Dr. Sedat Akleylek

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Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 February 2024 in IEEE Access

Journal Article

A Systematic Literature Review on Host-Based Intrusion Detection Systems

Hami Satılmış

Sedat Akleylek

Zaliha Yüce Tok

With the advancements in computer networks and systems, the number of security vulnerabilities and cyber attacks targeting/using these vulnerabilities continues to increase. Consequently, various intrusion detection systems (IDS) have been developed to detect cyber attacks and ensure information security. IDSs are categorized into two classes based on the data sources: Network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS). In this systematic literature review (SLR), studies are examined that focus on HIDS or propose methods applicable to HIDS, as well as those related to IDSs that can be converted into HIDSs. The studies published between 2020 and 2023 are collected from widely used academic databases through various query statements. Filtering based on specific selection and elimination criteria is undergone by the collected studies, resulting in 21 studies for examination. Subsequently, these studies and their advantages and disadvantages are discussed. In addition, while examining the studies, five research questions are addressed. Finally, the defects, potential areas for improvement, and future research directions related to HIDSs are discussed.

19 March 2021 in Neural Computing and Applications

Journal Article

A novel permission-based Android malware detection system using feature selection based on linear regression

27 January 2022 in IEEE Access

Journal Article

LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers

Durmus Ozkan Sahin

Sedat Akleylek

Erdal Kiliç

In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android operating system, are extracted through static analysis, and security analyzes of applications are carried out with machine learning techniques. Based on the multiple linear regression techniques, two classifiers are proposed for permission-based Android malware detection. These classifiers are compared on four different datasets with basic machine learning techniques such as support vector machine, k-nearest neighbor, Naive Bayes, and decision trees. In addition, using the bagging method, which is one of the ensemble learning, different classifiers are created, and the classification performance is increased. As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms.

13 August 2021 in Cluster Computing

Journal Article

Lattice-based cryptosystems for the security of resource-constrained IoT devices in post-quantum world: a survey

1 March 2018

Conference Paper

A survey on security threats and authentication approaches in wireless sensor networks

Aykut Karakaya

Sedat Akleylek

Wireless sensor networks (WSN) are networks in which data obtained by observing the environment by a large number of sensors deployed in a specific area are sent securely to other sensors or centers in the network. These networks have the abilities of being not connected to a central node, self-managing and healing, not being connected to a specific network topology, multi-way routing, preserving the integrity and confidentiality of data, and being robust. Today's ongoing work: designing sensors that are resistant to harsh weather conditions, reducing energy consumption, designing low-cost sensors with high capacities, and making data flow faster and safer. The data obtained from the sensors must be transmitted safely to the target. Wireless sensor networks have a large number of attack types (Sybil, Wormhole, Sinkhole, etc.) that threaten data flow. While designing security policies, a general structure is aimed at eliminating some or all of the attacks. For this reason, policies based on information security principles such as privacy, integrity, availability, authentication and non-repudiation have been developed. In this paper, current problems are assessed in the security of wireless sensor networks, and authentication security policies are discussed.

7 April 2016

Book Chapter

An Efficient Lattice-Based Signature Scheme with Provably Secure Instantiation

Sedat Akleylek

Nina Bindel

Johannes Buchmann

Juliane Krämer

Giorgia Azzurra Marson

About Me

HYUN JIN LEE received a B.S and M.S. in Electrical Engineering from AJOU University, Suwon, Rep. of Korea, in 2004, in 2006 respectively. He received a Ph.D. in Electrical Engineering from Ajou University, Rep. of Korea, in 2013. From 2013 to 2014, he was a Post-Doctoral Fellow at the Department of Electrical and Computer Engineering, Ajou University, Suwon, Rep. of Korea. From 2014 to 2015, he worked as a Senior Engineer in Avionic team, Danam Systems Inc., Anyang, Rep. of Korea. From 2015 to 2020, he worked as a Chief Engineer and Team leader in M&S (Modeling and Simulation) team, Solvit Systems Co., Ltd., Seoul, Rep. of Korea. From 2020 to 2022, he worked as a Principal Engineer and Team Leader in Principal Technology team, WINS Co., Ltd., Seongnam, Rep. of Korea. He has been with the Ground Control-Cyber Team/Future Information Communication R&D Center, Hanwha Systems Co., Ltd., Seongnam, Rep. of Korea, as a Principal Engineer since 2022. His research interests include the Military Network Communication and the Cyber Security. Dr. Lee was the recipient of Samsung Human Tech. Thesis Prize in 2004. He is a member of Korean Institute of Communication and Information Science (KICS), the Institute of Electronics and Information Engineer (IEIE), the Korea Institute of Information Security & Cryptology (KIISC), and the Korea Socity for Simulation (KSS).

Dr. Sedat Akleylek

Dr. Sedat Akleylek

Research Focus & Metrics

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About Me