Prevent Security Threats Research Articles

An adaptive multistage intrusion detection and prevention system in software defined networking environment

The advancements made in Software-Defined Networking (SDN) technology seem quite promising, with potential wide application in managing and controlling the latest network infrastructures. SDN technology decouples the control plane from the data plane, enabling effective and flexible network management. However, this dynamic phenomenon brings new security challenges. With the increasing dynamism and programmable nature of networks, conventional security protocols may not sufficient to protect against advanced and sophisticated attacks. Although Intrusion Detection Systems (IDSs) have been extensively applied for identifying and preventing security threats in traditional network environments, IDS models designed specifically for traditional network requirements may not be adequate for SDN environments. These issues may stem from the static nature of conventional networks, contrasting with the dynamicity of advanced SDN networks, and the traditional IDS’s inability to adapt to the dynamic nature of SDN. To address these challenges, the current research proposes a novel Deep Hybrid IDS model to enhance network security in SDN environments and prevent attacks using Scapy. The proposed model detects signature-based attacks by integrating Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) for real-time simulated datasets, achieving an accuracy of 97.8%, which is comparatively better than existing models.

Implementation of ISPS Code Security Level 2 at the Time the Ship Entered the Pontianak Navy Line to Prevent Security Threats Load at KM. Meratus Benoa

This research aims to implement ISPS Code Security Level 2 when the ship enters the Pontianak shipping channel to prevent cargo security threats on KM. Meratus Benoa. The time used by researchers to conduct research for 11 months and 13 days, starting from March 11, 2023-24 February 2024. KM. Meratus Benoa is one type of container deck ship managed by PT Meratus Line. This research method uses qualitative methods using Phenomenological research strategies. This data source collection technique is obtained from direct observation, interviews with ship crews and several journals related to this research. The results of this study indicate that the implementation of ISPS Code Security Level 2 when the ship enters the Pontianak shipping channel to prevent cargo security threats on KM.Meratus Benoa, namely by, a) Involving various efforts and activities such as patrols, security drills and additional supervision. Crew awareness and external support are also factors that influence the effectiveness of ISPS Code Security Level 2 implementation. b) There are coordination and training efforts carried out by ship officers to improve crew preparedness in dealing with security threats. As for the efforts made to prevent cargo security threats on KM Meratus Benoa by a) Restricting access to the ship and inside the ship to prevent unauthorized access, adding bulkheads to the pilot ladder door to increase security, adding guard officers and optimal division of guard duties when the ship enters a high-risk port area. b) conducting annual security exercise trials on a regular basis.

Application of Artificial Intelligence Technologies in Security of Cyber-Physical Systems

Cybersecurity questions of cyber-physical systems (CPS) have become ever more vital in recent times. Advances in technology and digitization have posed new weaknesses in CPS and cyber attackers take advantage of it. Viruses, malware, and sophisticated forms of cyberattacks have become a dangerous reality for critical infrastructure CPS. Lately, artificial intelligence (AI) technology has been extensively applied in the struggle against cyber threats in CPS. AI may improve system security by providing tools to quickly detect cyberthreats and automatically resolve them. Digitization of critical infrastructures (energy distribution networks, smart systems, oil and gas industry, water infrastructure, etc.) has increased their efficient management and at the same time, the number of cyber attackers on sensors, actuators, network and control equipment has also increased. Cyber security of critical objects can be ensured through AI. This article explores the theoretical foundations, application fields, and AI conceptual models. It analyzes the benefits and shortcomings of applying AI technologies to the security of the abovementioned systems. Mechanisms for detecting cyber threats in cyber-physical systems with the help of AI and predicting and preventing security threats are proposed.

Performance Analysis of the Brimob Training Unit (Satlat Brimob) Using a SWOT Approach

This research aims to analyze organizational performance measurements at the Brimob Training Unit (Satlat Brimob) using the SWOT method. The study employs a descriptive analysis with a qualitative approach. The findings reveal several strategies derived from the SWOT analysis. The Strengths-Opportunities strategy involves deploying trained resources to address security needs, collaborating with agencies for suitable and renewable equipment and weapons procurement, and conducting HR training with nationally and internationally accredited agencies. The Weaknesses-Opportunities strategy includes coordinating with the Indonesian National Police (INP) Headquarters to fulfill personnel requirements according to the Personnel and Equipment Composition List (DSPP) and seeking additional budget allocations and supporting facilities. The Strengths-Threats strategy focuses on positioning trained human resources in security areas to counter disturbances from radical groups and evolving crime modes, as well as utilizing adequate equipment to prevent security threats and detect sophisticated criminal activities. The Weaknesses-Threats strategy involves assigning the best personnel to maintain security against radical groups and implementing supporting facilities with information systems to detect crime routes. The research highlights the importance of strategic resource allocation, inter-agency collaboration, and continuous improvement in training and equipment to enhance the Brimob Training Unit's organizational performance and address security challenges effectively. These findings provide valuable insights for improving the operational efficiency and effectiveness of the Brimob Training Unit in maintaining public safety and security.

Two-Phase Industrial Control System Anomaly Detection Using Communication Patterns and Deep Learning

Several cases of Industrial Internet of Things (IIoT) attacks with zero-day vulnerabilities have been reported. To prevent these attacks, it is necessary to apply an abnormal behavior detection method; however, there are three main problems that make it hard. First, there are various industrial communication protocols. Instead of IT environments, many unstandardized protocols, which are usually defined by vendors, are used. Second, legacy devices are commonly used, not only EOS (End-of-service), but also EoL (End-of-Life). And last, the analysis of collected data is necessary for defining normal behavior. This behavior should be separately defined in each IIoT. Therefore, it is difficult to apply abnormal behavior detection in environments where economic and human investment is difficult. To solve these problems, we propose a deep learning based abnormal behavior detection technique that utilizes IIoT communication patterns. The proposed method uses a deep learning technique to train periodic data acquisition sequences, which is one of the common characteristics of IIoT. The trained model determined the sequence of packet is normal. The proposed technique can be applied without an additional analysis. The proposed method is expected to prevent security threats by proactively detecting cyberattacks. To verify the proposed method, a dataset was collected from the Korea Electric Power Control System. The model that defines normal behavior based on the application layer exhibits an accuracy of 79.6%. The other model, defining normal behavior based on the transport layer, has an accuracy of 80.9%. In these two models, most false positives and false negatives only occur when the abnormal packet is in a sequence.

The Role of Artificial Intelligence in Cyber Security

Without significant automation, humans are unable to manage the volume of data and the complexity of procedures required to safeguard cyberspace. Creating software and systems with typical fixed implementations (hardwired decision-making logic) that successfully prevent security threats is difficult. This problem can be solved with machine simplicity and AI learning approaches. This study assesses whether strengthening defensive mechanisms can improve cybersecurity capabilities and offers a brief summary of artificial intelligence (AI) applications of various cybersecurity using artificial technologies. We can determine that there are currently useful applications by looking at the most recent cybersecurity software that makes use of artificial intelligence. Neural networks are their primary tool for protecting the periphery and other cybersecurity domains. Nevertheless, it was evident that some cybersecurity problems would require the application of artificial intelligence technology. For instance, thorough information is needed for strategic decision-making, and logical decision support is one of the unmet cybersecurity needs.

Empowering Individuals Through Community Engagement in National Security: Shaping A Safer Future

This article explores into the pivotal role of community engagement in augmenting national security by empowering individuals to actively participate in safeguarding their communities. By examining the symbiotic relationship between citizens and security agencies, it elucidates how grassroots initiatives and collaborative efforts contribute to enhancing the overall security landscape. Drawing from diverse examples worldwide, ranging from neighborhood watch programs to digital citizen reporting platforms, this article showcases instances where proactive community involvement has led to significant advancements in preventing security threats. Through a comprehensive analysis, it underscores the transformative potential of community engagement, not only in mitigating risks but also in furthering resilience and inclusivity within societies. Ultimately, this exploration underscores the imperative of cultivating a culture of shared responsibility and collective action in safeguarding national security, thereby paving the way for a safer and more secure future.

A Hybrid Cryptographic Mechanism for Secure Data Transmission in Edge AI Networks

As Edge AI systems become more prevalent, ensuring data privacy and security in these decentralized networks is essential. In this work, a novel hybrid cryptographic mechanism was presented by combining Ant Lion Optimization (ALO) and Diffie–Hellman-based Twofish cryptography (DHT) for secure data transmission. The developed work collects the data from the created edge AI system and processes it using the Autoencoder. The Autoencoder learns the data patterns and identifies the malicious data entry. The Diffie–Hellman (DH) key exchange generates a shared secret key for encryption, while the ALO optimizes the key exchange and improves security performance. Further, the Twofish algorithm performs the data encryption using a generated secret key, preventing security threats during transmission. The implementation results of the study show that it achieved a higher accuracy of 99.45%, lower time consumption of 2 s, minimum delay of 0.8 s, and reduced energy consumption of 3.2 mJ.

Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques

In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.

Application of Artificial Intelligence Technology in the Field of Digital Currency Security

This paper explores the application of artificial intelligence technology in ensuring the security of digital currencies. With the increasing popularity of digital currencies, ensuring their security has become a crucial concern. AI techniques, such as machine learning and deep learning, can be employed to identify and prevent security threats in digital currency systems. Additionally, it investigates how AI enhances security measures and risk control on digital currency trading platforms, offering insights into real-time risk alert systems. Therefore, through empirical research in four distinct sections, this paper continuously refines and revises the theoretical model proposed in this project. The ultimate goal is to provide new theoretical tools for the management of financial stability in the process of Digital Currency replacing M0. this project gives the research content of the four parts, which include public perception, delivery path, underlying technology and virtual currency bubble, we hope that this research can offer valuable insights to relevant field managers.

Information security communications strategy as a prerequisite to counteracting hybrid warfare: world experience

The need for innovative tools to counter hybrid threats makes this study relevant. Professional cybersecurity can be proactive and prevent security threats; moreover, its combination with a professional communications strategy can serve as a countermeasure to hybrid warfare and enemy attacks. The article aims to provide a comprehensive comparative analysis of the information security strategies of Ukraine, Kazakhstan, and the European Union, determine the availability of communication strategies for cybersecurity in these countries, and suggest recommendations for developing or improving information security communications strategy. The study examines the current state of information security in the Republic of Kazakhstan, Ukraine, and the European Union. As a part of the study, the authors applied such methods as analysis, systematization, and comparison. The result of the study is the recommendations for the development of information security communications strategies.

Detecting and Preventing Common Web Application Vulnerabilities: A Comprehensive Approach

Web applications are becoming very important in our lives as many sensitive processes depend on them. Therefore, it is critical for safety and invulnerability against malicious attacks. Most studies focus on ways to detect these attacks individually. In this study, we develop a new vulnerability system to detect and prevent vulnerabilities in web applications. It has multiple functions to deal with some recurring vulnerabilities. The proposed system provided the detection and prevention of four types of vulnerabilities, including SQL injection, cross-site scripting attacks, remote code execution, and fingerprinting of backend technologies. We investigated the way worked for every type of vulnerability; then the process of detecting each type of vulnerability; finally, we provided prevention for each type of vulnerability. Which achieved three goals: reduce testing costs, increase efficiency, and safety. The proposed system has been validated through a practical application on a website, and experimental results demonstrate its effectiveness in detecting and preventing security threats. Our study contributes to the field of security by presenting an innovative approach to addressing security concerns, and our results highlight the importance of implementing advanced detection and prevention methods to protect against potential cyberattacks. The significance and research value of this survey lies in its potential to enhance the security of online systems and reduce the risk of data breaches.

Assessment of Deep Packet Inspection System of Network traffic and Anomaly Detection

Deep packet SSL inspection is a process that involves decrypting and inspecting SSL encrypted network traffic in order to detect and prevent security threats. With the increasing use of SSL encryption, it has become difficult for traditional network security solutions to inspect encrypted traffic for threats. Deep packet SSL inspection addresses this problem by decrypting the SSL traffic, inspecting it for threats, and then re-encrypting it before forwarding it to its destination. This process involves the use of SSL certificates that mimic the real ones used by the servers, as well as SSL inspection rules that specify which traffic should be decrypted and inspected. Deep packet SSL inspection can be a complex and resource- intensive process, and must be performed carefully to avoid legal or ethical issues related to the interception and inspection of encrypted traffic. However, it is a powerful tool for protecting networks from security threats, and can help organizations detect and prevent attacks that would otherwise go unnoticed.

Research on Industrial IoT Security Based on Deep Learning

<p>With the rapid development of "Internet +" and the construction of a new generation of information infrastructure, the intrusion behaviors against the Industrial Internet of Things are increasingly common. How to ensure the security of the industrial Internet of Things is one of the current research hotspots. The modern technology trend has the hottest technologies of the Internet of Things (IoT). The application of IoT on the other hand improves work efficiency and brings convenience to people’s life; on the other hand, it makes the network face increasingly serious security threat problems and attacks the network by unscrupulous elements occur from time to time. Machine learning-based intrusion detection techniques involve a large number of mathematical formula operations, while with the development of neural networks, the excellent autonomous feature learning capability of deep learning is recognized. An intrusion detection system plays an important role in preventing security threats and protecting them from attacks. The current research on industrial IoT security technology focuses on authentication technology, encryption technology, access control technology, and intrusion detection technology. In this paper, we analyze deep learning and industrial IoT intrusion detection and use the powerful data processing capability and feature learning capability of deep learning to conduct an in-depth study on industrial IoT intrusion detection methods based on deep learning. This paper achieves a 96.32% detection rate on industrial control dataset, which can better adapt to the needs of industrial IoT intrusion detection.</p> <p> </p>

Securing Information Management in Collaborative Environments Using Machine Learning

Recently, there has been a significant increase in the use of collaborative environments for managing and sharing information. However, these environments often present significant security risks due to the potential for unauthorized access, data leakage, and other security breaches. To address these risks, machine learning (ML) techniques have been increasingly used to secure information management in collaborative environments. We propose a novel ML approach to be applied to detect and prevent security threats in collaborative environments. Our approach integrates temporal convolution to detect and prevent security threats by analyzing spatial-temporal patterns in data from various sources, such as network traffic, system logs, and user behavior. Furthermore, we present a case study demonstrating the effectiveness of our model in securing collaborative information management. The case study involves the development of our system for detecting insider threats in a collaborative environment. Extensive experimentation on this case study demonstrates the efficiency and effectiveness of the proposed system for securing information management and promoting further developments.

AHDNN: Attention-Enabled Hierarchical Deep Neural Network Framework for Enhancing Security of Connected and Autonomous Vehicles

The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles’ security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks.

Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

AbstractAs mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)‐based column generation (CG‐ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.

An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms

To ensure the safe operation of static and dynamic devices in a limited platform space and prevent security threats caused by accidents and external intruders, the unmanned aerial vehicle (UAV) is becoming an important tool to monitor and reduce the operational risk of unattended offshore oil platforms. However, UAVs may encounter location errors, adversely affecting patrol efficiency and accuracy. Therefore, the optimal UAV path planning is vital to ensure complete monitoring routes and reduce the risk presented by the marine environment, devices, and employees on unattended offshore platforms. This paper established a multi-objective mathematical model with the shortest flight path and minimum correction times for the intelligent UAV patrol of unattended offshore platforms. An intelligent algorithm with large-scale constraints for UAV path planning was proposed based on non-dominated sorting genetic algorithms. A flight path length of 8076.11 m and 25 correction times presented the optimal solution. The results indicated that the proposed algorithm could be used to plan an effective and accurate three-dimensional (3D) UAV flight path according to the size of the offshore oil platform. This is highly significant for the intelligent risk monitoring of unattended offshore platforms in practical engineering in the future.

Протесты против цен на газ в Казахстане: первое в истории вмешательство ОДКБ в одном из ее государств-членов

Relevance. The intervention of the peacekeeping forces of the Collective Security Treaty Organization (CSTO) into Kazakhstan is the first case of using the peacekeeping forces of the CSTO to ensure the sovereignty of a Member State of the Treaty, to prevent security threats and stabilize situation. This operation demonstrated the readiness of the states to provide assistance within the framework of the Collective Security Treaty on the basis of mutual agreement and at the request of the injured state. The purpose of this study is to examine the CSTO peacekeeping forces participation in the conflict resolution in Kazakhstan. The objectives of the study are to consider the main aspects of the history of CSTO establishment; to study the triggers of the conflict in Kazakhstan; to analyze the activities of the organization in relation to the conflict under con-sideration; to substantiate the need for the actions taken by the Organization; to study the role of the CSTO in the resolution of this conflict. Methodology. To carry out this study, general scientific and private-law methods (technical-legal method) as well as the historical-legal method were used. Results. The Collective Security Treaty Organization is a regional international organization that ensures peace and security. It can be argued with a high degree of probability that the CSTO is ready to repel attempts to violate the sovereignty of its member states. Conclusions. It is necessary to further improve the legal framework of the CSTO in order to counter new threats and challenges of the present. The consent of the Member States in assistance and support will ensure normal de-velopment of each Member State and exclude the possibility of outside interference in their internal affairs. In general, this is exactly the organization that can ensure security in the region.

IoT 네트워크에서 보안위협 확산 방지를 위한 스마트 세그멘테이션 프레임워크 구현

이동통신과 사물인터넷 발전에 따라 네트워크 연결 디바이스 증가 및 사물인터넷 서비스 대중화로 디바이스 취약점을 악용하는 보안사고가 급증하고 있다. 특히 제한된 자원 특성으로 보안이 취약한 디바이스들이 해커들의 공격 대상이 되고 있으며, 대표적인 것이 대규모 분산 서비스 거부(DDoS) 공격을 유발하는 Mirai 악성코드의 감염확산이다. DDoS 공격 대응 방법 중 하나는 DDoS 공격 원인인 악성코드 확산 억제를 통한 대규모 사물봇넷 생성을 방지하는 것이다. 본 논문은 IoT 네트워크 내의 보안위협 확산을 억제하기 위해 디바이스 속성, 네트워크 정보, 서비스 유형 등을 기준으로 세그멘트를 정의하고, 보안위협 확산 여부에 따라 세그멘트 단위로 네트워크 접속을 자율적으로 통제하는 스마트 세그멘테이션 프레임워크에 대한 구현을 설명한다. 제안된 스마트 세그멘테이션 프레임워크는 Mirai 악성코드 감염 환경에서의 시험을 통해 악성코드 확산을 억제할 수 있음을 검증하였다.