Practical Implementation of an Adaptive Detection-Defense Unit against Link Layer DoS Attacks for Wireless Sensor Networks

Wireless sensor networks (WSNs) have become a very popular subject in both industrial and academic fields of study due to the fact that they can operate on their own, do not require extra maintenance, and can be utilized in a wide variety of applications. In addition, the sensor nodes having limited hardware resources and power units cause certain security problems awaiting to be resolved. The Denial-of-Service (DoS) attacks, which cause disrupts in the communication of sensor nodes or abnormal situations, thus resulting in the decrease of the lifespan of the network, constitute a serious threat against the WSN security. Especially in military applications in which security is the most important design criterion, the WSN used in chemical and biological intrusion detection applications must be resistant against all forms of attacks. In this study, an adaptive detection-defense unit has been developed against the DoS attacks (packet collision, exhaustion, and unfairness) which occur in the data link layer. The developed unit has also been implemented on the TelosB nodes. Due to the new unit that was designed the lifespan of the nodes has been extended without the need for additional hardware by making them more secure against DoS attacks in the data link layer of the WSN

Practical Implementation of an Adaptive Detection-Defense Unit against Link Layer DoS Attacks for Wireless Sensor Networks

Wireless sensor networks (WSNs) have become a very popular subject in both industrial and academic fields of study due to the fact that they can operate on their own, do not require extra maintenance, and can be utilized in a wide variety of applications. In addition, the sensor nodes having limited hardware resources and power units cause certain security problems awaiting to be resolved. The Denial-of-Service (DoS) attacks, which cause disrupts in the communication of sensor nodes or abnormal situations, thus resulting in the decrease of the lifespan of the network, constitute a serious threat against the WSN security. Especially in military applications in which security is the most important design criterion, the WSN used in chemical and biological intrusion detection applications must be resistant against all forms of attacks. In this study, an adaptive detection-defense unit has been developed against the DoS attacks (packet collision, exhaustion, and unfairness) which occur in the data link layer. The developed unit has also been implemented on the TelosB nodes. Due to the new unit that was designed the lifespan of the nodes has been extended without the need for additional hardware by making them more secure against DoS attacks in the data link layer of the WSN

NEURAL NETWORK BASED VEHICULAR LOCATION PREDICTION MODEL FOR COOPERATIVE ACTIVE SAFETY SYSTEMS

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models

Design and implementation of a web-based intrusion prevention system: a new hybrid model

Firewalls, intrusion detection and prevention systems are used to protect web applications against network attacks. HTTP is also used to attack to web applications. HTTP request detections are performed in order to prevent these attacks. In this study, a new hybrid model is proposed which uses signature-based detection and anomaly based detection to prevent web-based attacks. Some types of web-based known attacks detection were implemented by signature-based detection. Anomaly based detection were implemented by bayes classification, which is a data mining technique, using features of Alfanumeric Character, Letter Frequency and Request Length. Because signature based detection is faster than anomaly based detection, signature based detection database is updated with detected anomaly HTTP requests obtained by anomaly based detection. Proposed model was tested by using CSIC 2010, ECML-PKDD 2007 and WUGD 2015 dataset which is generated during this study. According to the test results; anomaly based detection was conducted with a high mean achievement percentage (95,1\%). The test results were compared with some similar studies. According to the comparison results, proposed model provided high performance and low false positive rate compared to the other studies

A modified genetic algorithm for a special case of the generalized assignment problem

Many central examinations are performed nationwide in Turkey. These examinations are held simultaneously throughout Turkey. Examinees attempt to arrive at the examination centers at the same time and they encounter problems such as traffic congestion, especially in metropolises. The state of mind that this situation puts them into negatively affects the achievement and future goals of the test takers. Our solution to minimize the negative effects of this issue is to assign the test takers to closest examination centers taking into account the capacities of examination halls nearby. This solution is a special case of the generalized assignment problem (GAP). Since the scale of the problem is quite large, we have focused on heuristic methods. In this study, a modified genetic algorithm (GA) is used for the solution of the problem since the classical GA often generates infeasible solutions when it is applied to GAPs. A new method, named nucleotide exchange, is designed in place of the crossover method. The designed method is run between the genes of a single parent chromosome. In addition to the randomness, the consciousness factor is taken into consideration in the mutation process. With this new GA method, results are obtained successfully and quickly in large-sized data sets