Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a graph-based algorithmic technique for malware detection. More precisely,  utilize the system-call dependency graphs (or, for short, ScD graphs), obtained by capturing taint analysis traces and a set of various similarity metrics in order to detect whether an unknown test sample is a malicious or a benign one. For the sake of generalization,  decide to empor our model against strong mutations by applying our detection technique on a ighted directed graph resulting from ScD graph after grouping disjoint subsets of its vertices. Additionally,  propose the Δ-Similarity metric, which is based on the Euclidean distance operating on the in-degree and out-degree of ScD's nodes along with their corresponding ights, distinguishing thus graph-representations of malware and benign software. Finally,  evaluate the potentials of our detection model and show that its performance makes it competing to other detection models.
['Stavros D. Nikolopoulos', 'Iosif Polenakis']
I am writing a research paper on a idea A graph-based model for malicious code detection exploiting dependencies of system-call groups
[{"from": "human", "value": "I am writing a research paper on a idea A graph-based model for malicious code detection exploiting dependencies of system-call groups"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a graph-based algorithmic technique for malware detection. More precisely,  utilize the system-call dependency graphs (or, for short, ScD graphs), obtained by capturing taint analysis traces and a set of various similarity metrics in order to detect whether an unknown test sample is a malicious or a benign one. For the sake of generalization,  decide to empor our model against strong mutations by applying our detection technique on a ighted directed graph resulting from ScD graph after grouping disjoint subsets of its vertices. Additionally,  propose the \u0394-Similarity metric, which is based on the Euclidean distance operating on the in-degree and out-degree of ScD's nodes along with their corresponding ights, distinguishing thus graph-representations of malware and benign software. Finally,  evaluate the potentials of our detection model and show that its performance makes it competing to other detection models."}]