Predicting individual differences to cyber attacks: Knowledge, arousal, emotional and trust responses

Cyber attacks are increasingly commonplace and cause significant disruption, and therefore, have been a focus of much research. The objective of this research was to understand the factors that might lead users to fail to recognize red flags and succumb to cyber events. We investigated users’ knowledge of cyber attacks, their propensity to trust technology, arousal, emotional valence, and situational trust in response to different types and severity of cyber attacks. Our findings suggest that high-risk attacks elicited more arousal and more negative emotional valence than low-risk attacks. The attack-type manipulation revealed that phishing scenarios yielded distinctive patterns, including weaker affective responses than ransomware and other malware. The authors further examined arousal, emotional valence, and situational trust patterns among the subset of high- knowledge participants who successfully identified all the attacks and compared these responses with those of less knowledgeable peers. Our findings suggest that the more knowledgeable the user, the higher was their general propensity to trust technology, the more sensitive were their emotional responses to the manipulation of risk, and the lower their situational trust when faced with cyber attack scenarios.

A Model of Cloud Forensic Application With Assurance of Cloud Log

The key concepts of digital forensic investigation in cloud computing are examination and investigation. Cybercriminals target cloud-based web applications due to presence of vulnerabilities. Forensic investigation is a complex process, where a set of activities are involved. The cloud log history plays an important role in the investigation and evidence collection. The existing model in cloud log information requires more security. The proposed model used for forensic application with the assurance of cloud log that helps the digital and cloud forensic investigators for collecting forensic scientific evidences. The cloud preservation and cloud log data encryption method is implemented in java. The real-time dataset, network dataset results tell that attacks with the highest attack type are generic type, and a case conducted chat log will predict the attacks in advance by keywork antology learning process, NLP, and AI techniques.

Use of MARKOV Chain for Early Detecting DDoS Attacks

DDoS has a variety of types of mixed attacks. Botnet attackers can chain different types of DDoS attacks to confuse cybersecurity defenders. In this article, the attack type can be represented as the state of the model. Considering the attack type, we use this model to calculate the final attack probability. The final attack probability is then converted into one prediction vector, and the incoming attacks can be detected early before IDS issues an alert. The experiment results have shown that the prediction model that can make multi-vector DDoS detection and analysis easier.

Mobile Ad Hoc Networks Intrusion Detection in Co-Operative Motion

As the number of wireless devices continues to increase rapidly, mobile ad hoc networking (MANET) has emerged as an exciting and significant technological advance. MANETs were susceptible to attacks because of their open media, continuously changing network design, cooperation mechanisms, lack of a protective measure and management point, and a coherent layer of attack. However, regular functioning frequently generates traffic corresponding to a "signature attack," which leads to false alerts. One of the significant disadvantages is the inability to identify new attacks without established signatures. In this article, we describe our efforts towards creating the capability for MANET intrusion detection (ID). Based on our previous works on outlier detection, we explore how Intrusion Detection in Partial Swarm Optimization (IDPSO) and Support vector Regression(SVR) may improve an anomaly detection method to give additional information about attack kinds and origins. We can use a basic formula to determine the attack type for many well-known assaults whenever an anomaly is detected.

The Agent Web Model: modeling web hacking for reinforcement learning

Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable. The tools used by attackers are becoming more and more automated and sophisticated, and malicious machine learning agents seem to be the next development in this line. In order to provide ethical hackers with similar tools, and to understand the impact and the limitations of artificial agents, we present in this paper a model that formalizes web hacking tasks for reinforcement learning agents. Our model, named Agent Web Model, considers web hacking as a capture-the-flag style challenge, and it defines reinforcement learning problems at seven different levels of abstraction. We discuss the complexity of these problems in terms of actions and states an agent has to deal with, and we show that such a model allows to represent most of the relevant web vulnerabilities. Aware that the driver of advances in reinforcement learning is the availability of standardized challenges, we provide an implementation for the first three abstraction layers, in the hope that the community would consider these challenges in order to develop intelligent web hacking agents.

Impactul managementului disfagiei asupra calității vieții persoanelor cu accident vascular cerebral

The objective of the following research is the investigation of the impact that management of dysphagia has upon quality of life in persons with neurological pathology of vascular cerebreal attack type. The group of participants in this study has 6 members with ages between 34 and 69, all suffering from a neurological pathology of VCA type or cranio-cerebreal trauma with direct implications on the swallowing process. This paper used case study methode as experimental design, the goal being to detect the impact that speach therapy has on the dysphagia, in the context of safety and quality of life. A series of instruments regarding clinical evaluation of dysphagia and quality of life in the context of VCA and dysphagia where been translated and adapted in the fallowing research.

Sensitivity analysis of the primary endpoint from the N-MOmentum study of inebilizumab in NMOSD

Background: In the N-MOmentum trial, the risk of an adjudicated neuromyelitis optica spectrum disorder (NMOSD) attack was significantly reduced with inebilizumab compared with placebo. Objective: To demonstrate the robustness of this finding, using pre-specified sensitivity and subgroup analyses. Methods: N-MOmentum is a prospective, randomized, placebo-controlled, double-masked trial of inebilizumab, an anti-CD19 monoclonal B-cell-depleting antibody, in patients with NMOSD. Pre-planned and post hoc analyses were performed to evaluate the primary endpoint across a range of attack definitions and demographic groups, as well as key secondary endpoints. Results: In the N-MOmentum trial (ClinicalTrials.gov: NCT02200770), 174 participants received inebilizumab and 56 received placebo. Attack risk for inebilizumab versus placebo was consistently and significantly reduced, regardless of attack definition, type of attack, baseline disability, ethnicity, treatment history, or disease course (all with hazard ratios < 0.4 favoring inebilizumab, p < 0.05). Analyses of secondary endpoints showed similar trends. Conclusion: N-MOmentum demonstrated that inebilizumab provides a robust reduction in the risk of NMOSD attacks regardless of attack evaluation method, attack type, patient demographics, or previous therapy. The N-MOmentum study is registered at ClinicalTrials.gov: NCT2200770.

A three-tiered intrusion detection system for industrial control systems

This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F-measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.

Development of Specialized Method for Increasing the Level of Security on Information and Communication Systems

This article proposes a method for increasing the level of security of a corporate network by its structure that meets the specified security requirements and distinguishes them from well-known attack type invariant structural units of the network - security domains. Hierarchy of security levels in the form of rings is represented allow more effectively protect a network that requires more security. The method allows to get output data only when the initial data allows to do this. In other words, if it is possible to improve the security level of a given network structure, then the method does this. A graph of the dependence of the security level on the ratio of the number of domains to the maximum number of objects in the domains is developed. A weighted domain allocation algorithm, which will increase the overall security on information and communication systems is proposed. For increasing the overall level of security, the splitting of the network into a larger number of security domains with as few services as possible is given. In accordance the weight of properties with modern data from the theory of information security vulnerability of a particular service is selected.