# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 6 AI-Based Malware Detection
### Active Immunity for Malware Detection  
The application of adversarial machine learning to malware detection provides a novel approach to augment the existing methods of extracting software information and implementing algorithms to detect malicious code. It is imperative to be cogniant of the possibility of adversarial manipulation when utilizing machine learning algorithms. The perpetrator will take advantage of the vulnerabilities of machine learning to evade detection. If the detector is capable of anticipating the likely evasion route of the attacker, it can drastically shorten the latency of detection and mitigate the damages caused by unknown attacks, thereby achieving an \"active immunity\".  
[PERSON] et al., 2017 [62], referred to the dynamic between evasion attack and defense as an \"arms race\". To begin with, they developed an efficacious evasion model (EvnAttack) by simulating the behavior of potential attackers. to efficiently address this type of evasion attack, they proposed a malware detection learning paradigm (SecDefender) that takes into account the cost associated with the attacker's evasion attack. The efficacy of this method was demonstrated through extensive experimentation on the actual datasets of the Comodo Cloud Security Center. [PERSON] et al., 2018 [63], noted that malignant software can circumvent machine learning detection techniques by periodically varying its structure while retaining its malicious functions [62; 63].  
Reinforcement learning can be utilized to continually generate simulated attackers to create new malware samples, thereby providing potential attack vectors for defend  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Software Information** & **Malware Type** & **ML Methods** \\ \hline \multirow{3}{*}{Signature-based Detection [61]} & A signature comprising software & \multirow{3}{*}{No restrictions} & \multirow{3}{*}{No ML methods used.} \\  & details developed either & & \\ \cline{1-1} \cline{3-4}  & automatically or manually. & \\ \cline{1-1} \cline{3-4} Network Behaviours [55] & Network Behaviours & & \\ \cline{1-1} \cline{3-4}  & APK and API [56; 57] & APK, API & Android Malware & Ensemble Learning \\ \cline{1-1} \cline{3-4} Binary Image [58] & Binary Image & Telnet Attack Software & CNN \\ \cline{1-1} \cline{3-4} Opcode and Graph [59; 60] & Opcode & Windows Malware & CNN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of Approaches for Detecting Malicious Software.  
Figure 10: Graph transformed from opcode representations.  
ers to consider. They constructed a reinforcement learning-based model to increase the likelihood of these freshly created malware to avoid ML models, and then re-trained the detection model utilizing these recently generated examples. The results of the experiments conducted showed that the detection accuracy of malware was substantially increased from 15.75% to 93.5% following retraining, significantly enhancing the detection model's capacity to detect unfamiliar attacks.  
[PERSON] et al., 2017 [64], examined the pre-existing attack frameworks, and synthesised and organized the current attacker's objectives, information, attack strategies, and potential attack scenarios. Subsequently, they undertook a series of evasion techniques to assess the efficacy of malicious software detectors. It was demonstrated that by employing linear and nonlinear classifiers with feature weights that are uniformly distributed, the security of the system can be improved without significantly compromising the computational efficiency [61; 64; 65; 66; 67].