Intrusion Detection
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Abidullha Adel ◽  
Md. Sohel Rana ◽  
Jayastree. J ◽  

Wireless Sensor Network (WSN) subjected various challenges during data transmission between nodes deployed in a network. To withstand those security challenges Intrusion Detection System (IDS) is designed. IDS is involved in attack detection and classification but is subjected to a lack of effective classification techniques for attack prevention. To overcome those challenges associated with security this research presented an effective clustering technique known as Centred-Order Node Clustering (CONC). Also, Cluster Head (CH) is elected based on the Improved Flower Pollination Algorithm (IFPA) with multi-objective characteristics. By this proposed method lifetime of the network is improved. Additionally, a supervised classification technique called AdaBoost Regression Classifier (ABRC) is developed with the Intrusion Detection System (IDS). The developed ABRC is constructed for malicious node detection with the prediction of several attacks using IDS. Through improved security mechanisms sensor nodes are involved in effective data transmission between sensor nodes. The simulation analysis stated that the proposed mechanism provides better results rather than the existing technique.

2021 ◽  
Vol 96 ◽  
pp. 107440
K.P. Sanal Kumar ◽  
S Anu H Nair ◽  
Deepsubhra Guha Roy ◽  
B. Rajalingam ◽  
R. Santhosh Kumar

2022 ◽  
Vol 70 (3) ◽  
pp. 5949-5965
K. S. Bhuvaneshwari ◽  
K. Venkatachalam ◽  
S. Hub醠ovsk� P. Trojovsk� P. Prabu

2022 ◽  
Vol 70 (3) ◽  
pp. 4261-4277
S. Ranjithkumar ◽  
S. Chenthur Pandian

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0

Security along the international border is a critical process in security assessment; It must be exercised the 24x7. With the advancements in wireless IoT technology, it has become much easier to design, develop and deploy a cost-effective, automatic and efficient system for intrusion detection in the context of surveillance. This paper set up to set up the most efficient surveillance solution, we propose a Border Surveillance Systems and sensitive sites. this surveillance and security system is to detect and track intruders trespassing into the monitoring area along the border, it able which triggers off precocious alerts and valuation necessary for the catch of efficient measurements in case of a threat. Our system is based on the classification of the human gestures drawn from videos envoy by Drones equipped with cameras and sensors in real-time. All accomplished experimentation and acquired results showed the benefit diverted from the use of our system and therefore it enables our soldiers to watch the borders at each and every moment to effectively and at low cost.

Ankit Kharwar ◽  
Devendra Thakor

2021 ◽  
Vol 30 (1) ◽  
Farouq Aliyu ◽  
Tarek Sheltami ◽  
Mohamed Deriche ◽  
Nidal Nasser

2021 ◽  
Vol 8 (1) ◽  
Abhijit Dnyaneshwar Jadhav ◽  
Vidyullatha Pellakuri

AbstractNetwork security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

Ozgur Koray Sahingoz ◽  
Ugur Cekmez ◽  
Ali Buldu

With the development of sensor and communication technologies, the use of connected devices in industrial applications has been common for a long time. Reduction of costs during this period and the definition of Internet of Things (IoTs) concept have expanded the application area of small connected devices to the level of end-users. This paved the way for IoT technology to provide a wide variety of application alternative and become a part of daily life. Therefore, a poorly protected IoT network is not sustainable and has a negative effect on not only devices but also the users of the system. In this case, protection mechanisms which use conventional intrusion detection approaches become inadequate. As the intruders’ level of expertise increases, identification and prevention of new kinds of attacks are becoming more challenging. Thus, intelligent algorithms, which are capable of learning from the natural flow of data, are necessary to overcome possible security breaches. Many studies suggesting models on individual attack types have been successful up to a point in recent literature. However, it is seen that most of the studies aiming to detect multiple attack types cannot successfully detect all of these attacks with a single model. In this study, it is aimed to suggest an all-in-one intrusion detection mechanism for detecting multiple intrusive behaviors and given network attacks. For this aim, a custom deep neural network is designed and implemented to classify a number of different types of network attacks in IoT systems with high accuracy and F1-score. As a test-bed for comparable results, one of the up-to-date dataset (CICIDS2017), which is highly imbalanced, is used and the reached results are compared with the recent literature. While the initial propose was successful for most of the classes in the dataset, it was noted that achievement was low in classes with a small number of samples. To overcome imbalanced data problem, we proposed a number of augmentation techniques and compared all the results. Experimental results showed that the proposed methods yield highest efficiency among observed literature.

Rekha P. M. ◽  
Nagamani H. Shahapure ◽  
Punitha M. ◽  
Sudha P. R.

The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.

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