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Mehra, Ankush, Badotra, Sumit.  2021.  Artificial Intelligence Enabled Cyber Security. 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC). :572–575.
In the digital era, cyber security has become a serious problem. Information penetrates, wholesale fraud, manual human test breaking, and other comparable occurrences proliferate, influencing a large number of individuals just as organizations. The hindrances have consistently been endless in creating appropriate controls and procedures and putting them in place with utmost precision in order to deal with cyber-attacks. To recent developments in artificial intelligence, the danger of cyber - attacks has increased drastically. AI has affected everything from healthcare to robots. Because malicious hackers couldn't keep this ball of fire from them, ``normal'' cyber-attacks have grown in to the ``intelligent'' cyber attacks. In this paper, The most promising artificial intelligence approaches are discussed. Researchers look at how such techniques may be used for cyber security. At last, the conversation concludes with a discussion about artificial intelligence's future and cyber security.
Al-Ameer, Ali, AL-Sunni, Fouad.  2021.  A Methodology for Securities and Cryptocurrency Trading Using Exploratory Data Analysis and Artificial Intelligence. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). :54–61.
This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Na\"ıve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.
Ren, Sothearin, Kim, Jae-Sung, Cho, Wan-Sup, Soeng, Saravit, Kong, Sovanreach, Lee, Kyung-Hee.  2021.  Big Data Platform for Intelligence Industrial IoT Sensor Monitoring System Based on Edge Computing and AI. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :480–482.
The cutting edge of Industry 4.0 has driven everything to be converted to disruptive innovation and digitalized. This digital revolution is imprinted by modern and advanced technology that takes advantage of Big Data and Artificial Intelligence (AI) to nurture from automatic learning systems, smart city, smart energy, smart factory to the edge computing technology, and so on. To harness an appealing, noteworthy, and leading development in smart manufacturing industry, the modern industrial sciences and technologies such as Big Data, Artificial Intelligence, Internet of things, and Edge Computing have to be integrated cooperatively. Accordingly, a suggestion on the integration is presented in this paper. This proposed paper describes the design and implementation of big data platform for intelligence industrial internet of things sensor monitoring system and conveys a prediction of any upcoming errors beforehand. The architecture design is based on edge computing and artificial intelligence. To extend more precisely, industrial internet of things sensor here is about the condition monitoring sensor data - vibration, temperature, related humidity, and barometric pressure inside facility manufacturing factory.
Alamaniotis, Miltiadis.  2021.  Fuzzy Integration of Kernel-Based Gaussian Processes Applied to Anomaly Detection in Nuclear Security. 2021 12th International Conference on Information, Intelligence, Systems Applications (IISA). :1–4.
Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.
Hu, Guangjun, Li, Haiwei, Li, Kun, Wang, Rui.  2021.  A Network Asset Detection Scheme Based on Website Icon Intelligent Identification. 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). :255–257.
With the rapid development of the Internet and communication technologies, efficient management of cyberspace, safe monitoring and protection of various network assets can effectively improve the overall level of network security protection. Accurate, effective and comprehensive network asset detection is the prerequisite for effective network asset management, and it is also the basis for security monitoring and analysis. This paper proposed an artificial intelligence algorithm based scheme which accurately identify the website icon and help to determine the ownership of network assets. Through experiments based on data set collected from real network, the result demonstrate that the proposed scheme has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.
Kalinin, Maxim O., Krundyshev, Vasiliy M..  2021.  Computational Intelligence Technologies Stack for Protecting the Critical Digital Infrastructures against Security Intrusions. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :118–122.
Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.
Li, Yanjie.  2021.  The Application Analysis of Artificial Intelligence in Computer Network Technology. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). :1126–1129.
In the information age, computer network technology has covered different areas of social life and involved various fields, and artificial intelligence, as an emerging technology with a very rapid development momentum in recent years, is important in promoting the development of computer network systems. This article explains the concept of artificial intelligence technology, describes the problems faced by computer networks, further analyses the advantages of artificial intelligence and the inevitability of application in network technology, and then studies the application of artificial intelligence in computer network technology.
Vast, Rahul, Sawant, Shruti, Thorbole, Aishwarya, Badgujar, Vishal.  2021.  Artificial Intelligence Based Security Orchestration, Automation and Response System. 2021 6th International Conference for Convergence in Technology (I2CT). :1–5.
Cybersecurity is becoming very crucial in the today's world where technology is now not limited to just computers, smartphones, etc. It is slowly entering into things that are used on daily basis like home appliances, automobiles, etc. Thus, opening a new door for people with wrong intent. With the increase in speed of technology dealing with such issues also requires quick response from security people. Thus, dealing with huge variety of devices quickly will require some extent of automation in this field. Generating threat intelligence automatically and also including those which are multilingual will also add plus point to prevent well known major attacks. Here we are proposing an AI based SOAR system in which the data from various sources like firewalls, IDS, etc. is collected with individual event profiling using a deep-learning detection method. For this the very first step is that the collected data from different sources will be converted into a standardized format i.e. to categorize the data collected from different sources. For standardized format Here our system finds out about the true positive alert for which the appropriate/ needful steps will be taken such as the generation of Indicators of Compromise report and the additional evidences with the help of Security Information and Event Management system. The security alerts will be notified to the security teams with the degree of threat.
He, Zewei.  2021.  Communication Engineering Application System Based on Artificial Intelligence Technology. 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA). :366–369.
In order to overcome a series of problems in the application process of traditional communication engineering in the new era, such as information security, this paper proposes a novel communication engineering application system based on artificial intelligence technology. The application system fully combines the artificial intelligence technology, and applies the artificial intelligence thinking to the reform of traditional communication engineering. Based on this, the application strategy also fully combines the application and development of 5g technology, and strengthens the security of communication engineering in the application process from many aspects. The results show that the application system can give full play to the role of artificial intelligence technology and improve the security of communication process as much as possible, which lays a good foundation for the further development of 5g technology.
Xu, Ling.  2021.  Application of Artificial Intelligence and Big Data in the Security of Regulatory Places. 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). :210–213.
This paper analyzes the necessity of artificial intelligence and big data in the security application of regulatory places. The author studies the specific application of artificial intelligence and big data in ideological dynamics management, access control system, video surveillance system, emergency alarm system, perimeter control system, police inspection system, daily behavior management, and system implementation management. The author puts forward how to do technical integration, improve information sharing, strengthen the construction of talents, and increase management fund expenditure. The purpose of this paper is to enhance the security management level of regulatory places and optimize the management environment of regulatory places.
Stan, Orly, Bitton, Ron, Ezrets, Michal, Dadon, Moran, Inokuchi, Masaki, Ohta, Yoshinobu, Yagyu, Tomohiko, Elovici, Yuval, Shabtai, Asaf.  2021.  Heuristic Approach for Countermeasure Selection Using Attack Graphs. 2021 IEEE 34th Computer Security Foundations Symposium (CSF). :1–16.
Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.
Bardhan, Shuvo, Battou, Abdella.  2021.  Security Metric for Networks with Intrusion Detection Systems having Time Latency using Attack Graphs. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :1107–1113.
Probabilistic security metrics estimate the vulnerability of a network in terms of the likelihood of an attacker reaching the goal states (of a network) by exploiting the attack graph paths. The probability computation depends upon several assumptions regarding the possible attack scenarios. In this paper, we extend the existing security metric to model networks with intrusion detection systems and their associated uncertainties and time latencies. We consider learning capabilities of attackers as well as detection systems. Estimation of risk is obtained by using the attack paths that are undetectable owing to the latency of the detection system. Thus, we define the overall vulnerability (of a network) as a function of the time window available to an attacker for repeated exploring (via learning) and exploitation of a network, before the attack is mitigated by the detection system. Finally, we consider the realistic scenario where an attacker explores and abandons various partial paths in the attack graph before the actual exploitation. A dynamic programming formulation of the vulnerability computation methodology is proposed for this scenario. The nature of these metrics are explained using a case study showing the vulnerability spectrum from the case of zero detection latency to a no detection scenario.
Wang, Wenhui, Han, Longxi, Ge, Guangkai, Yang, Zhenghao.  2021.  An Algorithm of Optimal Penetration Path Generation under Unknown Attacks of Electric Power WEB System Based on Knowledge Graph. 2021 2nd International Conference on Computer Communication and Network Security (CCNS). :141–144.
Aiming at the disadvantages of traditional methods such as low penetration path generation efficiency and low attack type recognition accuracy, an optimal penetration path generation algorithm based on the knowledge map power WEB system unknown attack is proposed. First, establish a minimum penetration path test model. And use the model to test the unknown attack of the penetration path under the power WEB system. Then, the ontology of the knowledge graph is designed. Finally, the design of the optimal penetration path generation algorithm based on the knowledge graph is completed. Experimental results show that the algorithm improves the efficiency of optimal penetration path generation, overcomes the shortcomings of traditional methods that can only describe known attacks, and can effectively guarantee the security of power WEB systems.
Ibrahim, Mariam, Nabulsi, Intisar.  2021.  Security Analysis of Smart Home Systems Applying Attack Graph. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :230–234.
In this work, security analysis of a Smart Home System (SHS) is inspected. The paper focuses on describing common and likely cyber security threats against SHS. This includes both their influence on human privacy and safety. The SHS is properly presented and formed applying Architecture Analysis and Design Language (AADL), exhibiting the system layout, weaknesses, attack practices, besides their requirements and post settings. The obtained model is later inspected along with a security requirement with JKind model tester software for security endangerment. The overall attack graph causing system compromise is graphically given using Graphviz.
Sahu, Abhijeet, Davis, Katherine.  2021.  Structural Learning Techniques for Bayesian Attack Graphs in Cyber Physical Power Systems. 2021 IEEE Texas Power and Energy Conference (TPEC). :1–6.
Updating the structure of attack graph templates based on real-time alerts from Intrusion Detection Systems (IDS), in an Industrial Control System (ICS) network, is currently done manually by security experts. But, a highly-connected smart power systems, that can inadvertently expose numerous vulnerabilities to intruders for targeting grid resilience, needs automatic fast updates on learning attack graph structures, instead of manual intervention, to enable fast isolation of compromised network to secure the grid. Hence, in this work, we develop a technique to first construct a prior Bayesian Attack Graph (BAG) based on a predefined threat model and a synthetic communication network for a cyber-physical power system. Further, we evaluate a few score-based and constraint-based structural learning algorithms to update the BAG structure based on real-time alerts, based on scalability, data dependency, time complexity and accuracy criteria.
Roy, Kashob Kumar, Roy, Amit, Mahbubur Rahman, A K M, Amin, M Ashraful, Ali, Amin Ahsan.  2021.  Structure-Aware Hierarchical Graph Pooling using Information Bottleneck. 2021 International Joint Conference on Neural Networks (IJCNN). :1–8.
Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks. For these tasks, different pooling strategies have been proposed to generate a graph-level representation by downsampling and summarizing nodes' features in a graph. However, most existing pooling methods are unable to capture distinguishable structural information effectively. Besides, they are prone to adversarial attacks. In this work, we propose a novel pooling method named as HIBPool where we leverage the Information Bottleneck (IB) principle that optimally balances the expressiveness and robustness of a model to learn representations of input data. Furthermore, we introduce a novel structure-aware Discriminative Pooling Readout (DiP-Readout) function to capture the informative local subgraph structures in the graph. Finally, our experimental results show that our model significantly outperforms other state-of-art methods on several graph classification benchmarks and more resilient to feature-perturbation attack than existing pooling methods11Source code at:
Ngo, Quoc-Dung, Nguyen, Huy-Trung, Nguyen, Viet-Dung, Dinh, Cong-Minh, Phung, Anh-Tu, Bui, Quy-Tung.  2021.  Adversarial Attack and Defense on Graph-based IoT Botnet Detection Approach. 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). :1–6.
To reduce the risk of botnet malware, methods of detecting botnet malware using machine learning have received enormous attention in recent years. Most of the traditional methods are based on supervised learning that relies on static features with defined labels. However, recent studies show that supervised machine learning-based IoT malware botnet models are more vulnerable to intentional attacks, known as an adversarial attack. In this paper, we study the adversarial attack on PSI-graph based researches. To perform the efficient attack, we proposed a reinforcement learning based method with a trained target classifier to modify the structures of PSI-graphs. We show that PSI-graphs are vulnerable to such attack. We also discuss about defense method which uses adversarial training to train a defensive model. Experiment result achieves 94.1% accuracy on the adversarial dataset; thus, shows that our defensive model is much more robust than the previous target classifier.
Thomas, Diya.  2021.  A Graph-based Approach to Detect DoB Attack. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). :422–423.
Wireless sensor networks (WSNs) are underlying network infrastructure for a variety of surveillance applications. The network should be tolerant of unexpected failures of sensor nodes to meet the Quality of Service (QoS) requirements of these applications. One major cause of failure is active security attacks such as Depletion-of-Battery (DoB) attacks. This paper model the problem of detecting such attacks as an anomaly detection problem in a dynamic graph. The problem is addressed by employing a cluster ensemble approach called the K-Means Spectral and Hierarchical ensemble (KSH) approach. The experimental result shows that KSH detected DoB attacks with better accuracy when compared to baseline approaches.
Guan, Xiaojuan, Ma, Yuanyuan, SHAO, Zhipeng, Cao, Wantian.  2021.  Research on Key Node Method of Network Attack Graph Based on Power Information Physical System. 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC)2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC). :48–51.
With the increasing scale of network, the scale of attack graph has been becoming larger and larger, and the number of nodes in attack graph is also increasing, which can not directly reflect the impact of nodes on the whole system. Therefore, in this paper, a method was proposed to determine the key nodes of network attack graph of power information physical system to solve the problem of uncertain emphasis of security protection of attack graph.
Goncharov, V. V., Goncharov, A. V., Shavrin, S. S., Shishova, N. A..  2021.  The Cyber Attack on the Corporate Network Models Theoretical Aspects. 2021 Systems of Signals Generating and Processing in the Field of on Board Communications. :1–4.
Mathematical model of web server protection is being proposed based on filtering HTTP (Hypertext Transfer Protocol) packets that do not match the semantic parameters of the request standards of this protocol. The model is defined as a graph, and the relationship between the parameters - the sets of vulnerabilities of the corporate network, the methods of attacks and their consequences-is described by the Cartesian product, which provides the correct interpretation of a corporate network cyber attack. To represent the individual stages of simulated attacks, it is possible to separate graph models in order to model more complex attacks based on the existing simplest ones. The unity of the model proposed representation of cyber attack in three variants is shown, namely: graphic, text and formula.
Ahn, Bohyun, Bere, Gomanth, Ahmad, Seerin, Choi, JinChun, Kim, Taesic, Park, Sung-won.  2021.  Blockchain-Enabled Security Module for Transforming Conventional Inverters toward Firmware Security-Enhanced Smart Inverters. 2021 IEEE Energy Conversion Congress and Exposition (ECCE). :1307–1312.
As the traditional inverters are transforming toward more intelligent inverters with advanced information and communication technologies, the cyber-attack surface has been remarkably expanded. Specifically, securing firmware of smart inverters from cyber-attacks is crucial. This paper provides expanded firmware attack surface targeting smart inverters. Moreover, this paper proposes a security module for transforming a conventional inverter to a firmware security built-in smart inverter by preventing potential malware and unauthorized firmware update attacks as well as fast automated inverter recovery from zero-day attacks. Furthermore, the proposed security module as a client of blockchain is connected to blockchain severs to fully utilize blockchain technologies such as membership service, ledgers, and smart contracts to detect and mitigate the firmware attacks. The proposed security module framework is implemented in an Internet-of-Thing (IoT) device and validated by experiments.
Maliszewski, Michal, Boryczka, Urszula.  2021.  Using MajorClust Algorithm for Sandbox-Based ATM Security. 2021 IEEE Congress on Evolutionary Computation (CEC). :1054–1061.
Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.
He, Zhangying, Miari, Tahereh, Makrani, Hosein Mohammadi, Aliasgari, Mehrdad, Homayoun, Houman, Sayadi, Hossein.  2021.  When Machine Learning Meets Hardware Cybersecurity: Delving into Accurate Zero-Day Malware Detection. 2021 22nd International Symposium on Quality Electronic Design (ISQED). :85–90.
Cybersecurity for the past decades has been in the front line of global attention as a critical threat to the information technology infrastructures. According to recent security reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers as well as harmful purposes to compromise security of computing systems. To address the high complexity and computational overheads of conventional software-based detection techniques, Hardware-Supported Malware Detection (HMD) has proved to be efficient for detecting malware at the processors' microarchitecture level with the aid of Machine Learning (ML) techniques applied on Hardware Performance Counter (HPC) data. Existing ML-based HMDs while accurate in recognizing known signatures of malicious patterns, have not explored detecting unknown (zero-day) malware data at run-time which is a more challenging problem, since its HPC data does not match any known attack applications' signatures in the existing database. In this work, we first present a review of recent ML-based HMDs utilizing built-in HPC registers information. Next, we examine the suitability of various standard ML classifiers for zero-day malware detection and demonstrate that such methods are not capable of detecting unknown malware signatures with high detection rate. Lastly, to address the challenge of run-time zero-day malware detection, we propose an ensemble learning-based technique to enhance the performance of the standard malware detectors despite using a small number of microarchitectural features that are captured at run-time by existing HPCs. The experimental results demonstrate that our proposed approach by applying AdaBoost ensemble learning on Random Forrest classifier as a regular classifier achieves 92% F-measure and 95% TPR with only 2% false positive rate in detecting zero-day malware using only the top 4 microarchitectural features.
Li, Yan, Lu, Yifei, Li, Shuren.  2021.  EZAC: Encrypted Zero-Day Applications Classification Using CNN and K-Means. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :378–383.
With the rapid development of traffic encryption technology and the continuous emergence of various network services, the classification of encrypted zero-day applications has become a major challenge in network supervision. More seriously, many attackers will utilize zero-day applications to hide their attack behaviors and make attack undetectable. However, there are very few existing studies on zero-day applications. Existing works usually select and label zero-day applications from unlabeled datasets, and these are not true zero-day applications classification. To address the classification of zero-day applications, this paper proposes an Encrypted Zero-day Applications Classification (EZAC) method that combines Convolutional Neural Network (CNN) and K-Means, which can effectively classify zero-day applications. We first use CNN to classify the flows, and for the flows that may be zero-day applications, we use K-Means to divide them into several categories, which are then manually labeled. Experimental results show that the EZAC achieves 97.4% accuracy on a public dataset (CIC-Darknet2020), which outperforms the state-of-the-art methods.
Mishra, Srinivas, Pradhan, Sateesh Kumar, Rath, Subhendu Kumar.  2021.  Detection of Zero-Day Attacks in Network IDS through High Performance Soft Computing. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). :1199–1204.
The ever-evolving computers has its implications on the data and information and the threats that they are exposed to. With the exponential growth of internet, the chances of data breach are highly likely as unauthorized and ill minded users find new ways to get access to the data that they can use for their plans. Most of the systems today have well designed measures that examine the information for any abnormal behavior (Zero Day Attacks) compared to what has been seen and experienced over the years. These checks are done based on a predefined identity (signature) of information. This is being termed as Intrusion Detection Systems (IDS). The concept of IDS revolves around validation of data and/or information and detecting unauthorized access attempts with an intention of manipulating data. High Performance Soft Computing (HPSC) aims to internalize cumulative adoption of traditional and modern attempts to breach data security and expose it to high scale damage and altercations. Our effort in this paper is to emphasize on the multifaceted tactic and rationalize important functionalities of IDS available at the disposal of HPSC.