Accurate detection of selective forwarding attack in wireless sensor networks by Qiong Zhang, Wenzheng Zhang

“…Moreover, to prevent collaborative selective forwarding attack, E-watchdog uses reports from more than one detection agents. …”
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Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…This study investigates the enhancement of detection efficiency for DoS and spoofing attacks in IoV by employing Ensemble Learning methods combined with feature selection techniques. …”
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An attack detection method based on deep learning for internet of things by Yihan Yu, Yu Fu, Taotao Liu, Kun Wang, Yishuai An

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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

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A stacked ensemble approach to detect cyber attacks based on feature selection techniques by Wahida Ferdose Urmi, Mohammed Nasir Uddin, Md Ashraf Uddin, Md. Alamin Talukder, Md. Rahat Hasan, Souvik Paul, Moumita Chanda, John Ayoade, Ansam Khraisat, Rakib Hossen, Faisal Imran

Subjects: “…Network-based intrusion detection system…”
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Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection by Mohammed Abdullah, Ryna Svyd

“… ABSTRACT: In the realm of cybersecurity, the increasing sophistication of cyber-attacks demands the creation of sophisticated intrusion detection systems (IDS) designed to accurately detect and counteract threats in real-time. …”
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RF-RFE-SMOTE: A DoS And DDoS Attack Detection Framework by Nora Rashid Najam, Razan Abdulhammed Abduljawad

“…For this reason, this thesis utilized one of the most recent datasets (CSE-CICIDS2018 and CIC-DDoS2019) containing most Dos/DDoS attacks. This study proposed a framework based on Machine Learning for detecting denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks. …”
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Towards a minimum universal features set for IoT DDoS attack detection by Osama Ebrahem, Salah Dowaji, Suhel Alhammoud

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Intelligent intrusion detection system based on crowd search optimization for attack classification in network security by Chetan Gupta, Amit Kumar, Neelesh Kumar Jain

“…In comparison to the various state-of-the-art classifiers, the CSO method was applied to the NSL-KDD dataset and the ROSPaCe dataset for the detection of the attacks. In the proposed work, we have used a random forest technique to perform feature selection (FS) to improve the effectiveness and efficiency of intrusion detection. …”
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Real-Time Power System Event Detection: A Novel Instance Selection Approach by Gabriel Intriago, Yu Zhang

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RF-SFAD: A RANDOM FOREST MODEL FOR SELECTIVE FORWARDING ATTACK DETECTION IN MOBILE WIRELESS SENSOR NETWORKS by N Usha Bhanu, Soubhagya Ranjan Mallick, Sreenivasa Rao Chappidi, K Sangeethalakshmi

Subjects: “…mobile wireless sensor networks, random forest algorithm, selective forwarding attack detection, clustering, feature selection.…”
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Text Select-Backdoor: Selective Backdoor Attack for Text Recognition Systems by Hyun Kwon, Jang-Woon Baek

“…In a backdoor attack, an attacker employs a specific trigger to initiate the attack. …”
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An Efficient Approach Based on RAE-GAMI-NET for Long Range Attack Detection on Blockchain by Vasavi Chithanuru, Mangayarkarasi Ramaiah

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Network Attack Detection for Business Safety by Fadia Abduljabbar Saeed, Ghalia Nassreddine, Joumana Younis

“…In this paper, a machine learning-based approach was developed to detect network attacks. Two Machine learning models were used: Support vector machine and Artificial neural network. …”
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About Detection of Code Reuse Attacks by Yury V. Kosolapov

“…At the heart of these attacks lies the detection, in the vulnerable program of suitable areas, of executable code — gadgets — and chaining these gadgets into chains. …”
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Android collusion attack detection model by Hongyu YANG, Zaiming WANG

“…In order to solve the problem of poor efficiency and low accuracy of Android collusion detection,an Android collusion attack model based on component communication was proposed.Firstly,the feature vector set was extracted from the known applications and the feature vector set was generated.Secondly,the security policy rule set was generated through training and classifying the privilege feature set.Then,the component communication finite state machine according to the component and communication mode feature vector set was generated,and security policy rule set was optimized.Finally,a new state machine was generated by extracting the unknown application’s feature vector set,and the optimized security policy rule set was matched to detect privilege collusion attacks.The experimental results show that the proposed model has better detective efficiency and higher accuracy.…”
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Matrix profile for DDoS attacks detection by Faisal Alotaibi, Alexei Lisitsa

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Detecting LDoS attack based on ASPQ by Jing ZHANG, Hua-ping HU, Bo LIU, Feng-tao XIAO

“…Based on the analysis of the effects on average size of packet in the queue which LDoS attack makes,the change of this value was got by simulation on NS2.So detection algorithm was proposed,and was applied on Droptail and RED,which were typical queue management algorithm.The result of simulation shows that the algorithm can effectively detect the LDoS attack.…”
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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

Subjects: “…Heart attack prediction…”
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Optimizing feature selection and deep learning techniques for precise detection of low-rate distributed denial of service (LDDoS) attack by Naeem Ali Al-Shukaili, Miss Laiha M. Kiah, Ismail Ahmedy

“…Low-rate DDoS refers to the small number of requests to overcome the sudden spikes that disrupt the server.This work aims to improve the detection of two common LDDoS attack types, slowloris and slowhttptest simulated attacks, by optimizing feature selection and utilizing deep learning techniques. …”
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