and voters to raise awareness and reduce the risk of human error. These findings contribute to the ongoing discourse on EVS security, providing insights into practical measures for improving the reliability and safety of electronic voting systems in educational institutions and beyond.

Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments.

Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems

There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

On adaptive resilient secondary control for DC microgrids under false data injection attacks

AbstractDistributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method.

Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security

This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks.

Zero Trust Security: Is it Optional?

Zero Trust Architecture (ZTA) is a cybersecurity model that authenticates and authorizes every interaction between a user or device and a network. It's based on the idea that "trust is good, but control is better", and assumes that all networks and traffic could be potential threats. ZTA goes beyond the traditional "trust but verify" approach, by treating every access request as potentially dangerous and requiring a thorough check before granting access. This is regardless of the requester's identity or location. Zero Trust is a security model that assumes nothing should be trusted automatically, even within a network. It requires all users, regardless of location, to be verified and authorized before accessing resources. This is achieved through strict security measures like multi-factor authentication, advanced endpoint protection, and robust identity management. Today, people expect to access applications and data anytime, anywhere. With the rise of cloud computing and IoT, the number of connected devices and potential attack points is growing. To protect data and networks, we need a new approach. This article explains what Zero Trust is and some of its key principles.

High Capacity and Reversible Fragile Watermarking Method for Medical Image Authentication and Patient Data Hiding.

The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration.

Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based Density-Based Spatial Clustering of Applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

BYOD Security Policy Model: A Systematic Literature Review

In education sector, Bring Your Own Device (BYOD) Security Policy (BYOD-SP) was established as earlier in 2015 in foreign universities with the purpose to safeguard campus’s network, system and confidential data and information. BYOD-SP is one of the strategic planning launched, aims to achieve maximum security for personally owned device through BYOD phenomenon. BYOD-SP acts as an official documentation that describes the detailed explanation of each component for the BYOD users to abide to all the components’ requirement in BYOD-SP. In BYOD practice in education field, BYOD-SP must meet the education agenda where BYOD-SP need to be tailored following education strategic plans and eliminate potential attacks that might cause the universities in loss if BYOD-SP is launched without having a robust model to refer of its components. Hence, it is crucial as this will welcome unauthorized parties to get access, gain and steal campus corporate data in BYOD environment. This study aims to identify what the necessary components that could assist top management and policy makers to formulate the comprehensive BYOD-SP in future and BYOD can be one of mobile computing that being accepted to be practiced in secured environment. Existing studies were executed in many research designs, where the review fulfilled the Preferred Reporting Items for Systematic reviews and MetaAnalyses (PRISMA) publishing standard. Two common online databases, World of Science (WOS) and Scopus were utilized to observe articles for this study purposes where a total of 16 articles published from 2013-2022 were reviewed to answer the formulated research questions on BYOD-SP. This review includes a topic based on the thematic analysis which is the components in drafting BYOD-SP documentation. The findings from this study proved four main components to be emphasized in tailoring BYOD-SP which are Device, Process, People and Jurisdiction/Law. The research’s findings here may drive the organization to have a reference model of BYOD-SP components and come out with the sturdy BYOD-SP to be adhered by all individuals in education premises. The top management in the organization need to involve in the procedures in each component of BYOD-SP to alleviate the case of security breach in BYOD activities.

Hardware Security Breaches: Alarming Exploits Beyond Tampering

Side Channel Attacks (SCAs) and Fault Injection Attacks have emerged as significant threats to the security of electronic devices. This paper explores these hardware attack vectors in the context of recent incidents [11] involving pagers and walkie-talkies. We shall deep dive into the possible mechanisms of these attacks, how they can be combined for increased efficacy, and their implications for device security. This paper discusses only the possible ways [1] the adversaries could have exploited and may include other attack vectors such as supply chain attack and few others. The aim of this paper as we discuss the potential attack mechanisms is only to focus on mitigations and awareness of the reader to perform advanced security testing.

Data reorganization for image encryption: presentation in the data of hare

ABSTRACT In track and field, a hare (or pacemaker) is a runner who leads the early part of a race to help other competitors achieve better performance and potentially set records. This practice became common in the early 1980s. In cryptography, organizing data before encryption serves as a strategic technique to strengthen defenses against cryptanalytic attacks. Attackers often exploit patterns in encrypted data to narrow down key possibilities. By deliberately disrupting data structure prior to encryption, we can complicate these efforts. This concept, termed “Presentation in the Data of Hare,” proposes various data organization methods (e.g., line by line, column by column) to enhance encryption. This approach can add 21 bits to the encryption key, significantly increasing the complexity and time required to crack it—equivalent to a 2 million-fold increase. This work focuses on improving security in cryptographic systems by employing innovative techniques, including those related to post-quantum cryptography. By strategically organizing data before encryption, we aim to hinder potential cryptanalytic attacks, making it considerably harder for attackers to exploit any regularities, thus bolstering resilience against both classical and quantum threats.

Novel Alkynylamide-Based Nonpeptidic Allosteric Inhibitors for SARS-CoV-2 3-Chymotrypsin-like Protease.

Although the coronavirus disease 2019 (COVID-19) crisis has passed, there remains a necessity for continuous efforts toward developing more targeted drugs and preparing for potential future virus attacks. Currently, most of the drugs received authorization for the treatment of COVID-19 have exhibited several limitations, such as poor metabolic stability, formidable preparation, and uncertain effectiveness. It is still significant to develop novel, structurally diverse small-molecule antiviral drugs targeting SARS-CoV-2 3-chymotrypsin-like protease (3CLpro). Herein, we report a class of alkynylamide-based nonpeptidic 3CLpro inhibitors that can be prepared conveniently by our previously developed one-pot synthetic method. The structure-activity relationships of alkynylamides as SARS-CoV-2 3CLpro inhibitors have been carefully investigated and discussed in this study. The two stereoisomers of the resulting molecules exhibit stereoselective interaction with 3CLpro, and the optimized compound (S,R)-4y inhibits 3CLpro with high potency (IC50 = 0.43 μM), low cytotoxicity, and acceptable cell permeability. Compound (S,R)-4y presents as a noncovalent inhibitor of 3CLpro against SARS-CoV-2 by the time-dependent inhibition assay (TDI) and mass spectrometry analysis. The Lineweaver-Burk plots, binding energy, surface plasmon resonance, and molecular docking studies suggest that (S,R)-4y specifically binds to an allosteric pocket of the SARS-CoV-2 3CLpro. These findings provide a novel class of nonpeptidic alkynylamide-based allosteric inhibitors with high selectivity against SARS-CoV-2 3CLpro featured by a simplified one-pot synthesis at room temperature in air.