Cyber-attack on synchrophasor data has become a widely explored area. However, GPS-spoofing and FDIA attacks require different responsive actions. State-estimation based attack detection method works similar way for both types of attacks. It implies that using state-estimation based detection alone doesn’t give the control center enough information about the attack type. This scenario is specifically more critical for those attack detection methods which consider GPS-spoofing attack as another FDIA with falsified phase angle data. Since identifying correct attack type is paramount, we have attempted to develop an algorithm to distinguish these two attacks. Previous researchers exploited low-rank approximation of Hankel Matrix to differentiate between FDIA and physical events. We have demonstrated that, together with angle unwrapping algorithm, low-rank approximation of Hankel Matrix can help us separating GPS-spoofing attack with FDIA. The proposed method is verified with simulation result. It has been demonstrated that the GSA with 3 second time-shift creates a low-rank approximation error 700% higher than that of normal condition, whereas FDIA doesn’t produce any significant change in low-rank approximation error from that of normal condition. Finally, we have proposed a real-time method for successful identification of event, FDIA and GSA. / M.S. / Cyber-attack on synchrophasor data has become a widely explored area. However, GPS-spoofing and FDIA attacks require different responsive actions. State-estimation based attack detection method works similar way for both types of attacks. It implies that using state-estimation based detection alone doesn’t give the control center enough information about the attack type. This scenario is specifically more critical for those attack detection methods which consider GPS-spoofing attack as another FDIA with falsified phase angle data. Since identifying correct attack type is paramount, we have attempted to develop an algorithm to distinguish these two attacks. Previous researchers exploited low-rank approximation of Hankel Matrix to differentiate between FDIA and physical events. We have demonstrated that, together with angle unwrapping algorithm, low-rank approximation of Hankel Matrix can help us separating GPS-spoofing attack with FDIA. The simulation result verifies the next chapter discusses our proposed algorithm on GPS-spoofing attack detection and its ability to distinguish this type of attack from conventional FDIA. The proposed method is verified with simulation result. It has been demonstrated that the GSA with 3 second time-shift creates a low-rank approximation error 700% higher than that of normal condition, whereas FDIA doesn’t produce any significant change in low-rank approximation error from that of normal condition. Finally, we have proposed a real-time method for successful identification of event, FDIA and GSA.

Detta är en kandidatuppsats inom företagsekonomi med inriktning marknadsföring vid Handels- och IT-Högskolan i Borås. Denna studie är inriktad på hur internationella investerare inom kulturella och kreativa näringar attraheras till en stad. Kulturella och kreativa näringar skiljer sig från hur internationella investerare traditionellt attraheras till en stad. Vi har upptäckt ett glapp i litteraturen, att det inte finns modeller som är inriktade på att attrahera internationella investerare till kulturella och kreativa näringar. I studien används Stockholm Business Region Development som ett fall och studien utgår från deras arbetssätt med att attrahera investerare till staden. Studien baserades på två befintliga modeller för traditionell attraktion av investerare. De befintliga modellerna förändrades utifrån empiri samt litteratur kring varumärkesbyggande och kulturella och kreativa näringar. Resultatet blev en modell som vi tagit fram för att attrahera internationella investerare inom kulturella och kreativa näringar.

Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification.