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2022-01-10
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.
2021-05-13
Li, Mingxuan, Yang, Zhushi, Zhong, Jinsong, He, Ling, Teng, Yangxin.  2020.  Research on Network Attack and Defense Based on Artificial Intelligence Technology. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2532—2534.
This paper combines the common ideas and methods in offensive and defensive confrontation in recent years, and uses artificial intelligence technology-based network asset automatic mining technology and artificial intelligence technology-based vulnerability automatic exploitation technology, carries out research and specific practices in discovering and using system vulnerability based on artificial intelligence technology, designs and implemented automatic binary vulnerability discovering and exploitation system, which improves improves the efficiency and success rate of vulnerability discovering and exploitation.
Zhao, Haining, Chen, Liquan.  2020.  Artificial Intelligence Security Issues and Responses. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :2276—2283.
As a current disruptive and transformative technology, artificial intelligence is constantly infiltrating all aspects of production and life. However, with the in-depth development and application of artificial intelligence, the security challenges it faces have become more and more prominent. In the real world, attacks against intelligent systems such as the Internet of Things, smart homes, and driverless cars are constantly appearing, and incidents of artificial intelligence being used in cyber-attacks and cybercrimes frequently occur. This article aims to discuss artificial intelligence security issues and propose some countermeasures.
Jain, Harsh, Vikram, Aditya, Mohana, Kashyap, Ankit, Jain, Ayush.  2020.  Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). :193—198.
Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.
Feng, Xiaohua, Feng, Yunzhong, Dawam, Edward Swarlat.  2020.  Artificial Intelligence Cyber Security Strategy. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :328—333.
Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments' strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.
Wenhui, Sun, Kejin, Wang, Aichun, Zhu.  2020.  The Development of Artificial Intelligence Technology And Its Application in Communication Security. 2020 International Conference on Computer Engineering and Application (ICCEA). :752—756.
Artificial intelligence has been widely used in industries such as smart manufacturing, medical care and home furnishings. Among them, the value of the application in communication security is very important. This paper makes a further exploration of the artificial intelligence technology and its application, and gives a detailed analysis of its development, standardization and the application.
Ho, Tsung-Yu, Chen, Wei-An, Huang, Chiung-Ying.  2020.  The Burden of Artificial Intelligence on Internal Security Detection. 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET). :148—150.
Our research team have devoted to extract internal malicious behavior by monitoring the network traffic for many years. We applied the deep learning approach to recognize the malicious patterns within network, but this methodology may lead to more works to examine the results from AI models production. Hence, this paper addressed the scenario to consider the burden of AI, and proposed an idea for long-term reliable detection in the future work.
Shu, Fei, Chen, Shuting, Li, Feng, Zhang, JianYe, Chen, Jia.  2020.  Research and implementation of network attack and defense countermeasure technology based on artificial intelligence technology. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). :475—478.
Using artificial intelligence technology to help network security has become a major trend. At present, major countries in the world have successively invested R & D force in the attack and defense of automatic network based on artificial intelligence. The U.S. Navy, the U.S. air force, and the DOD strategic capabilities office have invested heavily in the development of artificial intelligence network defense systems. DARPA launched the network security challenge (CGC) to promote the development of automatic attack system based on artificial intelligence. In the 2016 Defcon final, mayhem (the champion of CGC in 2014), an automatic attack team, participated in the competition with 14 human teams and once defeated two human teams, indicating that the automatic attack method generated by artificial intelligence system can scan system defects and find loopholes faster and more effectively than human beings. Japan's defense ministry also announced recently that in order to strengthen the ability to respond to network attacks, it will introduce artificial intelligence technology into the information communication network defense system of Japan's self defense force. It can be predicted that the deepening application of artificial intelligence in the field of network attack and defense may bring about revolutionary changes and increase the imbalance of the strategic strength of cyberspace in various countries. Therefore, it is necessary to systematically investigate the current situation of network attack and defense based on artificial intelligence at home and abroad, comprehensively analyze the development trend of relevant technologies at home and abroad, deeply analyze the development outline and specification of artificial intelligence attack and defense around the world, and refine the application status and future prospects of artificial intelligence attack and defense, so as to promote the development of artificial intelligence attack and Defense Technology in China and protect the core interests of cyberspace, of great significance
Hu, Xiaoyi, Wang, Ke.  2020.  Bank Financial Innovation and Computer Information Security Management Based on Artificial Intelligence. 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). :572—575.
In recent years, with the continuous development of various new Internet technologies, big data, cloud computing and other technologies have been widely used in work and life. The further improvement of data scale and computing capability has promoted the breakthrough development of artificial intelligence technology. The generalization and classification of financial science and technology not only have a certain impact on the traditional financial business, but also put forward higher requirements for commercial banks to operate financial science and technology business. Artificial intelligence brings fresh experience to financial services and is conducive to increasing customer stickiness. Artificial intelligence technology helps the standardization, modeling and intelligence of banking business, and helps credit decision-making, risk early warning and supervision. This paper first discusses the influence of artificial intelligence on financial innovation, and on this basis puts forward measures for the innovation and development of bank financial science and technology. Finally, it discusses the problem of computer information security management in bank financial innovation in the era of artificial intelligence.
Zhang, Yunxiang, Rao, Zhuyi.  2020.  Research on Information Security Evaluation Based on Artificial Neural Network. 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :424–428.

In order to improve the information security ability of the network information platform, the information security evaluation method is proposed based on artificial neural network. Based on the comprehensive analysis of the security events in the construction of the network information platform, the risk assessment model of the network information platform is constructed based on the artificial neural network theory. The weight calculation algorithm of artificial neural network and the minimum artificial neural network pruning algorithm are also given, which can realize the quantitative evaluation of network information security. The fuzzy neural network weighted control method is used to control the information security, and the non-recursive traversal method is adopted to realize the adaptive training of information security assessment process. The adaptive learning of the artificial neural network is carried out according to the conditions, and the ability of information encryption and transmission is improved. The information security assessment is realized. The simulation results show that the method is accurate and ensures the information security.

2021-03-01
Kuppa, A., Le-Khac, N.-A..  2020.  Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.

Cybersecurity community is slowly leveraging Machine Learning (ML) to combat ever evolving threats. One of the biggest drivers for successful adoption of these models is how well domain experts and users are able to understand and trust their functionality. As these black-box models are being employed to make important predictions, the demand for transparency and explainability is increasing from the stakeholders.Explanations supporting the output of ML models are crucial in cyber security, where experts require far more information from the model than a simple binary output for their analysis. Recent approaches in the literature have focused on three different areas: (a) creating and improving explainability methods which help users better understand the internal workings of ML models and their outputs; (b) attacks on interpreters in white box setting; (c) defining the exact properties and metrics of the explanations generated by models. However, they have not covered, the security properties and threat models relevant to cybersecurity domain, and attacks on explainable models in black box settings.In this paper, we bridge this gap by proposing a taxonomy for Explainable Artificial Intelligence (XAI) methods, covering various security properties and threat models relevant to cyber security domain. We design a novel black box attack for analyzing the consistency, correctness and confidence security properties of gradient based XAI methods. We validate our proposed system on 3 security-relevant data-sets and models, and demonstrate that the method achieves attacker's goal of misleading both the classifier and explanation report and, only explainability method without affecting the classifier output. Our evaluation of the proposed approach shows promising results and can help in designing secure and robust XAI methods.

2020-09-04
Jing, Huiyun, Meng, Chengrui, He, Xin, Wei, Wei.  2019.  Black Box Explanation Guided Decision-Based Adversarial Attacks. 2019 IEEE 5th International Conference on Computer and Communications (ICCC). :1592—1596.
Adversarial attacks have been the hot research field in artificial intelligence security. Decision-based black-box adversarial attacks are much more appropriate in the real-world scenarios, where only the final decisions of the targeted deep neural networks are accessible. However, since there is no available guidance for searching the imperceptive adversarial perturbation, boundary attack, one of the best performing decision-based black-box attacks, carries out computationally expensive search. For improving attack efficiency, we propose a novel black box explanation guided decision-based black-box adversarial attack. Firstly, the problem of decision-based adversarial attacks is modeled as a derivative-free and constraint optimization problem. To solve this optimization problem, the black box explanation guided constrained random search method is proposed to more quickly find the imperceptible adversarial example. The insights into the targeted deep neural networks explored by the black box explanation are fully used to accelerate the computationally expensive random search. Experimental results demonstrate that our proposed attack improves the attack efficiency by 64% compared with boundary attack.
2020-08-13
Zhang, Yueqian, Kantarci, Burak.  2019.  Invited Paper: AI-Based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). :17—1709.
Mobile crowdsensing (MCS) is a distributed sensing paradigm that uses a variety of built-in sensors in smart mobile devices to enable ubiquitous acquisition of sensory data from surroundings. However, non-dedicated nature of MCS results in vulnerabilities in the presence of malicious participants to compromise the availability of the MCS components, particularly the servers and participants' devices. In this paper, we focus on Denial of Service attacks in MCS where malicious participants submit illegitimate task requests to the MCS platform to keep MCS servers busy while having sensing devices expend energy needlessly. After reviewing Artificial Intelligence-based security solutions for MCS systems, we focus on a typical location-based and energy-oriented DoS attack, and present a security solution that applies ensemble techniques in machine learning to identify illegitimate tasks and prevent personal devices from pointless energy consumption so as to improve the availability of the whole system. Through simulations, we show that ensemble techniques are capable of identifying illegitimate and legitimate tasks while gradient boosting appears to be a preferable solution with an AUC performance higher than 0.88 in the precision-recall curve. We also investigate the impact of environmental settings on the detection performance so as to provide a clearer understanding of the model. Our performance results show that MCS task legitimacy decisions with high F-scores are possible for both illegitimate and legitimate tasks.
Augusto, Cristian, Morán, Jesús, De La Riva, Claudio, Tuya, Javier.  2019.  Test-Driven Anonymization for Artificial Intelligence. 2019 IEEE International Conference On Artificial Intelligence Testing (AITest). :103—110.
In recent years, data published and shared with third parties to develop artificial intelligence (AI) tools and services has significantly increased. When there are regulatory or internal requirements regarding privacy of data, anonymization techniques are used to maintain privacy by transforming the data. The side-effect is that the anonymization may lead to useless data to train and test the AI because it is highly dependent on the quality of the data. To overcome this problem, we propose a test-driven anonymization approach for artificial intelligence tools. The approach tests different anonymization efforts to achieve a trade-off in terms of privacy (non-functional quality) and functional suitability of the artificial intelligence technique (functional quality). The approach has been validated by means of two real-life datasets in the domains of healthcare and health insurance. Each of these datasets is anonymized with several privacy protections and then used to train classification AIs. The results show how we can anonymize the data to achieve an adequate functional suitability in the AI context while maintaining the privacy of the anonymized data as high as possible.
Wang, Tianyi, Chow, Kam Pui.  2019.  Automatic Tagging of Cyber Threat Intelligence Unstructured Data using Semantics Extraction. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). :197—199.
Threat intelligence, information about potential or current attacks to an organization, is an important component in cyber security territory. As new threats consecutively occurring, cyber security professionals always keep an eye on the latest threat intelligence in order to continuously lower the security risks for their organizations. Cyber threat intelligence is usually conveyed by structured data like CVE entities and unstructured data like articles and reports. Structured data are always under certain patterns that can be easily analyzed, while unstructured data have more difficulties to find fixed patterns to analyze. There exists plenty of methods and algorithms on information extraction from structured data, but no current work is complete or suitable for semantics extraction upon unstructured cyber threat intelligence data. In this paper, we introduce an idea of automatic tagging applying JAPE feature within GATE framework to perform semantics extraction upon cyber threat intelligence unstructured data such as articles and reports. We extract token entities from each cyber threat intelligence article or report and evaluate the usefulness of them. A threat intelligence ontology then can be constructed with the useful entities extracted from related resources and provide convenience for professionals to find latest useful threat intelligence they need.
Sadeghi, Koosha, Banerjee, Ayan, Gupta, Sandeep K. S..  2019.  An Analytical Framework for Security-Tuning of Artificial Intelligence Applications Under Attack. 2019 IEEE International Conference On Artificial Intelligence Testing (AITest). :111—118.
Machine Learning (ML) algorithms, as the core technology in Artificial Intelligence (AI) applications, such as self-driving vehicles, make important decisions by performing a variety of data classification or prediction tasks. Attacks on data or algorithms in AI applications can lead to misclassification or misprediction, which can fail the applications. For each dataset separately, the parameters of ML algorithms should be tuned to reach a desirable classification or prediction accuracy. Typically, ML experts tune the parameters empirically, which can be time consuming and does not guarantee the optimal result. To this end, some research suggests an analytical approach to tune the ML parameters for maximum accuracy. However, none of the works consider the ML performance under attack in their tuning process. This paper proposes an analytical framework for tuning the ML parameters to be secure against attacks, while keeping its accuracy high. The framework finds the optimal set of parameters by defining a novel objective function, which takes into account the test results of both ML accuracy and its security against attacks. For validating the framework, an AI application is implemented to recognize whether a subject's eyes are open or closed, by applying k-Nearest Neighbors (kNN) algorithm on her Electroencephalogram (EEG) signals. In this application, the number of neighbors (k) and the distance metric type, as the two main parameters of kNN, are chosen for tuning. The input data perturbation attack, as one of the most common attacks on ML algorithms, is used for testing the security of the application. Exhaustive search approach is used to solve the optimization problem. The experiment results show k = 43 and cosine distance metric is the optimal configuration of kNN for the EEG dataset, which leads to 83.75% classification accuracy and reduces the attack success rate to 5.21%.