Biblio

One of the most dynamic network is the Mobile Adhoc (MANET) network. It is a list of numerous mobile nodes. Dynamic topology and lack of centralization are the basic characteristics of MANET. MANETs are prone to many attacks due to these characteristics. One of the attacks carried out on the network layer is the blackhole attack. In a black-hole attack, by sending false routing information, malicious nodes interrupt data transmission. There are two kinds of attacks involving a black-hole, single and co-operative. There is one malicious node in a single black-hole attack that can act as the node with the highest sequence number. The node source would follow the direction of the malicious node by taking the right direction. There is more than one malicious node in the collaborative black-hole attack. One node receives a packet and sends it to another malicious node in this attack. It is very difficult to detect and avoid black-hole attacks. Many researchers have invented black-hole attack detection and prevention systems. In this paper, We find a problem in the existing solution, in which validity bit is used. This paper also provides a comparative study of many scholars. The source node is used to detect and prevent black hole attacks by using a binary partition clustering based algorithm. We compared the performance of the proposed solution with existing solution and shown that our solution outperforms the existing one.

Software defined networking (SDN) architecture frame- work eases the work of the network administrators by separating the data plane from the control plane. This provides a programmable interface for applications development related to security and management. The centralized logical controller provides more control over the total network, which has complete network visibility. These SDN advantages expose the network to vulnerabilities and the impact of the attacks is much severe when compared to traditional networks, where the network devices have protection from the attacks and limits the occurrence of attacks. In this paper, we proposed an entropy based algorithm in SDN to detect as well as stopping distributed denial of service (DDoS) attacks on the servers or clouds or hosts. Firstly, there explored various attacks that can be launched on SDN at different layers. Basically DDoS is one kind of denial of service attack in which an attacker uses multiple distributed sources for attacking a particular server. Every network in a system has an entropy and an increase in the randomness of probability causes entropy to decrease. In comparison with previous entropy based approaches this approach has higher performance in distinguishing legal and illegal traffics and blocking illegal traffic paths. Linux OS and Mininet Simulator along with POX controller are used to validate the proposed approach. By conducting pervasive simulation along with theoretical analysis this method can definitely detect and stop DDoS attacks automatically.

Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).

At present, the AE method has the advantages of live measurement, online monitoring and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. In this paper, development of a data processing software for PD acoustic detection based on a general fast analysis algorithm model is introduced. With considering the signal flow chart of current acoustic detection system widely used in operation and maintenance of power system equipments, the main function of the developed PD AE signals analysis software was designed, including the detailed analysis of individual data file, identification with phase compensation based on 2D PRPD histograms, batch processing analysis of data files, management of discharge fingerprint library and display of typical defect discharge data. And all of the corresponding developed software pages are displayed.

At present, the acoustic emission (AE) method has the advantages of live measurement and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. While the conventional AE detection system or instruments always can't give a right discrimination result, because them always work based on the reference voltage or phase information from an auxiliary 220V voltage signal source rather than the operation high voltage (HV) with the real phase information corresponding to the detected AE pulsed signals. So there is a random phase difference between the reference phase and operation phase. The discharge fingerprint formed by the detected AE pulsed signals with reference phase using the same processing process is compared to the discharge fingerprint database formed in the HV laboratory with the real phase information, therefore, the system may not be able to discriminate the discharge mode of the field measured data from GIS in substation operation. In this paper, in order to design and develop a general fast analysis algorithm model for PD acoustic detection system to make an assistant diagnosis, the pattern identification with phase compensation was designed and applied. The results show that the method is effective and useful to deatl with AE signals meased in operation situation.

Modular exponentiation of large number is widely applied in public-key cryptosystem, also the bottleneck in the computation of public-key algorithm. Modular multiplication is the key calculation in modular exponentiation. An improved Montgomery algorithm is utilized to achieve modular multiplication and converted into systolic array to increase the running frequency. A high efficiency fast modular exponentiation structure is developed to bring the best out of the modular multiplication module and enhance the ability of defending timing attacks and power attacks. For 1024-bit key operands, the design can be run at 170MHz and finish a modular exponentiation in 4,402,374 clock cycles.