Security Alert Research Articles

A Keypad-Based Mobile App Security Alert System with Virtual Private Network and Short Message Service Integration

In the digital age, mobile devices are integral to daily life, making robust security measures essential for protecting sensitive data. This paper presents the development of a mobile application aimed at enhancing personal security through a keypad-based authentication system, integrated with Short Message Service (SMS) and Virtual Private Network (VPN) functionalities. The intuitive interface of keypads allows users to swiftly send predefined emergency messages with a single keystroke, including their current location and urgency details. The application ensures encrypted and secure communication via a VPN and leverages SMS for reliable real-time notifications, even in areas with limited connectivity. A user-centered design approach, utilizing React JS for frontend development and Bootstrap for a responsive UI, ensures accessibility and ease of use for individuals with varying levels of technological proficiency. Rigorous testing validates the application's functionality and performance, demonstrating its potential to streamline emergency communication and enhance personal security in critical situations.

Legislation of Constituent Entities of the Russian Federation on the High Security Alert Regime: A Comparative Analysis

Legislation of Constituent Entities of the Russian Federation on the High Security Alert Regime: A Comparative Analysis

Improving Safety and Satisfaction in the Safety Monitoring Unit.

BACKGROUND: The safety monitoring unit (SMU) is a 4-bed unit designated for patients who require continual observation. Most experience some form of dementia, and agitation and aggression are not uncommon. When deescalation techniques do not work, request for help may be necessary. Referred to as Security Alert: Behavioral Assist (SABA), this system-wide message requires response from designated personnel. An increase in SABA events prompted this quality improvement project. METHODS: A survey sent to all unit staff members identified a need for specialized training on the care and management of SMU patients. Education on dementia care and deescalation techniques was provided by a certified dementia specialist and a psychiatry advanced practice registered nurse. Staff expressed a need for defined SMU admission criteria and the establishment of patient care guidelines. Patient care guidelines were developed. A structured schedule was implemented, and dedicated staff were hired to provide familiarity for patients. RESULTS: A postproject survey indicated a nonsignificant increase in staff satisfaction. Security Alert: Behavioral Assist events in the SMU decreased from an average of 3.6 to 1.75 episodes per month. CONCLUSION: Caring for SMU patients creates unique challenges to staff. Staff confidence and satisfaction were higher after implementing new SABA policies. This project could be replicated on similar units with ongoing leadership support and staff education.

A Lightweight Detection Scheme for the Safety of Human Behaviors in Power Construction Sites

Safety mishaps at electric power construction sites have been more frequent in recent years as a result of the growth of the electric power business, the inescapable carelessness of current safety oversight, and other causes. Traditional manual supervision and video surveillance methods demand a lot of human resources and suffer from issues like low efficiency and lack of objectivity, making it unable to provide security alerts in real-time and accurately. Therefore, solutions like intelligent monitoring based on object detection are required to address this issue. Many object detection methods now in use (such as R-CNN, Fast R-CNN, and YOLOV5) are computationally and energy intensive. They are typically installed on remote back-end servers, which makes it impossible to guarantee the real-time security alert. This paper trains a lightweight NanoDet network model to recognize the attire and actions of power construction workers in order to address the aforementioned issues. Additionally, in order to meet the real-time security warning requirements and fully utilize the limited computing resources of edge devices, the NanoDet network model is divided, with some layers being divided into CPU execution. According to experiments, power consumption can be reduced by deploying the Backbone or PAFPN blocks of the NanoDet model to CPUs and running them in 30W4Core mode.

AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods

Intrusion analysis is essential for cybersecurity, but oftentimes, the overwhelming number of false alerts issued by security appliances can prove to be a considerable hurdle. Machine learning algorithms can automate a task known as security alert data analysis to facilitate faster alert triage and incident response. This paper presents a bidirectional approach to address severe class imbalance in security alert data analysis. The proposed method utilizes an ensemble of three oversampling techniques to generate an augmented set of high-quality synthetic positive samples and employs a data subsampling algorithm to identify and remove noisy negative samples. Experimental results using an enterprise and a benchmark dataset confirm that this approach yields significantly improved recall and false positive rates compared with conventional oversampling techniques, suggesting its potential for more effective and efficient AI-assisted security operations.

Constructing An Integrated IoT-based Smart Home with An Automated Fire and Smoke Security Alert System

The Internet of Things is quickly becoming one of the most important developments in this century's automation technology field. The Internet of Things (IoT) establishes a new approach to resolving issues that may arise in the foreseeable future by establishing an interface between the many devices that are now in use and integrating the functionalities of these devices. This study's primary objective is to develop methods that will cleverly simplify living arrangements and increase their convenience. The majority of things are now automated and computerized. This work aims to provide a system for integrating sensors and other data sources for different home automation. A home with intelligence on par with humans is called a smart home. This study proposes an IoT-based smart home automation system that operates in different modules to automate the complete home. If there is a problem, it will immediately identify and attempt to resolve it. In that case, it will send a notification to the homeowner. The suggested prototype included an automated door lock system, an automated gas detection system, and a smart smoke and fire alert system that sends an alert message to the owner and remotely controls the inside fan light. The system will immediately notify the users if a fire is detected in the user's residence. A smartphone application known as the Blynk App controls things remotely. As a result of being transformed into a smart home, it can offer security, energy efficiency, and the capability to control all of the current appliances in the house remotely or from a central place. Due to the exponential growth of technology, day-to-day activities are becoming less complicated, which has contributed to the popularity of home automation systems in recent years.

Improved Detection and Response via Optimized Alerts: Usability Study

Security analysts working in the modern threat landscape face excessive events and alerts, a high volume of false-positive alerts, significant time constraints, innovative adversaries, and a staggering volume of unstructured data. Organizations thus risk data breach, loss of valuable human resources, reputational damage, and impact to revenue when excessive security alert volume and a lack of fidelity degrade detection services. This study examined tactics to reduce security data fatigue, increase detection accuracy, and enhance security analysts’ experience using security alert output generated via data science and machine learning models. The research determined if security analysts utilizing this security alert data perceive a statistically significant difference in usability between security alert output that is visualized versus that which is text-based. Security analysts benefit two-fold: the efficiency of results derived at scale via ML models, with the additional benefit of quality alert results derived from these same models. This quantitative, quasi-experimental, explanatory study conveys survey research performed to understand security analysts’ perceptions via the Technology Acceptance Model. The population studied was security analysts working in a defender capacity, analyzing security monitoring data and alerts. The more specific sample was security analysts and managers in Security Operation Center (SOC), Digital Forensic and Incident Response (DFIR), Detection and Response Team (DART), and Threat Intelligence (TI) roles. Data analysis indicated a significant difference in security analysts’ perception of usability in favor of visualized alert output over text alert output. The study’s results showed how organizations can more effectively combat external threats by emphasizing visual rather than textual alerts.

Dealing with Security Alert Flooding: Using Machine Learning for Domain-independent Alert Aggregation

Intrusion Detection Systems (IDS) secure all kinds of IT infrastructures through automatic detection of malicious activities. Unfortunately, they are known to produce large numbers of alerts that often become overwhelming for manual analysis. Therefore, aggregation methods have been developed for filtering, grouping, and correlating alerts. However, existing techniques either rely on manually defined attack scenarios or require specific alert formats, such as IDMEF that include IP addresses. This makes the application of existing aggregation methods infeasible for alerts from host-based or anomaly-based IDSs that frequently lack such network-related data. In this paper, we therefore present a domain-independent alert aggregation technique. We introduce similarity measures and merging strategies for arbitrary semi-structured alerts and alert groups. Based on these metrics and techniques we propose an incremental procedure for the generation of abstract alert patterns that enable continuous classification of incoming alerts. Evaluations show that our approach is capable of reducing the number of alert groups for human review by around \( 80\% \) and assigning attack classifiers to the groups with true positive rates of \( 80\% \) and false positive rates lower than \( 5\% \) .

Perpetrator and situational characteristics associated with security alerts in regional Australian emergency departments

BackgroundWorkplace violence is a regular feature of emergency departments (ED) and reported to be increasing in frequency and severity. There is a paucity of data from regional EDs in Australia. The aim of this study was to identify the perpetrator and situational characteristics associated with security alerts in regional emergency departments.MethodsThis retrospective descriptive study was conducted in two regional Australian hospital EDs. All incident reports, hospital summary spreadsheets, and patient medical records associated with a security alert over a two-year period (2017 - 2019) were included. The situational and perpetrator characteristics associated with security alerts in the ED were recorded.ResultsOne hundred fifty-one incidents were reported in the two-year period. Incidents most frequently occurred on late shifts and in an ED cubicle. Most incidents included multiple disciplines such as ED staff and paramedics, police and psychiatric services. One hundred twenty-five incidents had sufficient information to categorise the perpetrators. Mental and behavioural disorders (MBD) were the most frequent perpetrator characteristic present in security alerts (n = 102, 81.6%) and were associated with increased severity of incidents. MBDs other than psychoactive substance use (PSU) were associated with 59.2% (n = 74) of incidents and 66.7% (n = 18) of injuries. PSU was associated with 42.4% (n = 53) of incidents. Following PSU and MBDs other than PSU, repeat perpetrators were the next most prominent perpetrator category (24.8% n = 31) and were almost always associated with an MBD (93.5% n = 29).ConclusionsViolence incidents in the ED are often complex, patients present with multiple issues and are managed across disciplines. Interventions need to extend from one size fits all approaches to targeting specific perpetrator groups. Since MBDs are one of the most significant perpetrator factors, interventions focussing on this characteristic are needed to address workplace violence in EDs.

A Ranking Based Approach in Security Alert for Handling Sensitive Data Organization

A Ranking Based Approach in Security Alert for Handling Sensitive Data Organization

An enhanced security alert system for smart home using IOT

<p>Today due to advancement in technology and Internet of Things, to deal with step-by-step jobs like mailing, accessing bank entries, watching degree of hotness and other jobs PCs were utilized. Because of fast improvement in IOT nowadays tablets, advanced mobile phones are utilized for such assignments [1]. Home security can be characterized as observing the home remotely located or centrally located. Today, home security is getting to be vital as the conceivable outcomes of interruption is expanding step by step, yet they have issues like postponement, non-web empowered and hard to deal. Intruder recognition is basic parts of home computerization and home security frameworks. Most generally, utilized specialized devices are mobile phones and sending mails are considered for controlling home appliances remotely by proposing productive structures. An Enhanced Security Alert System for Smart Home using IOT is composed in our proposed work. It identifies the intruder when no one is in home by sending alert message or caught picture to the proprietor (or) owner. Proposed framework is ease home security framework utilizes an IP Web-camera and Raspberry pi for limiting delay during the time of sending alert message. Fog Computing is incorporated to proposed framework to expand the level of security and limit the engendering delay. Proposed framework is to give minimal effort arrangement and adaptable association instrument for coordinating Internet of Things with home security frameworks.</p><p><em> </em></p>

Should Public Internet Security Alerts Be Better Governed?

Despite all the security measures to build a securely networked world there are innumerable loopholes for malicious activity in the Internet. One of the countermeasures to fight such malicious activities is to issue security alerts. The purpose of this paper is to see if there is significant impact by these security alerts; to best of our knowledge, this is the first statistical study to understand such impact of Internet security alerts. For the analysis, this paper looks at two of the most malicious security alerts, the Phishing and Spam, issued by two well-known private sources. These alerts often include rankings of countries based on the number of security violations. The statistical analysis concludes that for phishing, the security alerts, in terms of rankings, indeed have a significant positive impact. For spam, the security alert data is less frequent. As such, the available data is limited; and, our statistical analysis concludes that spam is still out of control. Though there is positive impact on Internet security as a whole due to the alerts and rankings, there are no governing rules or policies requiring the sources issuing such data to be regular and systematic. Indeed, such rankings are kind of ad hock at present and do not come under the guidelines of any governing bodies of the Internet. An objective of this paper is to highlight the issues in current security alert and ranking process and stress upon the need of some kind of governance of this process.

Long Range Security and Safety Tracking System for Merchant Vessels

Seventy percent of the earth’s surface is covered by water, in this large area the vessels are trading, fishing and transporting. Whatever the purpose of sailing we can consider it shipping and is relaying and requiring safety navigation, taking into account keeping clean environment. The Shipping plays a vital role in the economics of most countries so the safety and security control and monitoring from shore is significant. This paper will explain the maritime vessels security systems and safety and will be concentrated on the integration of the security system devices, safety system devices and transmission through satellite communication systems to the shore side, by other means main office on land, to raise the level of safety and security. The safety by choice not by chance Keywords: IMO international maritime organization, GIS geographic information system, SOLAS safety of live at sea ,VDR voyage data recorder ,ECDIS electronic chart display and information system ,RADAR radio detection and ranging ,AIS automatic identification system ,MIS marine information system ,PLC programming logic control ,SCADA supervisory control and data acquisition , GPS global position system , GMDSS global maritime distress and safety system ,SASS Ship Security Alert System ,NAVTEX national weather service BNWAS bridge navigational watch alarm system ,NMEA 183 combined electrical and data specification for communication between marine electronics . tronics .

Advanced Security Model for Detecting Frauds in ATM Transaction

card fraud is causing billions of dollars in losses for the card payment industry. In today's world the most accepted payment mode is Debit card for both online and also for regular purchasing; hence frauds related with it are also growing. To find the fraudulent transaction, we implement an Advanced Security Model for ATM payment using Hidden Markov Model (HMM), which detects the fraud by using customers spending behavior. This Security Model is primarily focusing on the normal spending behavior of a cardholder and some advanced securities such as Location, Amount, Time and Sequence of transactions. If the trained Security model identifies any misbehavior in upcoming transaction, then that transaction is permanently blocked until the user enter High Security Alert Password (HSAP). This paper provides an overview of frauds and begins with ATM card statistics and the definition of ATM card fraud. The main outcome of the paper is to find the fraudulent transaction and avoids the fraud before it happens.

A Paper On “Design and Implementation of Automatic Security Alert System Using GSM Network”

Real-time surveillance is an important aspect of an intelligent building with modern security demands. This paper presents the design and Implementation of Automatic Security Alert System Using PCI Board, GSM Network and Cross Platform Technology. To solve the problem of complex cabling, misdeclaration and missing alarm of traditional alarm system, GSM network is used. The project deals with a complete end-to- end detection and alerting application which employs advanced technologies. Telecommunication card is used for the proper communication between the two ends as it offers communication between a numbers of places with the control centre. Cross platform is used as it is convenient to use single source code on multiple operating systems. In the following paper the application for fire is considered, where architecture for an Intellectual Voice Response System (IVRS) is being proposed. This system has been successfully installed in one of the corporation which is very useful for the society. Index Terms: Fire alert system, GSM module, smoke detector, dialogic card, Cross platform.

Are Capital Markets Color-Blind? The Impact of the Homeland Security Alert Level on Capital Markets

AbstractThis paper examines whether changes in the color-coded terror alert level setby the U.S. Department of Homeland Security affect U.S. capital markets. Thealert level has changed 17 times (9 times raised and 8 times lowered) from thetime the alert system was established on March 12, 2002 until September 21,2006. Using daily index data for the main U.S. capital markets and employing astandard event study framework, I find that markets do not react significantly tochanges in the terror level. A heteroscedasticity-based estimation technique us-ing information provided by the second moments of financial variables and othertests using data on industry level, trading volume, interest rates and U.K. stockprices also indicate minimal responses of capital markets to changes in threatlevels. It appears that from the investors’ point of view, threat level changes arenot informative, or the amount of information they provide is minor.JEL Classification. G14, H56Keywords. Terrorism, Capital Markets, Homeland Security Advisory System(HSAS), Color-Coded Threat Level