Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

Recurrent Transient Ischemic Attacks Revealing Cerebral Amyloid Angiopathy: A Comprehensive Case

This case report investigates the manifestation of cerebral amyloid angiopathy (CAA) through recurrent Transient Ischemic Attacks (TIAs) in an 82-year-old patient. Despite initial diagnostic complexities, cerebral angiography-MRI revealed features indicative of CAA. Symptomatic treatment resulted in improvement, but the patient later developed a fatal hematoma. The discussion navigates the intricate therapeutic landscape of repetitive TIAs in the elderly with cardiovascular risk factors, emphasizing the pivotal role of cerebral MRI and meticulous bleeding risk management. The conclusion stresses the importance of incorporating SWI sequences, specifically when suspecting a cardioembolic TIA, as a diagnostic measure to explore and exclude CAA in the differential diagnosis. This case report provides valuable insights into these challenges, highlighting the need to consider CAA in relevant cases.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

A Trajectory Privacy Protection Method to Resist Long-Term Observation Attacks

Users face the threat of trajectory privacy leakage when using location-based service applications, especially when their behavior is collected and stored for a long period of time. This accumulated information is exploited by opponents, greatly increasing the risk of trajectory privacy leakage. This attack method is called a long-term observation attack. On the premise of ensuring lower time overhead and higher cache contribution rate, the existing methods cannot utilize cache to answer subsequent queries while also resisting long-term observation attacks. So this article proposes a trajectory privacy protection method to resist long-term observation attacks. This method combines caching technology and improves the existing differential privacy mechanism, while incorporating randomization factors that are difficult for attackers to recognize after long-term observation to enhance privacy. Search for locations in the cache of both the mobile client and edge server that can replace the user’s actual location. If there are replacement users in the cache, the query results can be obtained more quickly. Simultaneously obfuscating the spatiotemporal correlation of actual trajectories by generating confusion regions. If it does not exist, the obfuscated location generation method that resists long-term observation attacks is executed to generate the real anonymous area and send it to the service provider. The above steps can comprehensively protect the user’s trajectory privacy. The experimental results show that this method can protect user trajectories from long-term observation attacks while ensuring low time overhead and a high cache contribution rate.

GUARDIAN: A Multi-Tiered Defense Architecture for Thwarting Prompt Injection Attacks on LLMs

This paper introduces a novel multi-tiered defense architecture to protect language models from adversarial prompt attacks. We construct adversarial prompts using strategies like role emulation and manipulative assistance to simulate real threats. We introduce a comprehensive, multi-tiered defense framework named GUARDIAN (Guardrails for Upholding Ethics in Language Models) comprising a system prompt filter, pre-processing filter leveraging a toxic classifier and ethical prompt generator, and pre-display filter using the model itself for output screening. Extensive testing on Meta’s Llama-2 model demonstrates the capability to block 100% of attack prompts. The approach also auto-suggests safer prompt alternatives, thereby bolstering language model security. Quantitatively evaluated defense layers and an ethical substitution mechanism represent key innovations to counter sophisticated attacks. The integrated methodology not only fortifies smaller LLMs against emerging cyber threats but also guides the broader application of LLMs in a secure and ethical manner.

Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer.First,a topologyrepairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources considerably.The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated.Second,a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed.Moreover,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given explicitly.The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.

The role of cochlear implantation in alleviating Tumarkin drop attacks of Meniere's disease;a case report

Ménière’s disease(MD)patients may suffer episodes of sudden falls,named Tu markin drop attacks(DAs).This fall occurs abruptly and without warning or loss of consciousness.DAs usually aggravate the clinical picture of MD and are challenging to manage.The present report describes a case treated by cochlear implantation(CI)due to concomitant deafness and offers some clinical considerations for this condition.A male patient aged 48 years with a 10-year history of definite bilateral MD had profound SNHL on the right and severe SNHL on the left side.He suffered from intermittent attacks of vertigo,ear fullness,and tinnitus and,in the last year,had developed DAs and experienced 14 episodes in the previous six months.The preoperative category of acoustic performance was 3.The Dizziness Handicap Inventory(DHI)questionnaire showed a total score of 46,which indicated a moderate degree of disability.A CI was planned for the right side.The patient did not report any further DAs episode for two years since then.The postoperative category of acoustic performance became 11,and the postoperative DHI questionnaire showed a decrease in the total score(from 46 to 19),which indicated a mild disability.Unilateral CI effectively alleviated the DAs associated with bilateral MD.Our report proposes a new modality for managing vertiginous symptoms in cases of MD with hearing loss without the need for more aggressive surgical interventions with the need for clinical trials to confirm our results.

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyberphysical systems subject to process disturbances and measurement noise.A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model.The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values.In order to help detect such attacks,a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change,due to the attack,at a time prior to the attack occurrence time.A key characteristic of this residual is that its magnitude increases every time a new attack occurs.To estimate this unknown residual,an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices.The smoother design guarantees robustness with respect to process disturbances and measurement noise,and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices.The adaptive threshold is designed based on the estimated backward-in-time residual,and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology.Finally,a simulation example is used to demonstrate the effectiveness of the developed methodology.

Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task nowadays.For this purpose,authen-tication protocol plays a vital role in web communication which securely transfers data from one party to another.This authentication works via OpenID,Kerberos,password authentication protocols,etc.However,there are still some limitations present in the reported security protocols.In this paper,the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method.The proposed OpenID-based encrypted Email’s Authentication,Authorization,and Accounting(EAAA)protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String(SSEAS).This string is deployed on both the relying party and the email server,which is unique and trustworthy.The first authentication,OpenID Uniform Resource Locator(URL)identity,is performed on the identity provider side.A second authentication is carried out by the hidden Email’s server side and receives a third authentication link.This Email’s third SSEAS authentication link manages on the relying party(RP).Compared to existing cryptographic single sign-on protocols,the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing attacks.This study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods,such as Single Sign-On(SSO)and OpenID.The experimental sites were examined by 72 information technology(IT)specialists,who found that 88.89%of respondents successfully validated the user authorization provided to them via Email.The proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.

Collaborative Detection and Prevention of Sybil Attacks against RPL-Based Internet of Things

The Internet of Things(IoT)comprises numerous resource-constrained devices that generate large volumes of data.The inherent vulnerabilities in IoT infrastructure,such as easily spoofed IP and MAC addresses,pose significant security challenges.Traditional routing protocols designed for wired or wireless networks may not be suitable for IoT networks due to their limitations.Therefore,the Routing Protocol for Low-Power and Lossy Networks(RPL)is widely used in IoT systems.However,the built-in security mechanism of RPL is inadequate in defending against sophisticated routing attacks,including Sybil attacks.To address these issues,this paper proposes a centralized and collaborative approach for securing RPL-based IoT against Sybil attacks.The proposed approach consists of detection and prevention algorithms based on the Random Password Generation and comparison methodology(RPG).The detection algorithm verifies the passwords of communicating nodes before comparing their keys and constant IDs,while the prevention algorithm utilizes a delivery delay ratio to restrict the participation of sensor nodes in communication.Through simulations,it is demonstrated that the proposed approach achieves better results compared to distributed defense mechanisms in terms of throughput,average delivery delay and detection rate.Moreover,the proposed countermeasure effectively mitigates brute-force and side-channel attacks in addition to Sybil attacks.The findings suggest that implementing the RPG-based detection and prevention algorithms can provide robust security for RPL-based IoT networks.

Certrust:An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate flows.Traditional methods to identify the malicious flows in Crossfire attacks are rerouting,which is based on statistics.In these existing mechanisms,the identification of malicious flows depends on the IP address.However,the IP address is easy to be changed by attacks.Comparedwith the IP address,the certificate ismore challenging to be tampered with or forged.Moreover,the traffic trend in the network is towards encryption.The certificates are popularly utilized by IoT devices for authentication in encryption protocols.DTLShps proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN controller.Based on DTLShps,the SDN controller can collect statistics on certificates.In this paper,we proposeCertrust,a framework based on the trust of certificates,tomitigate the Crossfire attack by using SDN for IoT.Our goal is threefold.First,the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire attack.Moreover,the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay rate.Second,for detecting the Crossfire attack accurately,a method based on graph connectivity is proposed.Third,several trust-based routing principles are proposed tomitigate the Crossfire attack.These principles can also encourage users to use certificates in communication.The performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting schemes.Moreover,our trust model has a more appropriate decay rate than the traditional methods.

A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection attacks.Most DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing resources.This paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to victims.It defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection attacks.BLCD also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS packets.For evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence locations.Experiments show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.