Solutions descriptions and advancements Sample Clauses

Solutions descriptions and advancements. AI and ML contributions

Solutions descriptions and advancements. ‌ To fill the aforementioned gaps, we leverage the potential of Deep Learning to build three solutions for tackling the stealthy DDoS attacks in a 5G environment, considering two settings (i.e., network slicing and multi-domain/multi-tenancy) and performing malicious behaviour detection either by using data collected from network layer (i.e., characteristics of network flows) or application layer (i.e., resource usage and service performance metrics).

Solutions descriptions and advancements. To date, there have been very few empirically published accounts of an effective GPS spoofing detection approach that accommodates resource, cost, and environmental constraints. For this purpose, we propose a new solution to devise an effective cellular-enabled UE GPS spoofing detection system, where MLP is used to analyse the statistical features of path losses between UE and base stations (BSs). The proposed approach consists of three processes, namely data sampling, statistical analysis, and MLP prediction. • Data sampling: There are two kinds of data that need to be collected in the processing, namely the actual path losses data and the theoretical path losses data. The actual path losses data are the observations of base stations, and the corresponding theoretical path losses data are computed according to both UE’ GPS positions and BSs’ GPS positions. One of the theoretical path loss models between BS and UE is defined in 3GPP document in [64]. The theoretical path loss model in [64] is adopted in our solution. • Statistical analysis: A statistical analysis is then performed using moments (e.g., Mean Variance Skewness Kurtosis (MVSK)), quartile (e.g., BOX), and probability distributions difference (e.g., ▇▇▇▇▇▇▇▇▇▇▇ Distance (WD)) methods to extract the statistical characteristics of actual and theoretical path losses data. To obtain the metrics of path loss, we consider the number of data transmissions is O in a time duration t. The corresponding actual path loss set 𝐿𝑖𝑣 (𝑡) is expressed as: {𝐿1 (𝑡), … , 𝐿𝑜 (𝑡), … , 𝐿𝑂 (𝑡)} 𝑖𝑣 𝑖𝑣 where 𝐿𝑜 (𝑡) donates the 𝑜𝑡ℎ path loss in t between BS 𝑖 and UE 𝑣. Similarly, the theoretical path loss set is defined as: {𝐿̅1 (𝑡), … , 𝐿̅𝑜 (𝑡), … , 𝐿̅𝑂 (𝑡)} 𝑖𝑣 𝑖𝑣 where 𝐿̅𝑜 (𝑡) denotes the 𝑜𝑡ℎ path loss in t. According to the MVSK method, we have ▇▇ (𝑡) = 1 ∑𝑂 𝐿𝑜 (𝑡), 𝑂 𝑜=1 𝑖𝑣 𝐿𝑉 (𝑡) = 1 ∑𝑂 (𝐿𝑜 (𝑡) − ▇▇ (𝑡))2, 𝑂 𝑜=1 𝑖𝑣 𝑖𝑣 3 𝐿 (𝑡) = 1 𝑂 ∑ 𝑜=1 𝐿𝑜 (𝑡)− ▇▇(𝑡) ( 𝑖𝑣 𝑖𝑣 ) , 𝑖𝑣 √𝐿𝑉 (𝑡) 𝐾 1 ∑ 𝐿𝑜 (𝑡)− ▇▇(𝑡) 𝐿𝑖𝑣 (𝑡) = 𝑂 𝑜=1 ( 𝑖𝑣 𝑖𝑣 ) , 𝑖𝑣 √𝐿𝑉 (𝑡) where ▇▇ (𝑡), 𝐿𝑉 (𝑡), 𝐿𝑆 (𝑡) and 𝐿𝐾 (��) represent, respectively, the mean, variance, skewness, 𝑖𝑣 and kurtosis values of actual path losses in time interval t. Similarly, we can obtain the theoretical ones ▇̅▇ (𝑡), 𝐿̅𝑉 (𝑡), 𝐿̅𝑆 (𝑡) and 𝐿̅𝐾 (𝑡). The details on BOX and WD metrics can be found at [65]. 𝑖𝑣 𝑖𝑣 • MLP prediction: The statistical properties are used as inputs to the devised MLP models (See Figure 17). The input ∆𝐿𝑥𝑚 denotes the 𝑚𝑡ℎ difference of path losses reported by BS 𝑖 under each of t...

Solutions descriptions and advancements. To date, there have been very few empirically published accounts of an effective GPS spoofing detection approach that accommodates resource, cost, and environmental constraints. For this purpose, we propose a new solution to devise an effective cellular-enabled UE GPS spoofing detection system, where MLP is used to analyse the statistical features of path losses between UE and Base Stations (BSs). The proposed approach consists of three processes, namely data sampling, statistical analysis, and MLP prediction. x Data sampling: There are two kinds of data that need to be collected in the processing, namely the actual path losses data and the theoretical path losses data. The actual path losses data are the observations of base stations, and the corresponding theoretical path losses data are ĐŽŵƉƵƚĞĚ ĂĐĐŽƌĚŝŶŐ ƚŽ ďŽƚŚ h ͛ 'W^ ƉŽƐŝƚŝŽŶ path loss models between BS and UE is defined in 3GPP document in [64]. The theoretical path loss model in [64] is adopted in our solution. x Statistical analysis: A statistical analysis is then performed using moments (e.g., Mean Variance Skewness Kurtosis (MVSK)), quartile (e.g., BOX), and probability distributions difference (e.g., ▇▇▇▇▇▇▇▇▇▇▇ Distance (WD)) methods to extract the statistical characteristics of actual and theoretical path losses data. To obtain the metrics of path loss, we consider the number of data transmissions is O in a time duration t. The corresponding actual path loss set ܮ௜௩ሺݐሻis expressed as: ൛ܮଵሺݐሻǡǥǡܮ௢ሺݐሻǡǥǡܮைሺݐሻൟ ௜௩ where ܮ௢ሺݐሻdonates the ݋௧௛path loss in t between BS ݅and UE ݒ. Similarly, the theoretical path loss set is defined as: ൛ܮതଵ ሺݐሻǡǥǡܮത௢ሺݐሻǡǥǡܮതைሺݐሻൟ ௜௩ ௜௩ ௜௩ ௜௩ where ܮത௢ሺݐሻdenotes the ݋௧௛path loss in t. According to the MVSK method, we have ܮெሺݐሻ= ଵσை ܮ௢ሺݐሻ, ௜௩ ை ௢ୀଵ ௜௩ ܮ௏ሺݐሻ= ଵσை ሺܮ௢ሺݐሻെ ܮெሺݐሻଶ, ௜௩ ை ௢ୀଵ ௜௩ ௜௩ ଷ ௌ ଵ ௅೚ሺ௧ሻି ௅ಾሺ௧ሻ ܮ ሺݐሻ= σை ቌ ೔ೡ ೔ೡ ቍ , ௜௩ ை ௄ ଵ ௢ୀଵ ೔ೡ ට௅ೇሺ௧ሻ ସ ௅೚ሺ௧ሻି ௅ಾሺ௧ሻ ܮ ሺݐሻ= σை ቌ ೔ೡ ೔ೡ ቍ , ௜௩ ை ௢ୀଵ ೔ೡ ට௅ೇሺ௧ሻ where ܮெሺݐሻǡܮ௏ሺݐሻǡܮௌሺݐሻand ܮ௄ሺݐሻrepresent, respectively, the mean, variance, skewness, ௜௩ ௜௩ ௜௩ ௜௩ and kurtosis values of actual path losses in time interval t. Similarly, we can obtain the theoretical ones ܮതெሺݐሻǡܮത௏ሺݐሻǡܮതௌሺݐሻand ܮത௄ሺݐሻ. The details on BOX and WD metrics can be found at [65]. ௜௩ ௜௩ ௜௩ ௜௩ ௜௩ x MLP prediction: The statistical properties are used as inputs to the devised MLP models (See Figure 17). The input οܮ௫೘ denotes the ݉௧௛difference of path losses reported by BS ݅under each of the statistical methods (i.e., MVSK, BOX, WD). ݉௫אሼܯǡܸǡܵǡܭሽfor MVSK-based MLP (MVSK-MLP) model, ݉௫אሼܳ...