• Cyber Security Advantages of Optical Communications in SATCOM Networks

      Kholidy, Hisham A.; Baker, Cameron; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-12)
      Space-based communications, whether it is ground-to-space or inter-satellite communications, have so far been primarily within the RF spectrum. With the increase in space missions and the need for larger amounts of data being sent to and from satellites, the near infrared or optical spectrum has started to become more widely used instead of RF. Higher bandwidth is not the only advantage of using optics for communications over RF, there is also an inherent security advantage as well. Currently, there is far too little enforcement of security standards for space communications networks, and the use of RF only worsens the problem due to its very large beam spread when compared to optics. This paper will seek to prove that optics is a far more superior technology to be used for space communications networks from a security standpoint as well as providing an increase in available bandwidth. These points will be proven by first introducing the technology by examining current Free Space Optics (FSO) systems and space optics systems being provided by manufacturers. Secondly, this paper will discuss the current state of space communications security, and issues space communications networks are facing using RF with the recent advancement into low-cost SmallSat operations that threaten existing space vehicles, and the lack of standard security practices within these networks. Lastly, this paper will provide evidence into why optics communications can improve the security of spaced based communications due to its lower beam spread and the ability to incorporate quantum key distribution into the communications channel.
    • A Wireless Intrusion Detection for the Next Generation (5G) Networks

      Kholidy, Hisham A.; Ferrucci, Richard; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-05)
      5G data systems are closed to delivery to the public. The question remains how security will impact the release of this cutting edge architecture. 5G data systems will be sending massive amounts of personal data due to the fact that everybody in the world is using mobile phones these days. With everyone using a 5G device, this architecture will have a huge surface area for attackers to compromise. Using machine learning techniques previously applied to 802.11 networks. We will show that improving upon these previous works, we can have a better handle on security when it comes to 5G architecture security. We find that using a machine learning classifier known as LogIT boost, combined with a selected combination of feature selection, we can provide optimal results in identifying three different classes of traffic referred to as normal, flooding, and injection traffic. We drastically decrease the time taken to perform this classification while improving the results. We simulate the Device2Device (D2D) connections involved in the 5G systems using the AWID dataset. The evaluation and validation of the classification approach are discussed in details in this thesis.