scholarly journals An Intelligent Spam Detection Model Based on Artificial Immune System

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 209 ◽  
Author(s):  
Abdul Jabbar Saleh ◽  
Asif Karim ◽  
Bharanidharan Shanmugam ◽  
Sami Azam ◽  
Krishnan Kannoorpatti ◽  
...  
Keyword(s):  
Negative Selection ◽  
High Performance ◽  
Detection Rate ◽  
Single Individual ◽  
Spam Filtering ◽  
True Negative ◽  
Negative Selection Algorithm ◽  
Detection Model ◽  
Set Up ◽  
Negative Detection

Spam emails, also known as non-self, are unsolicited commercial or malicious emails, sent to affect either a single individual or a corporation or a group of people. Besides advertising, these may contain links to phishing or malware hosting websites set up to steal confidential information. In this paper, a study of the effectiveness of using a Negative Selection Algorithm (NSA) for anomaly detection applied to spam filtering is presented. NSA has a high performance and a low false detection rate. The designed framework intelligently works through three detection phases to finally determine an email’s legitimacy based on the knowledge gathered in the training phase. The system operates by elimination through Negative Selection similar to the functionality of T-cells’ in biological systems. It has been observed that with the inclusion of more datasets, the performance continues to improve, resulting in a 6% increase of True Positive and True Negative detection rate while achieving an actual detection rate of spam and ham of 98.5%. The model has been further compared against similar studies, and the result shows that the proposed system results in an increase of 2 to 15% in the correct detection rate of spam and ham.

scholarly journals A Soft-YoloV4 for High-Performance Head Detection and Counting

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3096
Author(s):  
Zhen Zhang ◽  
Shihao Xia ◽  
Yuxing Cai ◽  
Cuimei Yang ◽  
Shaoning Zeng
Keyword(s):  
High Performance ◽  
Detection Rate ◽  
Detection Method ◽  
State Of The Art ◽  
Counting Method ◽  
People Counting ◽  
Detection Model ◽  
Detection And Counting ◽  
Head Detection ◽  
Ap Value

Blockage of pedestrians will cause inaccurate people counting, and people’s heads are easily blocked by each other in crowded occasions. To reduce missed detections as much as possible and improve the capability of the detection model, this paper proposes a new people counting method, named Soft-YoloV4, by attenuating the score of adjacent detection frames to prevent the occurrence of missed detection. The proposed Soft-YoloV4 improves the accuracy of people counting and reduces the incorrect elimination of the detection frames when heads are blocked by each other. Compared with the state-of-the-art YoloV4, the AP value of the proposed head detection method is increased from 88.52 to 90.54%. The Soft-YoloV4 model has much higher robustness and a lower missed detection rate for head detection, and therefore it dramatically improves the accuracy of people counting.

Windows Malware Detection Method Based on the Path IRP

2014 ◽  
Vol 687-691 ◽  
pp. 2626-2629
Author(s):  
Fu Yong Zhang
Keyword(s):  
Operating System ◽  
Positive Selection ◽  
Negative Selection ◽  
Detection Rate ◽  
Detection Method ◽  
Malware Detection ◽  
Experimental Results ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Request Sequence

Because the IRP (I/O Request Packets) sequences of programs are not identical in different environments in the same operating system, which have a certain influence on the detection results. Through a lot of experiments, we found that the IRP request sequences of programs on the same operation path are consistent. Therefore, the new malware detection method based on the path IRP sequences is proposed. Every single IRP request sequence on the same operation path is extracted, Negative Selection Algorithm (NSA) and Positive Selection Algorithm (PSA) are used for detection. Experimental results reveal that our method outperforms the method which based on IRP sequences in detection rate.

An improved real-valued negative selection algorithm based on the constant detector for anomaly detection

2021 ◽  
Vol 40 (5) ◽  
pp. 8793-8806
Author(s):  
Dong Li ◽  
Xin Sun ◽  
Furong Gao ◽  
Shulin Liu
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
State Space ◽  
False Alarm Rate ◽  
Negative Selection ◽  
Detection Rate ◽  
Detection Methods ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Synthetic Datasets

Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce the false alarm rate. We present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases.

scholarly journals Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector

2022 ◽  
Vol 14 (2) ◽  
pp. 254
Author(s):  
Minjing Shi ◽  
Pengfei He ◽  
Yuli Shi
Keyword(s):  
Northern Hemisphere ◽  
High Performance ◽  
Mean Average Precision ◽  
Mature Stage ◽  
Extratropical Cyclones ◽  
Single Shot ◽  
Average Precision ◽  
Acceptable Result ◽  
Detection Model ◽  
Set Up

In this paper, we propose a deep learning-based model to detect extratropical cyclones (ETCs) of the northern hemisphere, while developing a novel workflow of processing images and generating labels for ETCs. We first labeled the cyclone center by adapting an approach from Bonfanti et al. in 2017 and set up criteria of labeling ETCs of three categories: developing, mature, and declining stages. We then gave a framework of labeling and preprocessing the images in our dataset. Once the images and labels were ready to serve as inputs, an object detection model was built with Single Shot Detector (SSD) and adjusted to fit the format of the dataset. We trained and evaluated our model with our labeled dataset on two settings (binary and multiclass classifications), while keeping a record of the results. We found that the model achieves relatively high performance with detecting ETCs of mature stage (mean Average Precision is 86.64%), and an acceptable result for detecting ETCs of all three categories (mean Average Precision 79.34%). The single-shot detector model can succeed in detecting ETCs of different stages, and it has demonstrated great potential in the future applications of ETC detection in other relevant settings.

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