성균관대학교 · Computer Science
Tamer Abuhmed 교수의 연구실은 사이버 보안 및 지능형 시스템 분야에 초점을 맞추고 있습니다. 특히 딥 패킷 인spect( DPI ) 기반의 악성 공격 탐지, 무선 센서 네트워크의 소프트웨어 기반 코드 인증, 그리고 인공지능 기반 의료 진단 프레임워크 개발을 통해 보안성과 신뢰성을 강화하는 데 주력하고 있습니다. 또한 최적화 알고리즘의 성능 향상과 3D 뇌 영상 분석을 통한 알츠하이머병 조기 진단 기술 개발 등 다학제적 연구를 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attacks. In this paper, we survey the deep packet inspection implementations techniques, research challenges and algorithms. Finally, we provide a comparison between the differen
Sensor nodes are usually vulnerable to be compromised due to their unattended deployment. The low cost requirement of the sensor node precludes using an expensive tamper resistant hardware for sensor physical protection. Thus, the adversary can reprogram the compromised sensors and deviates sensor network functionality. In this paper, we propose two simple software-based remote code attestation schemes for different WSN criterion. Our schemes use different independent memory noise filling techni
This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the e
Artificial intelligence (AI)-based diagnostic systems provide less error-prone and safer support to clinicians, enhancing the medical decision-making process. This study presents a smart and reliable healthcare framework for detecting Alzheimer's disease (AD) progression. Early detection of AD before the onset of clinical symptoms is the most crucial step in starting timely treatment. To predict the conversion of cognitively normal patients to those with AD, three-dimensional 3D magnetic resonan
Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attacks. In this paper, we survey the deep packet inspection implementations techniques, research challenges and algorithms. Finally, we provide a comparison between the differen
