# OSINT-Based LPC-MTD and HS-Decoy for Organizational Defensive Deception
## 3 Proposed Deception Models with Organizational OSINT, LPC-MTD and HS-Decoy
### Organizational Social Engineering Deception in the Proposed Model
#### 3.1.2 LPC-MTD
On the other hand, the US Coast Guard produced low security changes and high precautions on average, even with changes in time slot length. However, it showed the highest overhead frequency according to the change in each mutation cycle. Although producing high deception efficiency on average, the US Marine Corps showed higher uncertainty in the performance overhead processing according to mutation as compared to the US Army. Thus, the optimized trade-off based on the LPC-MTD mutation cycle for each of the three organizations showing similar graphs proved to be 23 s for the US Army, 25 s for the US Marine Corps, and 21 s for the US Coast Guard. Finally, the US Navy and US Space Forces produced the lowest degree of security change, preventiveness, and overhead when compared to all the other organizations. Further, the US Navy's trade-off was proven to be 26 s and the US Space Forces to be 2 s. Finally, Figure 9a theoretically presents optimized deception security within limited OSINT while considering the overhead in the US Military-based test bed. Figure 9b shows the attacker's inferiority optimized by hierarchical command and control system specifications as USPACOM, USEUCOM, USSOUTHCOM, USCENTCOM, USNORTHCOM, USCYBERCOM, USSTRATCOM, and USSPACECOM, among the organizations constituting the US Unified Combat Command. Unlike the US Military, all of the command centers with the exception of USCENTCOM and USPACOM produced the highest predictiveness between 1 and 3 s, followed by a sharp downward gradient as the time interval increased. Additionally, the optimized trade-offs were derived in sequence between 5 and 9 s. This is because only a few OSINTS were applied to IODA and the deception sequences, limiting the simulation's influence range by weight assigned hierarchically. On the contrary, USCENTCOM and USPACOM showed a downward gradient that was related to a decrease in the defender's superiority according to a change in the mutation cycle because the number of applied hierarchical OSINTs were three to five times more than other organizations, and the trade-off was also calculated as 13 s. Finally, the optimized deception security could also be conceptualized while considering the overhead in the environment of the US Unified Combat Command, as shown in Figure 9b. Figure 9c shows the attacker's inferiority optimized with the specifications of the information community related to the NSA, FBI, and CIA, among US Intelligence Agencies. Similar to the US Unified Combat Command, all of the intelligence agencies produced the highest predictive security at 1 s, and it also showed a gradual downward gradient in line with an increase in the time slot length. However, the FBI had 2.8 times the number of published OSINTs compared to the NSA and CIA, so the graph shows a higher sequential descent. The NSA and CIA produced a trade-off at 19 and 20 s, respectively, and the trade-off is composed of 28 s. Again, this can be attributed to only a few available public OSINTs, which limits the influence of each OSINT element. In addition, it is also related to the fixation of the mutation surface, which occurs because the OSINT information group contains fewer elements that improve the deception sequence efficiency, such as hosts and services, except for authorized IP or VPN. Finally, in Figure 9c, it is possible to conceptualize the deception applicability and security in the Intelligence Agency environments. The trade-offs that are based on public OSINTs are summarized in Table 6 in Appendix. Finally, we calculated the attack-defense efficiency and defender's superiority according to the deceptive surfaceization at the attack-defense point as the compromise likelihood of the CKC phase, and present, in Figure 10, the result set of surfaceization performance based on the multidimensional transfer entropy gradient.