Network security is a complex and challenging problem.

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This review addresses critical gaps and often-ignored aspects of immune response to a decellularized ECM.

Wireless sensor nodes lack hardware support for tamperresistance and are often deployed in unattended environments, thus leaving them vulnerable to capture and compromise by an adversary. In a node replication attack, an adversary uses the credentials of a compromised node to surreptitiously introduce replicas of that node into the network. These replicas are then used to launch a variety of attacks that subvert the goal of the sensor application, and the operation of the underlying protocols. We present a novel distributed approach called Localized Multicast for detecting node replication attacks. We evaluate the performance and security of our approach both theoretically and via simulation. Our results show that Localized Multicast is more efficient than previous distributed approaches in terms of communication and memory costs. Further, in our approach, the probability of detecting node replicas is much higher than that achieved in previous distributed protocols.

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Alpha linolenic acid is an essential polyunsaturated fatty acid and is reported to have the anti-cancer potential with no defined hypothesis or mechanism/s. Henceforth present study was in-quested to validate the effect of alpha linolenic acid on mitochondrial apoptosis, hypoxic microenvironment and de novo fatty acid synthesis using in-vitro and in-vivo studies. The IC50 value of alpha linolenic acid was recorded to be 17.55μM against ER+MCF-7 cells. Treatment with alpha linolenic acid was evident for the presence of early and late apoptotic signals along with mitochondrial depolarization, when studied through acridine orange/ethidium bromide and JC-1 staining. Alpha linolenic acid arrested the cell cycle in G2/M phase. Subsequently, the in-vivo efficacy was examined against 7, 12-dimethylbenz anthracene induced carcinogenesis. Treatment with alpha linolenic acid demarcated significant effect upon the cellular proliferation as evidenced through decreased in alveolar bud count, restoration of the histopathological architecture and loss of tumor micro vessels. Alpha linolenic acid restored the metabolic changes to normal when scrutinized through 1H NMR studies. The immunoblotting and qRT-PCR studies revealed participation of mitochondrial mediated death apoptosis pathway and curtailment of hypoxic microenvironment after treatment with alpha linolenic acid. With all above, it was concluded that alpha linolenic acid mediates mitochondrial apoptosis, curtails hypoxic microenvironment along with inhibition of de novo fatty acid synthesis to impart anticancer effects.

Although Machine Learning (ML) based approaches have shown promise for Android malware detection, a set of critical challenges remain unaddressed. Some of those challenges arise in relation to proper evaluation of the detection approach while others are related to the design decisions of the same. In this paper, we systematically study the impact of these challenges as a set of research questions (i.e., hypotheses). We design an experimentation framework where we can reliably vary several parameters while evaluating ML-based Android malware detection approaches. The results from the experiments are then used to answer the research questions. Meanwhile, we also demonstrate the impact of some challenges on some existing ML-based approaches. The large (market-scale) dataset (benign and malicious apps) we use in the above experiments represents the real-world Android app security analysis scale. We envision this study to encourage the practice of employing a better evaluation strategy and better designs of future ML-based approaches for Android malware detection.

In-network data aggregation in wireless sensor networks (WSNs) is a technique aimed at reducing the communication overhead-sensed data are combined into partial results at intermediate nodes during message routing. However, in the above technique, some sensor nodes need to send their individual sensed values to an aggregator node, empowered with the capability to decrypt the received data to perform a partial aggregation. This scenario raises privacy concerns in applications like personal health care and the military surveillance. A few other solutions exist where the data are not disclosed to the aggregator (e.g., using privacy homomorphism (PH)), but these solutions are not robust to node or communication failure. The contributions of this paper are two-fold: first, we design a private data aggregation protocol that does not leak individual sensed values during the data aggregation process. In particular, neither the base station (BS) nor the other nodes are able to compromise the privacy of an individual node's sensed value. Second, the proposed protocol is robust to data-loss; if there is a node-failure or communication failure, the protocol is still able to compute the aggregate and to report to the base station the number of nodes that participated in the aggregation. To the best of our knowledge, our scheme is the first one that efficiently addresses the above issues all at once. Copyright © 2009 John Wiley & Sons, Ltd