Packet Dropping Attacks Research Articles

Detection and Isolation of Selective Packet Drop Attack in MANET

Detection and Isolation of Selective Packet Drop Attack in MANET

Optimized Energy Efficient Trust Aware System in Wireless Sensor Networks

Energy and security are very important issues in Wireless Sensor Networks (WSN) which need to be handled. These issues are interrelated because of limited energy there are some restrictions on implementation of security. Insider packet drop attack is one of the dangerous attacks for wireless sensor network that causes a heavy damage to WSN functionalities by dropping packets. It becomes necessary to identify such attack for secure routing of data in WSN. To detect this attack, trust mechanism has been proven as a successful technique. In this mechanism, each node verifies the trustworthiness of its neighbor node before packet transmission so that packets can only be transmitted to trustworthy nodes. But there is a problem of False Alarm with such trust-aware scheme. False alarm occurs when a good node’s trust value goes down due to natural packet dropping and being eliminated from the routing paths. This wastes network’s resources that further shortens network lifetime. In this paper, we have proposed a system for identification and recovery of false alarms (IRFA) which is the optimization of existing trust based system. But security solution needs to be energy efficient due to scarcity of energy resources in WSN. To provide energy efficiency, we have implemented proposed IRFA system in cluster based environment which detects insider packet drop attackers in an energy efficient manner. We have conducted OMNET++ simulation and results demonstrate that the proposed system performance is better than existing trust-based system in terms of packet delivery rate and energy efficiency which improves network lifetime.

Detecting Provenance Forgery and Packet Drop Attacks for Fire Detection System in Wireless Sensor Network

Detecting Provenance Forgery and Packet Drop Attacks for Fire Detection System in Wireless Sensor Network

A stochastic approach for packet dropping attacks detection in mobile Ad hoc networks

A Mobile Ad hoc Network (MANET) is a dynamic network composed of mobile nodes that can communicate without relying on an existing infrastructure. In such decentralized environment, packet forwarding and other routing services are provided by network nodes cooperatively without any central administration. Most of existing Ad hoc routing protocols are based on the assumption that all network nodes are trustworthy. However, this assumption may be inconsistent when a malicious node decides to drop packets that are supposed to be forwarded in the aim of disrupting the routing services. Furthermore, the malicious node can change its behavior over time in order to appear as a legitimate node and still disrupting the network without being detected. To address this problem, we propose in this paper a fully decentralized mechanism that allows a node to monitor and detect neighbors that are malicious even if they have a changing behavior. Our mechanism is based on a Bernoulli Bayesian model for nodes’ behavior classification and a Markov chain model for behavior evolution tracking. Performance analysis of numerical results obtained using NS2 simulations show an accurate detection of malicious nodes, which can be used to guarantee a reliable and secure packet forwarding among network nodes.

An Intelligent Intrusion Detection and Prevention System for Safeguard Mobile Adhoc Networks against Malicious Nodes

Objectives: Mobile Adhoc Networks (MANETs) due to their dynamic topology are more liable to have security problems. These Adhoc Networks are easily susceptible to various types of attacker nodes. Out of the numerous attacks black hole, flooding and selective packet drop attacks are more hazardous attacks which reduce the performance of network under various parameters. Due to this problem, there is a need to develop a new approach for mitigating these attacker nodes simultaneously to improve the performance of MANETs Methods: An Intelligent Intrusion Detection and Prevention System (IIDPS) is proposed for preventing the ad hoc network from these three types of attacks under the AODV protocol. The proposed mechanism works on the basis of trust management. This research work consists of a central network administrator for detecting malicious nodes in the MANETs. IIDPS includes a trust manager which categorizes the trust of the network into different categories. Different types of malicious nodes are identified by the behavior classifier based on a predefined threshold and risk factor conditions. Findings: The proposed IIDPS is responsible for preventing MANETs from the black hole, flooding, and selective packet drop attacker nodes. At the same time, the proposed prevention system improves the performance of the network in the terms of numerous parameters like throughput, overhead, delay, packet delivery ratio etc. Novelty/ Improvement: There is no technique exist for MANETs under AODV protocol for detecting black hole, flooding and selective packet drop malicious nodes. The proposed IIDPS solves this issue to handle of these multiple attacks at the same time.

An extended AODV routing protocol for secure MANETs based on node trust values

In MANETs, various QoS problems exist such as high error rates, handling node mobility, inefficient routing, power conservation, and limited processing capabilities of network devices. To obtain secure ad hoc routing, misbehaving or malicious nodes should be detected and isolated. Otherwise, the overall network performance will be decreased. The participating nodes should establish a network with an acceptable level of trust relationships among themselves. This problem is solved with the adoption of a trust management framework and by extending the conventional routing protocol. This paper proposes a new routing protocol (TRUST_AODV) that extends the AODV routing protocol by incorporating a trust algorithm that detects misbehaving nodes. For each node, the proposed algorithm computes the weighted trust by taking into account the packet delivery ratio, energy consumption rate and buffer length. TRUST_AODV secures the MANET against potential packet drop attacks and denial-of-service attacks. It also improves the network performance under heavy network load conditions. The simulation results demonstrate that it supports secure routing and optimised performance.

HSecGR: Highly Secure Geographic Routing

An ad hoc wireless network is a set of nodes connected by wireless links in which nodes cooperate to forward packets from a source to a destination. Geographic routing (or position-based routing) has become an attractive solution for such networks since it reduces routing control overhead flooded in the network to construct routes (routes discovery). Many geographic routing protocols have been designed to guarantee packet delivery in such networks. However, these protocols consider that all nodes in the network are trustworthy which allows malicious nodes to violate network security and disrupt packet forwarding. In this paper, we propose and evaluate a new security approach that secures geographic routing protocols against a variety of attacks. Our approach is based on the use of MACs to allow intermediate nodes to verify the authenticity and the integrity of forwarded packets and uses authenticated acknowledgements to prevent packet dropping attacks. To meet node's resource constraints, we have based our solution on symmetric cryptography. Our solution is robust against modification and dropping attacks even in the presence of compromised nodes in the network.

Defense against packet collusion attacks in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics such as an open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nor destination nodes have control over the behaviour of intermediate nodes in the network. Consequently, the knowledge of where or when packets are/will be dropped is difficult to gather. In this paper, we present a novel attack and traceback mechanism against a special type of packet dropping attacks – packet collusion attacks, where the malicious node(s) drops some or all packets and then injects new fake packets in their place to mask the packet dropping. Our novel detection and traceback mechanism is based on the concept of a Merkle (or hash) tree and simulation results show it to be highly effective and accurate in terms of detecting attack instances and tracing back to the malicious node(s) in the network that is the attack source.

Fuzzy-based trusted routing to mitigate packet dropping attack between data aggregation points in smart grid communication network

Providing adequate security measure during data transmission is one of the essential requirements of smart grid communication network (SGCN). In this paper, a novel trust management model is proposed to alleviate packet dropping attack between data aggregation points of SGCN. The concept of probability theory is used to calculate trust of every node in the network before transmitting the packet. Trust evaluation is conducted in four stages viz., Direct, Indirect, Integrated and Overall trust and in each stage, suitable equations are suggested to measure the trust of each node. A novel technique based on fuzzy set theory is used to route the packet through the trusted channel. Hop count, reliability and capacity of the link are the parameters considered for constructing if-then rules and membership function. A qualitative reasoning is performed using inference mechanism and a defuzzication strategy is applied on the calculated trust value to make decision. Simulations are carried out to evaluate the performance of the proposed approach by varying the trust threshold and malicious nodes. From the experiments, it is observed that for all the metrics, the proposed trusted routing using fuzzy theory helps to improve the network performance with much reliable communication and reduced packet loss.

Uncertainty analysis framework for trust based routing in MANET

MANET (Mobile Ad hoc Network) has distinctive characteristics like node mobility, broadcast nature of wireless communication, and peer to peer data transfer. Due to these characteristics uncertainty is an integral part of MANET behavior. Uncertainty quantification is important for performance evaluation and better decision making in MANET. Packet dropping is a serious issue in MANET which degrades the performance of the network to a great extent. The packet dropping attack increases uncertainty in the network communication and eventually of the MANET. Trust based routing mechanisms are useful for neutralizing packet dropping attack. There are many existing trust based routing protocols which uses uncertainty for precise trust calculations. Thus, trust and uncertainty are closely related to each other. The paper proposes Uncertainty Analysis Framework (UAF) for MANET, it calculates the network Belief, Disbelief, and Uncertainty (BDU) values. The UAF framework integrated into different trust variants of AODV protocol which use direct trust, indirect trust, and global trust. The research work discovers impact of different trust models on MANET BDU using various mobility models. Experimental analysis shows that, trust based routing protocols show average 3 % gain in packet delivery ratio and at least 5 % increase in the network belief.

MANET: Securing AODV Based on a Combined Immune Theories Algorithm (CITA)

Mobile Ad hoc Networks consist of a set of mobile nodes communicating with each other in a decentralized and dynamic topology where nodes provide retransmission capabilities. Communications between source nodes and destinations go through routes represented by a set of intermediate nodes that are required to adapt and behave in response to some actions according to orders given by the chosen routing protocol. Absence of a centralized architecture, in addition to open wireless medium of Ad hoc networks, as well as nodes mobility are ones of the network characteristics that render the environment much vulnerable to different routing attacks. A wide range of current researches focus on enhancing MANET security using various techniques like cryptography, but these mechanisms creates too much overhead. Artificial Immune Systems provide intrusion detection techniques based on the abstraction of the human immune system. They are known to be very efficient and lightweight algorithms. Multiple immune theories are implemented like Negative selection, Clonal selection, Danger theory, Immune network...etc. This paper proposes the use of combined immune theories as an Intrusion Detection System that integrates to the AODV routing protocol and that can sense the presence of non-trusted nodes, as it can eliminate them from the network. The proposed approach is tested and validated in presence of Packet Dropping Attack. Promising results in terms of network performance then are discussed.

Mitigating Blackhole and Grayhole Attack in MANET using Enhanced AODV with TLTB Mechanism

MANET is multi-hop network which is a decentralized and infrastructure-less network that includes collection of mobile nodes that are self configurable and co-operates with each other for transmission of data. It has dynamic nature in terms of topology. Due to this dynamic nature of topology and no fixed infrastructure in MANET, these nodes have to be dependent on each other for transmission of data and thus are prone to packet drop attacks like Blackhole, grayhole attack. These attacks hinder the smooth transmission of data between nodes and hampers effective communication. In this paper, a new mechanism Traffic Light Trust Based (TLTB) is proposed to detect both Blackhole attacks and Grayhole attacks. This mechanism works after modification in the standard AODV routing protocol and Watchdog mechanism. This mechanism uses color scheme to define trust level of a node or any path. Just like traffic light it includes three colors to depict the level of trust. AODV routing protocol packets are modified to include new fields for application of proposed trust mechanism. These Solutions are compared with Lightweight Trust Based (LTB) and EDRI mechanism for Normalized Control load, Packet Delivery Ratio, accuracy in Blackhole and Grayhole detection and reliability of paths.

A Novel Intrusion Detection System for Detecting Black-Hole Nodes in MANETS

Mobile ad-hoc networks (MANETs) are autonomous,infrastructure less, self-organized networks. In MANETs, nodes are not stationary and thus move arbitrarily, resulting in rapid and unpredictable topology changes in the network. Due to the limited transmission range of the nodes in the MANETs, these nodes are not capable of directly communicating with each other. Hence, routing paths in MANETs potentially contain multiple hops, and every node in it has the responsibility to act as a router.So, the presence of any intermediate node in the route, which is either highly congested or behaving as a malicious node, is likely to drop the packets. In computer networking, this type of attack is known as a packet drop attack or black hole attack which is a type of denial-of-service attack in which a router that is supposed to relay packets instead discards them. The proposed algorithm will detect black hole nodes in the network by implementing changes into the existing AODV routing algorithm. The implementation of the algorithm was being achieved using theNS-2 simulator.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

A Novel Intrusion Detection System Based on Soft Computing Techniques Using Neuro- Fuzzy Classifier for Packet Dropping Attack in MANETs.

Due to the advancement in communication technologies, mobile ad hoc networks are very attractive in terms of communication because mobile nodes can communicate without the relay on predefined infrastructure. Therefore, some complex properties of mobile ad hoc networks make it more vulnerable to internal and external attacks. From the security perspective, prevention based methods such as encryption and authentication are not considerably good solution for mobile ad hoc networks to eliminate the attacks so that intrusion detection systems are applied as a keystone in these types of networks. The main objective of intrusion detection system is to categories the normal and suspicious activities in the network. This paper proposed a novel intrusion detection system based on soft computing techniques for mobile ad hoc networks. The proposed system is based on neuro-fuzzy classifier in binary form to detect, one of vey possible attack, i.e. packet dropping attack in mobile ad hoc networks. Qualnet Simulator 6.1 and MatLab toolbox are used to visualize the proposed scenarios and evaluate the performance of proposed approach in mobile ad hoc networks. Simulation results show that the proposed soft computing based approach efficiently detect the packet dropping attack with high true positive rate and low false positive rate.

Avoidance and Mitigation of All Packet Drop Attacks in MANET using Enhanced AODV with Cryptography

MANET is a decentralized network that has no infrastructure and nodes can communicate with each other by multi-hoping the messages. Due to this nature of MANET, it is prone to many security attacks in which packet drop attacks are very common. Various packet drop attacks like Blackhole attacks, Grayhole attack, Co- operative Blackhole attack, etc are the attacks that become a bottleneck in efficient routing and security in MANET. Various mechanisms are devised in past but none of them prove to be effective against all types of packet drop attacks. In this paper, a mechanism is formulated that enhances the AODV routing protocol with Trueness Level and Cryptography for effectively counter all types of packet drop attacks by mitigating them through avoidance and elimination of source of attack after detection. This mechanism is powered by efficient use of Cryptography in its natural form. This work is compared with published work Extended Data Routing Information mechanism against various parameters like Packet Delivery Ratio, Normalized Control Load, Accuracy and Reliability in the same environment and parameters in MATLAB 2013a.

The IDS for Securing MANET from Packet Dropping Attack

The IDS for Securing MANET from Packet Dropping Attack

PROVENANCE FORGERY AND PACKET DROP ATTACKS DETECTION IN WIRELESS NETWORKS ,

large scale sensor networks are deployed in numerous application domains, and the data they collect are used in decision-making for critical infrastructures. Data are streamed from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. In this paper, we propose a novel lightweight scheme to securely transmit provenance for sensor data. The proposed technique relies on in packet Bloom filters to encode provenance. We introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. We evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the lightweight secure provenance scheme in detecting packet forgery and loss attacks.

Mitigation of Collaborative Blackhole Attack using TRACEROUTE mechanism with Enhancement in AODV Routing Protocol

MANET is multi-hop network in which collection of mobile nodes is self configurable and co-operates together for communicating data without the need of any centralized component for management. Due to this dynamic nature of topology and infrastructure less frame in MANET, these nodes have to rely on each other for data transmission in multi-hop fashion and thus are prone to packet drop attacks like Blackhole attack, CoOperative Blackhole attack, etc and various types of data security attacks. In this paper, Solutions are proposed to detect Collaborative co-operative Blackhole attack in MANET. We introduced the mechanism of TRACEROUTE that helps in detecting the source of collaborative Blackhole attack and thus break the collaboration by eliminating and marking source of Collaboration between those malicious nodes. AODV routing protocol is enhanced by introducing new field that helps in finding the optimal, secure and reliable routes. These Solutions are compared with W-AODV for Packet Delivery Ratio, Control load, accuracy in Blackhole detection and accuracy in Blackhole detection.

Defense against Sybil attacks and authentication for anonymous location-based routing in MANET

In MANET, providing authentication and security to location-based routing is a big task. To overcome this problem, in this paper, we proposed a defense against Sybil attacks and authentication for anonymous location-based routing in MANET. Each random forwarder has a table of RSS values estimated from the previous message exchanges across a zone to detect the Sybil attack. The difference in RSS values of two neighboring nodes is estimated based on which the node's arrival angle into the zone is detected. Depending on the arrival angle, the nodes can be categorized as safety zone and caution zone. The messages exchanged between the RFs and senders can be protected by means of group signature. Finally, misrouting packet drop attack is detected and eliminated by using ant colony optimization technique. By simulation results, we show the proposed technique reduces the packet drop due to attacks, thereby increasing the delivery ratio.