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2023-03-17
Dash, Lipsa, Sharma, Sanjeev, M, Manish, M, Chaitanya, P, Vamsi Krishna, Manna, Souvik.  2022.  Comparative Analysis of Secured Transport Systems using RFID Technology for Schools. 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). :1–6.
Despite the strict measures taken by authorities for children safety, crime against children is increasing. To curb this crime, it is important to improve the safety of children. School authorities can be severely penalized for these incidents, hence monitoring the school bus is significantly important in limiting these incidents. The developing worry of families for the security and insurance of their kids has started incredible interest in creating strong frameworks that give successful following and oversight of kids driving among home and school. Coordinated transport following permits youngsters to partake more in their normal schoolwork longer than trusting that a transport will be late with the assistance of notice and guarantees the security of every understudy. These days, reacting to the necessities existing apart from everything else, numerous instructive foundations have begun to push more towards a compelling global positioning framework of their vehicles that ensures the wellbeing of their understudies. Effective transport following is accomplished by procuring the geographic directions utilizing the GPS module and communicating the informationto a distant server. The framework depends on prepared to-utilize inactive RFID peruses. Make a message pop-up from the server script subsequent to checking the understudy's RFID tag be. The RFID examine exhibiting that the understudy boarded the vehicle to the specific trained professionals and the parent. Successful transport following permits school specialists, guardians, and drivers to precisely design their schedules while protecting kids from the second they get on until they get off the transport. The framework overall makes it conceivable to educate the administration regarding crises or protests. A variety of reports can be generated for different school-wide real-time bus and vehicle activities. This paper reviews the various smart security transport systems proposed for providing security features.
2022-04-25
Nawaz, Alia, Naeem, Tariq, Tayyab, Muhammad.  2021.  Application Profiling From Encrypted Traffic. 2021 International Conference on Cyber Warfare and Security (ICCWS). :1–7.
Everyday millions of people use Internet for various purposes including information access, communication, business, education, entertainment and more. As a result, huge amount of information is exchanged between billions of connected devices. This information can be encapsulated in different types of data packets. This information is also referred to as network traffic. The traffic analysis is a challenging task when the traffic is encrypted and the contents are not readable. So complex algorithms required to deduce the information and form patterns for traffic analysis. Many of currently available techniques rely on application specific attribute analysis, deep packet inspection (DPI) or content-based analysis that become ineffective on encrypted traffic. The article will focused on analysis techniques for encrypted traffic that are adaptive to address the evolving nature and increasing volume of network traffic. The proposed solution solution is less dependent on application and protocol specific parameters so that it can adapt to new types of applications and protocols. Our results shows that processing required for traffic analysis need to be in acceptable limits to ensure applicability in real-time applications without compromising performance.
2022-03-14
Aldossary, Lina Abdulaziz, Ali, Mazen, Alasaadi, Abdulla.  2021.  Securing SCADA Systems against Cyber-Attacks using Artificial Intelligence. 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). :739—745.
Monitoring and managing electric power generation, distribution and transmission requires supervisory control and data acquisition (SCADA) systems. As technology has developed, these systems have become huge, complicated, and distributed, which makes them susceptible to new risks. In particular, the lack of security in SCADA systems make them a target for network attacks such as denial of service (DoS) and developing solutions for this issue is the main objective of this thesis. By reviewing various existing system solutions for securing SCADA systems, a new security approach is recommended that employs Artificial Intelligence(AI). AI is an innovative approach that imparts learning ability to software. Here deep learning algorithms and machine learning algorithms are used to develop an intrusion detection system (IDS) to combat cyber-attacks. Various methods and algorithms are evaluated to obtain the best results in intrusion detection. The results reveal the Bi-LSTM IDS technique provides the highest intrusion detection (ID) performance compared with previous techniques to secure SCADA systems
2021-01-11
Bhat, P., Batakurki, M., Chari, M..  2020.  Classifier with Deep Deviation Detection in PoE-IoT Devices. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). :1–3.
With the rapid growth in diversity of PoE-IoT devices and concept of "Edge intelligence", PoE-IoT security and behavior analysis is the major concern. These PoE-IoT devices lack visibility when the entire network infrastructure is taken into account. The IoT devices are prone to have design faults in their security capabilities. The entire network may be put to risk by attacks on vulnerable IoT devices or malware might get introduced into IoT devices even by routine operations such as firmware upgrade. There have been various approaches based on machine learning(ML) to classify PoE-IoT devices based on network traffic characteristics such as Deep Packet Inspection(DPI). In this paper, we propose a novel method for PoE-IoT classification where ML algorithm, Decision Tree is used. In addition to classification, this method provides useful insights to the network deployment, based on the deviations detected. These insights can further be used for shaping policies, troubleshooting and behavior analysis of PoE-IoT devices.
2020-12-01
Yang, R., Ouyang, X., Chen, Y., Townend, P., Xu, J..  2018.  Intelligent Resource Scheduling at Scale: A Machine Learning Perspective. 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE). :132—141.

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement.

2020-06-12
Hughes, Ben, Bothe, Shruti, Farooq, Hasan, Imran, Ali.  2019.  Generative Adversarial Learning for Machine Learning empowered Self Organizing 5G Networks. 2019 International Conference on Computing, Networking and Communications (ICNC). :282—286.

In the wake of diversity of service requirements and increasing push for extreme efficiency, adaptability propelled by machine learning (ML) a.k.a self organizing networks (SON) is emerging as an inevitable design feature for future mobile 5G networks. The implementation of SON with ML as a foundation requires significant amounts of real labeled sample data for the networks to train on, with high correlation between the amount of sample data and the effectiveness of the SON algorithm. As generally real labeled data is scarce therefore it can become bottleneck for ML empowered SON for unleashing their true potential. In this work, we propose a method of expanding these sample data sets using Generative Adversarial Networks (GANs), which are based on two interconnected deep artificial neural networks. This method is an alternative to taking more data to expand the sample set, preferred in cases where taking more data is not simple, feasible, or efficient. We demonstrate how the method can generate large amounts of realistic synthetic data, utilizing the GAN's ability of generation and discrimination, able to be easily added to the sample set. This method is, as an example, implemented with Call Data Records (CDRs) containing the start hour of a call and the duration of the call, in minutes taken from a real mobile operator. Results show that the method can be used with a relatively small sample set and little information about the statistics of the true CDRs and still make accurate synthetic ones.