Risk Of Intrusion Research Articles

Design of intrusion detection system for intelligent mobile network teaching

Because of the openness and complexity of the mobile network environment, the problem of network security is becoming more and more serious, and the mobile network teaching system is also facing the risk of intrusion and attack. Therefore, this paper designs an intrusion detection system for mobile network teaching system. The system uses machine learning and data mining technology to analyze and process the data flow of the mobile network teaching system, extract the characteristics of the data flow, build the intrusion detection model, and realize the intrusion detection of the mobile network teaching system. The research results of this paper show that the intrusion detection system of mobile network teaching system based on intelligent algorithm can effectively detect the intrusion behavior of mobile network teaching system, with high detection accuracy and low false alarm rate, which provides a guarantee for the security and stability of mobile network teaching system.

Application cost of intelligent intrusion detection in medical logistics management under public cloud environment

Because the medical logistics management involves a large number of sensitive data and information, the security problem has become the key problem to be solved. This paper explores the use of Intelligent Intrusion Detection Technology in the public cloud to improve the security of medical logistics management applications, and evaluates its impact on costs. The research adopts computer steps, including collecting data and information of medical logistics management, constructing intrusion detection system model, designing experimental scheme, conducting simulation experiments and data analysis. The results show that the application of Intelligent Intrusion Detection Technology in the public cloud can significantly improve the security of medical logistics management and reduce the potential intrusion risk. The comprehensive analysis shows that the cost of intelligent intrusion detection technology is relatively low, which is reasonable compared with the improvement of security.

IoT empowered smart cybersecurity framework for intrusion detection in internet of drones

The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD, specifically while applying B-LSTM and LSTM. The system attains precision values of 89.10% and 90.16%, accuracy rates up to 91.00–91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.

The passive sampler assisted human exposure risk characterization for tetrachloroethene soil vapor intrusion scenario

The potential human health risks associated with soil vapor intrusion and volatile organic compounds (VOCs) exposure were characterized at an industrialized site by the quantification of gaseous VOCs in soil pores using a passive sampling technique. The gaseous tetrachloroethene (PCE) in soil pores varied between 12 and 5,400 μg m−3 showing 3 orders of magnitude variation with dependence on groundwater PCE concentrations. Though the PCE concentration in the air only varied between 0.45 and 1.5 μg m−3 showing negligible variations compared to the variation observed in soil pores. The PCE concentration in the air varied between 0.45 and 1.5 μg m−3. The calculation of fugacity suggested that the PCE in the test site originated from groundwater. Measured PCE in groundwater ranged from 14 to 2,400 times higher than PCE in soil gas. This indicates that conducting a vapor intrusion risk assessment using passive soil gas sampling is critical for accurate risk characterization and assessment. Estimated PCE inhalation cancer risks for street cleaners and indoor residents varied between 10−6 and 10−4 with a low plausible hazard, and between 10−3 and 10−2 with a high risk, respectively. The results of this study demonstrate that passive sampling offers a significantly lower cost and labor-intensive approach compared to traditional methods for assessing pollution distribution in contaminated sites and characterizing risks. This highlights the potential for wider application of passive sampling techniques in environmental studies.

Impact of connected conduit on pumping-induced seawater intrusion in a coastal karst aquifer

IntroductionCoastal karst aquifers face the risk of seawater intrusion due to groundwater development. Based on the conceptualization of Woodville Karst Plain (WKP), this study investigates the effect of karst conduit and pumping conditions on the aquifer vulnerability and pumping security.MethodsAccording to the purposes of this study, two cases are considered: one with conduit and one without. Salinization levels are compared between two cases, considering pumping rates ranging from 50 to 200 m3/day and various pumping locations throughout the on-shore region.ResultsThe results reveal that the aquifer with conduit is more susceptible to seawater intrusion at low pumping rates, whereas both scenarios experience significant salinization at high pumping rates. Specifically, in the non-conduit case, contamination is most prevalent when wells are located in the middle of the aquifer, while in the conduit case, pumping from inland areas poses high vulnerability. Moreover, conduit case and non-conduit case display different regions for pumping clean water. At low pumping rates, both cases show saline water being pumped from wells near the shore, and clean water is obtained from inland wells. At high pumping rates, the non-conduit case allows for clean water extraction from wells situated further inland, while in the conduit case, no wells within the entire aquifer are deemed safe.DiscussionThe analysis and findings of this study offer valuable insights for the management of groundwater in coastal karst aquifers, encompassing vulnerability assessment, selection of pumping locations, and determination of pumping rates.

야간조명의 조도가 야간침입절도에 미치는 영향

As cities grow and develop, lighting is no longer limited to merely improving visibility in the dark and providing an environment for nighttime activities. It also plays a crucial role in preventing crimes that occur at night, ensuring safety, and reducing fear. Despite various studies conducted to clarify the effect of lighting on crime, little research has been devoted to investigating the impact of nighttime illumination on criminal activities, particularly in conjunction with open-street CCTVs. This study aims to address the limitations of these previous studies.
 To achieve this goal, the study selected Songpa-gu, Seoul, as the research area, collected illuminance data, open-street CCTV data, and data on nighttime trespassing theft, and analyzed them using GIS and statistical methods. The study's findings revealed that the intensity of nighttime lighting, the number of open-street CCTVs, and the interaction between nighttime illumination intensity and the number of open-street CCTVs all had a statistically significant positive effect on nighttime trespassing theft.
 These results appear somewhat contradictory to previous studies, and two explanations are possible. First, contrary to expectations, nighttime illumination intensity and open-street CCTVs might not assist in reducing nighttime trespassing theft; instead, they could act as factors that increase it. Second, the observed outcome might be due to autonomous districts increasing illumination and installing numerous open-street CCTVs primarily in areas with a high risk of nighttime intrusion and theft.
 This study concludes with policy proposals related to these findings, research limitations, and discussions on potential future research directions.

A Hydrogeological Conceptual Model Refines the Behavior of a Mediterranean Coastal Aquifer System: A Key to Sustainable Groundwater Management (Grombalia, NE Tunisia)

The Mediterranean coastal aquifer system of the Grombalia basin (NE Tunisia) offers immense potential as a source of fresh water for agriculture, industry, and drinking water supply. Nonetheless, due to its intricate hydrogeological characteristics and the prevailing issue of groundwater salinity, comprehending its groundwater system behavior becomes crucial for the effective and sustainable management of this aquifer system. Based on the hydrogeological characterization of the Grombalia basin, a novel 3D hydrogeological conceptual model was developed to enhance the understanding of its complex aquifer system. The integration of insights from geological, hydrogeological, hydrodynamic, and hydrochemical components facilitated the construction of the hydrogeological conceptual model. Although the model’s validity faced initial uncertainties due to spatial interpolation of lithological sequences, this study’s thorough and encompassing hydrogeological investigation overcame these limitations. As a result, a more informed comprehension of the aquifer system complexities was achieved. This study reveals that the basin is underlain by an extensive, cohesive Mio–Plio–Quaternary aquifer system. The model demonstrates vertical and lateral hydrogeological continuity between the Quaternary and underlying Mio–Pliocene deposits, enabling groundwater flow and exchange between these layers. Over-abstraction of the Mio–Plio–Quaternary aquifer system has led to a significant drop in piezometric levels and raised the risk of seawater intrusion. These findings emphasize the critical necessity of taking into account the interconnections among hydrogeological units to ensure sustainable groundwater management. The developed conceptual model offers a key tool for understanding the hydrodynamic functioning of the Grombalia aquifer system with a view toward guiding future groundwater management strategies. The application of this approach in the Grombalia basin suggests its potential applicability to other regional aquifers facing comparable challenges.

Two-phase changes in workability of sand-bentonite mixture triggered by seawater and binder during seepage cut-off wall construction in marine environment

Phenomenal sea level rise is exacerbating the risk of seawater intrusion in coastal areas, especially those with sandy soil, causing land erosion. Seepage cut-off walls made of in-situ sand, bentonite and binder can be employed. However, the presence of salts in soil is detrimental to water retention ability of bentonite, which can be aggravated by binder-induced cation exchange reactions, causing constructability issues for two-phase cut-off walls. Therefore, this study aims to evaluate the workability properties, including flowability and bleeding of sand-bentonite-binder mixture in seawater environment. The mechanism of decrease in water retention of bentonite has been investigated using Atterberg limits, X-ray diffration (XRD) and scanning electron microscope (SEM) analyses. In phase-one construction, seawater significantly increased flowability of sand-bentonite mixture. During the second phase, binder inclusion increased flowability even further. Given comparable flowability, the bleeding in sand-bentonite-binder mixture containing seawater was much greater than in the non-seawater environment. This was ascribed to a loss in water retention capacity of bentonite, in which external cations from seawater and binder exchanged with bentonite, resulting in reduction in liquid limit. The Na-based montmorillonite was transformed into Ca-based montmorillonite, causing the morphology of bentonite particles to flocculate. Despite this, the study discovered a feasible sand-bentonite-binder mixture including reactive magnesia (MgO)-activated ground granulated blast-furnace slag (GGBS) and seawater-resistant bentonite, which impeded the increase in workability.

Effects of Source Depletion on Vapor Intrusion Risk Assessment

The current regulatory site investigation employs the J&E model to predict vapor intrusion risk. However, the J&E model assumes that the source concentration is constant for a given exposure period, which is not consistent with the actual site source under depletion. In this study, we compared the differences between the J&E model (constant source), SD source depletion model, and RBCA source depletion model for predicting indoor concentration variation as well as the risk levels during the exposure period with a case study in Beijing. The results showed that the source and indoor air concentrations predicted by the SD and RBCA models showed exponential decreases, whereas those predicted by the J&E model maintained high concentrations throughout the exposure period, which greatly overestimated the risk. The RBCA predicted source depletion at the fastest rate, but the predicted indoor air concentrations were still lower than those of the SD model, which was related to the fact that the RBCA did not consider the effect of buildings on source depletion and did not follow mass conservation. Further, the sensitivity analysis showed that the pressure difference (dP) had the greatest influence on the source concentration in the SD model. For the calculated carcinogenic risk and hazard quotients, the J&E constant source model, the SD source depletion model, and the RBCA source depletion model were ranked in descending order. The results indicated that in general the J&E model was too conservative, the RBCA model may have underestimated risk, and the SD model was more suitable for quantifying vapor intrusion risk in reality.

The Proposed Intrusion Detection System using Support Vector Machine by IOT Enabled WSN

Abstract: Detecting intruders in computer networks is very important because it affects multiple communication and security domains finding network intruders can be difficult furthermore network intrusion detection remains a challenging undertaking due to the large amount of data required to train modern machine learning models to detect network intrusion risks recently many methods have been published for detecting network intruders however they face significant challenges as new threats continue to emerge that are undetectable by older systems this study evaluates different approaches to creating network intrusion detection systems the best features of the dataset are selected based on the correlations between features in addition we provide a complete functional and performance overview of an adaboost-based network attack detection solution based on these selected characteristics.

Agnostic CH-DT Technique for SCADA Network High-Dimensional Data-Aware Intrusion Detection System

The pervasiveness in the industrial internet of things (IIoT) due to the application of supervisory control and data acquisition (SCADA) has led to the growth of heterogeneous sensor data, thereby increasing the risk of intrusions and attacks. The existence and effect of intruders and their innovative attack techniques are on the rise. Existing intrusion detection systems (IDS) tend to be computationally expensive with this form of data due to the presence of noise. In real-time domains, available methods lag, necessitating additional research into effective feature extraction schemes, which is fundamental in machine learning (ML) for time exigency. This study, in a comparative analysis of some feature selection techniques (FS), proposes a combination of an efficient ML classifier and an agnostic feature selection (FS) scheme for attack detection and classification in a real-time SCADA network. The flexibility and interoperability of the proposed approach resolve the computational complexity of vulnerability detection schemes while reducing false alarm rates (FAR) and overall model execution time. With the view of an online preprocessing, the proposed technique is phased thus: (i) data preparatory consisting of data cleansing and normalization followed by (ii) the combination of a pre-pruned decision tree (DT) algorithm and an agnostic Chi-square FS approach built to obtain an optimal subset of data features for efficient IDS. (iii) Evaluation of proposed agnostic DT-CH and other FS candidates for anomaly detection.

Design of A Security System with Mobile Notifications in Case of Intrusion

Physical security is a crucial aspect of cybersecurity, considering the constant risk of unauthorized intrusions and data theft that companies face. While conventional security measures like alarm systems and video surveillance aim to protect physical infrastructure, they often fall short of meeting businesses' actual requirements. It is vital for companies to have immediate awareness of intrusions and the ability to respond promptly. To tackle this challenge, the recommended approach involves implementing an alert security system that offers real-time notifications. This method entails deploying an Internet of Things (IoT) system equipped with sensors throughout the company's premises. These sensors are specifically designed to detect unauthorized entry, triggering instant alerts to the appropriate personnel via a dedicated mobile application. This application enables seamless communication with the IoT system, enabling swift decision-making in response to potential security breaches. In this research, the initial emphasis was on designing the system using Unified Modeling Language (UML) diagrams. Subsequently, the IoT system was implemented, integrating the necessary sensors. Additionally, a user-friendly application was developed to establish a secure connection between the company's system and employees' mobile devices. Through extensive research efforts, an effective and functional system capable of effectively safeguarding businesses has been successfully devised.

Evaluating the role of preferential pathways in exacerbating vapour intrusion risks

Vapour intrusion (VI) has been garnering more attention in the research field in the past few decades owing to its associated health risks. Many field studies have been conducted to understand the process of vapour migration from the source to indoor air and the factors affecting it. This research project primarily concentrated on the role of highly permeable backfill materials like sand and gravel used as bedding and embedment for utility lines. These can function as preferential pathways for vapour migration in the vadose zone. Extensive laboratory investigations were carried out over 13 days using two large two-dimensional (2-D) columns, one with preferential pathway and one without preferential pathway, to investigate the natural attenuation process and the role of preferential pathways in VI. The presence of preferential pathway has a role in determining soil gas vapour distribution in the vadose zone. A notable increase in vapour concentrations was observed in the top layer of the column with preferential pathway compared to the column with no preferential pathway. This was due to enhanced distribution of contaminant vapours along the column facilitated by the preferential pathway. An increase in concentration was observed in the column with preferential pathway throughout compared to the column with no preferential pathway, confirming that the presence of natural or man-made drainage features in the sun-surface can influence the distribution of contaminants throughout a site. This research project generates a better understanding of preferential pathways and recommends taking into account the role of highly permeable backfill materials when developing CSM and VI health risks assessments. In this way the strategic priorities for remediation projects will be successfully established.

Real-time driving risk assessment based on the psycho-physical field

Real-time Driving Risk Assessment (RDRA) is a critical component of traffic safety and is influenced by confounding impacts from drivers, surrounding vehicles, and roadway conditions. Previous studies simplified the RDRA based on kinematic characteristics. However, drivers perceived the risks not only through kinematic characteristics but also by anticipating the behaviors of surrounding participants and execute collision-preventive maneuvers. In this study, an innovative RDRA framework based on the Psycho-Physical Field (PPF) is proposed. Specifically, the PPF of the subjective vehicle exerts repulsive forces on intrusive risk sources in different directions, of which the two-dimensional field distribution is determined by physical collision-related measures and regulated by the psychological characteristics of behavior anticipations and risk perception abilities. The proposed method was first analyzed through theoretical feasibility analyses and verified for over 450 high-risk events from naturalistic driving data, including three typical types of scenarios: car-following, lane-changing, and being cut-in/off. Moreover, the adaptability was further validated through three cases and compared with the risk warning functions of Mobileye. The results showed that the proposed method can provide accurate risk evaluations, and identify potential hazards about 2 s in advance for high-risk cut-in events.

Intrusion Detection Techniques in Social Media Cloud: Review and Future Directions

As social media use increases, the number of users has risen also. This has increased the volume of data carried over the network, making it more important to secure users’ data and privacy from threats. As users are unaware of hackers, social media’s security flaws and new forms of attack will persist. Intrusion detection systems, therefore, are vital to identifying intrusion risks. This paper examines a variety of intrusion detection techniques used to detect cyberattacks on social media networks. The paper provides a summary of the prevalent attacks on social media networks, such as phishing, fake profiles, account compromise, and cyberbullying. Then, the most prevalent techniques for classifying network traffic, including statistical and artificial intelligence (AI) techniques, are addressed. The literature also demonstrates that because AI can manage vast, scalable networks, AI-based IDSs are more effective at classifying network traffic and detecting intrusions in complex social media networks. However, AI-based IDSs exhibit high computational and space complexities; therefore, despite their remarkable performance, they are more suitable for high computing power systems. Hybrid IDSs, utilizing statistical feature selection and shallow neural networks, may provide a compromise between computational requirements and efficiency. This investigation shows that accuracies of statistical techniques range from 90% to 97.5%. In contrast, AI and ML technique detection accuracy ranges from 78% to 99.95%. Similarly, swarm and evolutionary techniques achieved from 84% to 99.95% and deep learning-based detection techniques achieved from 45% to more than 99% detection rates. Convolutional neural network deep learning systems outperformed other methods due to their ability to automatically craft the features that would classify the network traffic with high accuracy.

Community-based social recommendation under local differential privacy protection

Social recommendation refers to recommendation technology taking social relations as additional input to improve merchandise sales and user satisfaction. It has been widely used in various social networking services. Social recommendation requires the collection of each user's social relations to build a social graph, posing the risk of user privacy intrusion caused by untrusted servers. Local differential privacy is a widely adopted approach for providing privacy protection while allowing acceptable utility of the protected data for analytics. Growing research interest has been applying local differential privacy protection to social graph analysis. However, local differential privacy is suitable for analyzing coarse-grained statistics from perturbed user data, and it is difficult to obtain the fine-grained user relations needed for social recommendation. This paper proposes a novel locally differentially private social recommendation method. It first perturbs the degrees of users in a given interval and discovers coarse-grained communities from them, then generates a fine-grained social graph according to the features of intra-community and inter-community relations, thus ensuring the accuracy of the recommendation results. Experimental results show that the social recommendation accuracy of our proposed method improves by about 20% compared to existing methods in a community-structured ego-network dataset, and by about 10% in two non-community-structured page-page network datasets. The experimental results show that compared with the state-of-the-art methods such as LDPGen, LFGDPR, and AsgLDP, the social recommendation accuracy of our proposed method is improved by about 20% in one community-structured ego-network dataset, and 10% in two non-community-structured page-page network datasets.

Skeleton-based automatic assessment and prediction of intrusion risk in construction hazardous areas

Intrusion behavior in hazardous areas is one of the major causes of construction safety accidents including falls from height, strikes by objects, etc. Implementing automatic and precise assessment of intrusions to enhance safety performance is of great importance in construction areas. Due to the large area of construction sites and diverse human behaviors, it is difficult to accurately predict worker behavior, resulting in many intrusions detected after the occurrence. Notably, computer vision-based skeleton extraction can provide a promising non-contact solution for assessing intrusions. This paper presents a novel intrusion behavior detection and evaluation approach by defining a safety buffer zone and using two key quantitative elements, i.e. the motion state and orientation posture of intruders. An indoor experiment was conducted by employing skeleton detection technology with safety knowledge to demonstrate the feasibility and effectiveness of the assessment method. The participants’ risk levels were evaluated separately and simultaneously based on the motion and posture. The risk level was compared based on various evaluated methods and the ground truth. The results show that a satisfying accuracy of intrusion assessment can be achieved at different risk levels. Appropriate warning and intervention methods can be implemented to mitigate the occurrence or reduce the severity of intrusions and thus reduce safety accidents with the use of the proposed method.

The Spatio-Temporal Dynamic Patterns of Shallow Groundwater Level and Salinity: The Yellow River Delta, China

Shallow groundwater in coastal aquifers is a highly dynamic and complex system with a high risk of seawater intrusion. Analyzing the spatio-temporal dynamic patterns of groundwater can help to manage the groundwater resource and prevent it from degradation. Based on the groundwater level (GWL) and electrical conductivity (EC) monitoring data of 18 observation wells in the Yellow River Delta (YRD) from 2004 to 2010, this research analyses the groundwater dynamics using a robust seasonal trend decomposition technique (STL) and spatial interpolation method to detect the groundwater spatio-temporal dynamic patterns of groundwater level and salinity. Combined with hydro-climatic data, the Pearson correlation method and the Mann-Kendall (MK) trend analysis were used to further reveal the impacts that induce their trends and seasonal variations. Our analyses show that the risk of seawater intrusion into local shallow aquifers in this region is high, with the mean groundwater level over 42% of the region lower than the local sea level, and the mean groundwater EC over 96% of the region met the standards for seawater intrusion. In addition, the trends of groundwater level generally declined by 0.01~0.45 m/a and salinity increased by 1.153~25.608 μs/cm.a, which are consistent with the trend of precipitation decline. The seasonal dynamics of groundwater level and salinity are highly correlated with the seasonal components of rainfall and evaporation. It can be concluded that the extent of seawater intrusion will increase in the future with sea level rise. The approaches used in this study proved to be effective and can certainly serve as an example for the analysis of the spatio-temporal dynamics of groundwater in other coastal regions.

The Proposed Intrusion Detection System using Support Vector Machine by IOT enabled WSN: A Survey

Abstract: Detecting intruders in computer networks is very important because it affects multiple communication and security domains finding network intruders can be difficult furthermore network intrusion detection remains a challenging undertaking due to the large amount of data required to train modern machine learning models to detect network intrusion risks recently many methods have been published for detecting network intruders however they face significant challenges as new threats continue to emerge that are undetectable by older systems this study evaluates different approaches to creating network intrusion detection systems the best features of the dataset are selected based on the correlations between features in addition we provide a complete functional and performance overview of an adaboost-based network attack detection solution based on these selected characteristics.

Research on Seawater Intrusion Suppression Scheme of Minjiang River Estuary

Seawater intrusion in the Minjiang River estuary has gravely endangered the water security of the surrounding area in recent years. Previous studies mainly focused on exploring the mechanism of intrusion, but failed to provide a scheme for suppressing seawater intrusion. The three most relevant determinants to chlorine level, which represented the strength of seawater intrusion, were determined using Pearson correlation analysis as being the daily average discharge, daily maximum tidal range, and daily minimum tidal level. Considering the lower requirement of sample data and the ability to handle high-dimensional data, the random forest algorithm was used to construct a seawater intrusion suppression model and was combined with a genetic algorithm. The critical river discharge for suppressing estuary seawater intrusion determined using this model. The critical river discharge was found to gradually increase with the maximum tidal range, which in three different tide scenarios was 487 m3/s, 493 m3/s, and 531 m3/s. The practicable seawater intrusion suppression scheme was built up with three phases to make it easier to regulate upstream reservoirs. In the scheme, the initial reading of river discharge was 490 m3/s, and it rose to 650 m3/s over six days, from four days before the high tide's arrival to two days following it, and before falling down to 490 m3/s at the end. Verified with the 16 seawater intrusion events in the five dry years, this scheme could eliminate 75% of the seawater intrusion risk and effectively reduce the chlorine level for the remaining 25% of events.