Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood.Several cyber-attacks lead to the compromise of data security.The proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence mechanisms.The modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm.Before termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host logs.An anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of LM.After extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and precision.It is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s action.The performance of the proposed system is compared with the state-of-the-art models.

Highly Durable and Fast Response Fabric Strain Sensor for Movement Monitoring Under Extreme Conditions

The exploration of smart electronic textiles is a common goal to improve people’s quality of life.However,current smart e-textiles still face challenges such as being prone to failure under humid or cold conditions,lack of washing durability and chemical fragility.Herein,a multifunctional strain sensor with a negative resistance change was developed based on the excellent elasticity of knitted fabrics.A reduced graphene oxide(rGO)conductive fabric was first obtained by electrostatic self-assembly of chitosan(CS).Then a strain sensor was prepared using a dip-coating process to adsorb nanoscale silica dioxide and poly(dimethylsiloxane)(PDMS).A broad working range of 60%,a fast response time(22 ms)and stable cycling durability over 4000 cycles were simultaneously achieved using the prepared sensor.Furthermore,the sensor showed excel-lent superhydrophobicity,photothermal effects and UV protection,as graphene,silica and PDMS acted in synergy.This multifunctional sensor could be mounted on human joints to perform tasks,including activity monitoring,medical rehabili-tation evaluation and gesture recognition,due to its superior electromechanical capabilities.Based on its multiple superior properties,this sensor could be used as winter sportswear for athletes to track their actions without being impacted by water and as a warmer to ensure the wearer's comfort.