Biblio
The 6L0WPAN adaptation layer is widely used in many Internet of Things (IoT) and vehicular networking applications. The current IoT framework [1], which introduced 6LoWPAN to the TCP/IP model, does not specif the implementation for managing its received-fragments buffer. This paper looks into the effect of current implementations of buffer management strategies at 6LoWPAN's response in case of fragmentation-based, buffer reservation Denial of Service (DoS) attacks. The Packet Drop Rate (PDR) is used to analyze how successful the attacker is for each management technique. Our investigation uses different defence strategies, which include our implementation of the Split Buffer mechanism [2] and a modified version of this mechanism that we devise in this paper as well. In particular, we introduce dynamic calculation for the average time between consecutive fragments and the use of a list of previously dropped packets tags. NS3 is used to simulate all the implementations. Our results show that using a ``slotted'' buffer would enhance 6LoWPAN's response against these attacks. The simulations also provide an in-depth look at using scoring systems to manage buffer cleanups.
As everyone knows vulnerability detection is a very difficult and time consuming work, so taking advantage of the unlabeled data sufficiently is needed and helpful. According the above reality, in this paper a method is proposed to predict buffer overflow based on semi-supervised learning. We first employ Antlr to extract AST from C/C++ source files, then according to the 22 buffer overflow attributes taxonomies, a 22-dimension vector is extracted from every function in AST, at last, the vector is leveraged to train a classifier to predict buffer overflow vulnerabilities. The experiment and evaluation indicate our method is correct and efficient.
This paper describes a high-performance and space-efficient memory-resident datastore for text analytics systems based on a hash table for fast access, a dynamic trie for staging and a list of Level-Order Unary Degree Sequence (LOUDS) tries for compactness. We achieve efficient memory allocation and data placement by placing freqently access keys in the hash table, and infrequently accessed keys in the LOUDS tries without using conventional cache algorithms. Our algorithm also dynamically changes memory allocation sizes for these data structures according to the remaining available memory size. This technique yields 38.6% to 52.9% better throughput than a double array trie - a conventional fast and compact datastore.
By identifying memory pages that external I/O operations have modified, a proposed scheme blocks malicious injected code activation, accurately distinguishing an attack from legitimate code injection with negligible performance impact and no changes to the user application.