A Perspective to Adopt Continuous Dynamic Cognition for Malicious Node Detection in Heterogeneous Networks

The current day networks use Proactive networks for adaption to the dynamic scenarios. The use of cognition technique based on the Observe, Orient, Decide and Act loop (OODA) is proposed to construct proactive networks. The network performance degradation in knowledge acquisition and malicious node presence is a problem that exists. The use of continuous time dynamic neural network is considered to achieve cognition. The variance in service rates of user nodes is used to detect malicious activity in heterogeneous networks. The improved malicious node detection rates are proved through the experimental results presented in this paper. (C) 2015 The Authors. Published by Elsevier B.V.

Malicious node detection in OppNets using hash chain technique

Opportunistic Networks aim to set a reliable networks where the nodes has no end-To-end connection and the communication links often suffer from frequent disruption and long delays. The design of the OppNets routing protocols is facing a serious challenges such as the protection of the data confidentiality and integrity. OppNets exploit the characteristics of the human social, such as similarities, daily routines, mobility patterns and interests to perform the message routing and data sharing. Packet dropping attack is one of the hardest attacks in Opportunistic Networks as both the source nodes and the destination nodes have no knowledge of where or when the packet will be dropped. In this paper, we present a new malicious nodes detection technique against packet faking attack where the malicious node drops one or more packets and instead of them injects new fake packets. We have called this novel attack in our previous works a packet faking attack. Each node in Opportunistic Networks can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our hash chain based defense techniques we have two phases. The first phases is to detect the attack, and the second phases is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. In our simulation, we have achieved a very high node detection accuracy and low false negative rate.

Malicious node traceback in opportunistic networks using merkle trees

Security is a major challenge in Opportunistic Networks because of its characteristics, such as 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 nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a malicious nodes detection mechanism against a special type of packet dropping attack where the malicious node drops one or more packets and then injects new fake packets instead. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, Merkle tree hashing technique. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Malicious code detection architecture inspired by human immune system

Malicious code is a threat to computer systems globally. In this paper, we outline the evolution of malicious code attacks. The threat is evolving, leaving challenges for attackers to improve attack techniques and for researchers and security specialists to improve detection accuracy. We present a novel architecture for an effective defense against malicious code attack, inspired by the human immune system. We introduce two phases of program execution: Adolescent and Mature Phase. The first phase uses a malware profile matching mechanism, whereas the second phase uses a program profile matching mechanism. Both mechanisms are analogous to the innate immune system

Packet faking attack: a novel attack and detection mechanism in OppNets

 Security is a major challenge in Opportunistic Networks (OppNets) due to its characteristics of being an open medium with dynamic topology, there is neither a centralized management nor clear lines of defence. A packet dropping attack is one of the major security threats in OppNets as neither source nodes nor destination nodes have any knowledge of when or where a packet will be dropped. In this paper, we present a novel attack and detection mechanism against a special type of packet dropping where the malicious node drops one packet or more and injects a new fake packet instead. Our novel detection mechanism is very powerful and has very high accuracy. It relies on a very simple yet powerful idea; the creation time of each packet. Significant results show this robust mechanism achieves a very high accuracy and detection rate.

An efficient detection mechanism against packet faking attack in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics, such as 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 nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In our previous novel attack (Packet Faking Attack [1]) we presented a special type of packet dropping where the malicious node drops one or more packets and then injects new fake packets instead. In this paper, we present an efficient detection mechanism against this type of attack where each node can detect the attack instead of the destination node. Our detection mechanism is very powerful and has very high accuracy. It relies on a very simple yet powerful idea, that is, the packet creation time of each packet. Simulation results show this robust mechanism achieves a very high accuracy, detection rate and good network traffic reduction.

Sentinel node detection in cervical cancer with Tc-99m-phytate

Objectives. The aim of this study was to investigate the feasibility of sentinel lymph node (SLN) identification using radioisotopic lymphatic mapping with technetium-99 m-labeled phytate in patients undergoing radical hysterectomy with pelvic lymphadenectomy for treatment of early cervical cancer.Methods. Between July 2001 and February 2003, 56 patients with cervical cancer 1160 stage I (it 53) or stage 11 (it 3) underwent sentinel lymph node detection with preoperative lymphoscintigraphy (Te-99m-labeled phytate injected into the uterine cervix, at 3, 6, 9, and 12 o'clock, at a dose of 55-74 MBq in a volume of 0.8 ml) and intratoperative lymphatic mapping with a handheld gamma probe, Radical hysterectomy was aborted in three cases because parametrial invasion was found intraoperatively and we performed only sentinel node resection. The remaining 53 patients underwent radical hysterectomy with complete pelvic lymphadenectomy, Sentinel nodes were detected using a handheld gamma-probe and removed for pathological assessment during the abdominal radical hysterectomy and pelvic lymphadenectomy.Results. One or more sentinel nodes were detected in 52 out of 56 eligible patients (92.8%). A total of 120 SLNs were detected by lymphoscintigraphy (mean 2.27 nodes per patient) and intraoperatively by gamma probe, Forty-four percent of SLNs were found in the external iliac area, 39% in the obturator region, 8.3% in interiliae region, and 6.7),) in the common iliac area. Unilateral sentinel nodes were found in thirty-one patients (59%). The remaining 21 patients (4100 had bilateral sentinel nodes, Microscopic nodal metastases were confirmed in 17 (32%) cases. In 10 of these patients, only SLNs had metastases. The 98 sentinel node.,, that were negative on hematoxylin and eosin were submitted to cytokeratin immunohistochemical analysis. Five (5.1%) micrometastases were identified with this technique. The sensitivity of the sentinel node was 82.3% (CI 95% - 56.6-96.2) and the negative predictive value was 92.1% (CI 95% 78.6 98.3) the accuracy of sentinel node in predicting the lymph node status was 94.2%,Conclusion. Preoperative lymphoscintigraphy and intraoperative lymphatic mapping with Tc-99-labeled phytate are effective in identifying sentinel nodes in patients undergoing radical hysterectomy and to select women in whom lymph node dissection call be avoided. (c) 2005 Elsevier B.V. All rights reserved.

Association between sentinel lymph node excision with or without preoperative SPECT/CT and metastatic node detection and disease-free survival in melanoma

Malignant melanoma has become an increasing interdisciplinary public health challenge worldwide. Sentinel lymph node excision (SLNE) is considered the most sensitive and specific staging test for the detection of micrometastatic melanoma in regional lymph nodes.

A Specific Mapping Study Using Fluorescence Sentinel Lymph Node Detection in Patients with Intermediate- and High-risk Prostate Cancer Undergoing Extended Pelvic Lymph Node Dissection.

Sentinel lymph node (SLN) detection techniques have the potential to change the standard of surgical care for patients with prostate cancer. We performed a lymphatic mapping study and determined the value of fluorescence SLN detection with indocyanine green (ICG) for the detection of lymph node metastases in intermediate- and high-risk patients undergoing radical prostatectomy and extended pelvic lymph node dissection. A total of 42 patients received systematic or specific ICG injections into the prostate base, the midportion, the apex, the left lobe, or the right lobe. We found (1) that external and internal iliac regions encompass the majority of SLNs, (2) that common iliac regions contain up to 22% of all SLNs, (3) that a prostatic lobe can drain into the contralateral group of pelvic lymph nodes, and (4) that the fossa of Marcille also receives significant drainage. Among the 12 patients who received systematic ICG injections, 5 (42%) had a total of 29 lymph node metastases. Of these, 16 nodes were ICG positive, yielding 55% sensitivity. The complex drainage pattern of the prostate and the low sensitivity of ICG for the detection of lymph node metastases reported in our study highlight the difficulties related to the implementation of SNL techniques in prostate cancer. PATIENT SUMMARY There is controversy about how extensive lymph node dissection (LND) should be during prostatectomy. We investigated the lymphatic drainage of the prostate and whether sentinel node fluorescence techniques would be useful to detect node metastases. We found that the drainage pattern is complex and that the sentinel node technique is not able to replace extended pelvic LND.

First-priority relation graph-based malicious users detection in mobile social networks

Mobile social networks (MSNs) consist of many mobile users (individuals) with social characteristics, that provide a variety of data delivery services involving the social relationship among mobile individuals. Because mobile users move around based on their common interests and contact with each other more frequently if they have more social features in common in MSNs. In this paper, we first propose the first-priority relation graph, say FPRG, of MSNs. However, some users in MSNs may be malicious. Malicious users can break the data delivery through terminating the data delivery or tampering with the data. Therefore, malicious users will be detected in the process of looking for the data delivery routing to obtain efficient and reliable data delivery routing along the first-priority relation graph. Secondly, we propose one hamiltonian cycle decomposition of FPRG-based adaptive detection algorithm based on in MSNs under the PMC detection model (the system-level detection model).

Catabolism attack and anabolism defense: a novel attack and traceback mechanism in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics, such as 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 nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a novel attack and traceback mechanism against a special type of packet dropping where the malicious node drops one or more packets and then injects new fake packets instead. We call this novel attack a Catabolism Attack and we call our novel traceback mechanism against this attack Anabolism Defense. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.

Establishing trust relationships in OppNets using Merkle trees

Opportunistic Networks (OppNets) are exposed to a variety of attacks, among them are packet dropping attacks. The security challenges in OppNets is to effectively and securely forward data and guarantee their delivery without any loss. Security and trust in OppNets have gained popularity in research because of their inherent features, including frequent partitions, long delays and intermittent connectivity. This paper presents an efficient malicious path and malicious node detection technique against selective packet dropping attacks. In our algorithm we have developed a solid detection mechanism using the Merkle tree hashing technique. The result of malicious path detection is used to build trust by destination nodes for each path, the built trust value of nodes is then used to detect malicious nodes. Simulation results show that the technique accurately detects malicious paths. The results also show that with the increase of simulation time, node detection accuracy also increases as intermediate nodes have more time to establish trust with destination nodes.

Cluster-based Intrusion Detection (CBID) architecture for mobile ad hoc networks

The ad hoc networks are vulnerable to attacks due to distributed nature and lack of infrastructure. Intrusion detection systems (IDS) provide audit and monitoring capabilities that offer the local security to a node and help to perceive the specific trust level of other nodes. The clustering protocols can be taken as an additional advantage in these processing constrained networks to collaboratively detect intrusions with less power usage and minimal overhead. Existing clustering protocols are not suitable for intrusion detection purposes, because they are linked with the routes. The route establishment and route renewal affects the clusters and as a consequence, the processing and traffic overhead increases due to instability of clusters. The ad hoc networks are battery and power constraint, and therefore a trusted monitoring node should be available to detect and respond against intrusions in time. This can be achieved only if the clusters are stable for a long period of time. If the clusters are regularly changed due to routes, the intrusion detection will not prove to be effective. Therefore, a generalized clustering algorithm has been proposed that can run on top of any routing protocol and can monitor the intrusions constantly irrespective of the routes. The proposed simplified clustering scheme has been used to detect intrusions, resulting in high detection rates and low processing and memory overhead irrespective of the routes, connections, traffic types and mobility of nodes in the network. Clustering is also useful to detect intrusions collaboratively since an individual node can neither detect the malicious node alone nor it can take action against that node on its own.

Sentinel node mapping in a pregnant woman with cervical cancer: a case report

The possibility of reducing morbidity associated with surgical dissection while maintaining accurate tumor staging is one of the greatest advantages of the sentinel node approach in surgical oncology. The sentinel node mapping has already proven to be useful in melanoma, breast cancer, and vulvar cancer. We report the first case of sentinel node detection by technetium-labeled radiocolloid in a pregnant woman with cervical cancer. The histologic analysis of the operative specimen showed a poorly differentiated squamous carcinoma with metastasis in the sentinel node and a neoplasic embolus in a blood vessel of the placental bed. The lymphatic mapping and sentinel lymph node detection are feasible during pregnancy.